Prepare to remove tensor_load and tensor_store special handling from hlo to lhlo legalization.
This updates the tests to no longer rely on tensor_store. Once all users of this behavior have adopted, the tensor_store support will be removed. PiperOrigin-RevId: 348624899
This commit is contained in:
Stephan Herhut
TensorFlow MLIR Team
parent
c4accdcc41
commit
ccdd07f8e4
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@ -220,6 +220,7 @@ struct HloToLhloDynamicReshapeConverter
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}
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};
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// TODO(b/175670649) Fix this to no longer access original tensor operands.
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class HloToLhloDynamicBroadcastInDimOpConverter
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: public BaseOpConversion<mhlo::DynamicBroadcastInDimOp> {
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public:
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@ -462,19 +463,8 @@ struct HloToLhloReturnOpConverter : public BaseOpConversion<mhlo::ReturnOp> {
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}
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};
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class HloToLhloTensorLoadOpConverter
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: public BaseOpConversion<mlir::TensorLoadOp> {
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public:
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using BaseOpConversion<mlir::TensorLoadOp>::BaseOpConversion;
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LogicalResult matchAndRewrite(
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mlir::TensorLoadOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter& rewriter) const final {
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rewriter.replaceOp(op, operands);
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return success();
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}
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};
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class HloToLhloTensorStoreOpConverter
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// TODO(b/175789537) Remove this pattern.
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class HloToLhloTensorStoreOpLegacyConverter
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: public BaseOpConversion<mlir::TensorStoreOp> {
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public:
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using BaseOpConversion<mlir::TensorStoreOp>::BaseOpConversion;
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@ -569,9 +559,13 @@ struct HloLegalizeToLhlo
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target.addLegalDialect<lmhlo::LmhloDialect>();
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target.addLegalDialect<StandardOpsDialect>();
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target.addLegalDialect<tensor::TensorDialect>();
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target.addIllegalOp<mlir::TensorLoadOp>();
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target.addIllegalOp<mlir::TensorStoreOp>();
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target.addIllegalDialect<mhlo::MhloDialect>();
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// Declare tensor_load and tensor_store illegal.
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target.addIllegalOp<mlir::TensorLoadOp, mlir::TensorStoreOp>();
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// tensor_to_memref is illegal if it has uses.
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// TODO(b/175670649) Make tensor_to_memref illegal.
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target.addDynamicallyLegalOp<mlir::TensorToMemrefOp>(
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[](auto op) { return op->use_empty(); });
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BufferizeTypeConverter converter;
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auto isMemRefType = [](Type type) { return type.isa<BaseMemRefType>(); };
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@ -596,9 +590,15 @@ struct HloLegalizeToLhlo
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populateCallOpTypeConversionPattern(patterns, &context, converter);
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populateBranchOpInterfaceAndReturnOpTypeConversionPattern(
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patterns, &context, converter);
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populateEliminateBufferizeMaterializationsPatterns(&context, converter,
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patterns);
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populateShapeStructuralTypeConversionsAndLegality(&context, converter,
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patterns, target);
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// TODO(b/175789537) Remove this pattern.
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patterns.insert<HloToLhloTensorStoreOpLegacyConverter>(&context);
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if (failed(applyPartialConversion(getOperation(), target,
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std::move(patterns))))
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signalPassFailure();
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@ -668,9 +668,7 @@ void populateHLOToLHLOConversionPattern(MLIRContext* context,
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HloToLhloOpConverter<mhlo::TransposeOp>,
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HloToLhloOpConverter<mhlo::XorOp>,
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HloToLhloReduceOpConverter,
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HloToLhloReturnOpConverter,
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HloToLhloTensorLoadOpConverter,
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HloToLhloTensorStoreOpConverter
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HloToLhloReturnOpConverter
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>(*converter, context);
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// clang-format on
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}
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@ -45,8 +45,7 @@ func @trivial_broadcast_wrapper() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<3xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -63,8 +62,7 @@ func @trivial_broadcast_wrapper() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<3xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -97,8 +95,7 @@ func @broadcast_in_X_dim_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<1x4xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -114,8 +111,7 @@ func @broadcast_in_X_dim_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<1x4xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -145,8 +141,7 @@ func @broadcast_in_Y_dim_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<3x1xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -163,8 +158,7 @@ func @broadcast_in_Y_dim_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<3x1xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -197,8 +191,7 @@ func @broadcast_in_X_dim_transpose_wrapper() {
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broadcast_dimensions = dense<[1, 0]> : tensor<2xi64>
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} : (tensor<4x1xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -214,8 +207,7 @@ func @broadcast_in_X_dim_transpose_wrapper() {
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broadcast_dimensions = dense<[1, 0]> : tensor<2xi64>
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} : (tensor<4x1xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -245,8 +237,7 @@ func @broadcast_in_Y_dim_transpose_wrapper() {
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broadcast_dimensions = dense<[1, 0]> : tensor<2xi64>
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} : (tensor<1x3xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -263,8 +254,7 @@ func @broadcast_in_Y_dim_transpose_wrapper() {
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broadcast_dimensions = dense<[1, 0]> : tensor<2xi64>
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} : (tensor<1x3xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -288,8 +278,7 @@ func @broadcast_scalar_1d_wrapper() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<1xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -306,8 +295,7 @@ func @broadcast_scalar_1d_wrapper() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<1xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -331,8 +319,7 @@ func @broadcast_scalar_2d_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<1x1xf32>) -> tensor<3x4xf32>
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%output_buf = alloc() : memref<3x4xf32>
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tensor_store %output, %output_buf : memref<3x4xf32>
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%output_buf = tensor_to_memref %output : memref<3x4xf32>
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%unranked_output = memref_cast %output_buf : memref<3x4xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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@ -349,8 +336,7 @@ func @broadcast_scalar_2d_wrapper() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<1x1xf32>, tensor<2xindex>) -> tensor<3x4xf32>
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%dyn_output_buf = alloc() : memref<3x4xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x4xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x4xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x4xf32> to memref<*xf32>
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@ -386,8 +372,7 @@ func @broadcast_to_the_same_shape() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<2x3xf32>) -> tensor<2x3xf32>
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%output_buf = alloc() : memref<2x3xf32>
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tensor_store %output, %output_buf : memref<2x3xf32>
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%output_buf = tensor_to_memref %output : memref<2x3xf32>
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%unraked_output = memref_cast %output_buf : memref<2x3xf32> to memref<*xf32>
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call @print_memref_f32(%unraked_output) : (memref<*xf32>) -> ()
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@ -401,8 +386,7 @@ func @broadcast_to_the_same_shape() {
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broadcast_dimensions = dense<[0, 1]> : tensor<2xi64>
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} : (tensor<2x3xf32>, tensor<2xindex>) -> tensor<2x3xf32>
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%dyn_output_buf = alloc() : memref<2x3xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<2x3xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<2x3xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<2x3xf32> to memref<*xf32>
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@ -433,8 +417,7 @@ func @broadcast_1d_to_2d() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<3xf32>) -> tensor<3x3xf32>
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%output_buf = alloc() : memref<3x3xf32>
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tensor_store %output, %output_buf : memref<3x3xf32>
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%output_buf = tensor_to_memref %output : memref<3x3xf32>
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%unraked_output = memref_cast %output_buf : memref<3x3xf32> to memref<*xf32>
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call @print_memref_f32(%unraked_output) : (memref<*xf32>) -> ()
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@ -451,8 +434,7 @@ func @broadcast_1d_to_2d() {
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broadcast_dimensions = dense<0> : tensor<1xi64>
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} : (tensor<3xf32>, tensor<2xindex>) -> tensor<3x3xf32>
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%dyn_output_buf = alloc() : memref<3x3xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x3xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x3xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x3xf32> to memref<*xf32>
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@ -484,8 +466,7 @@ func @broadcast_1d_to_2d_with_transpose() {
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broadcast_dimensions = dense<1> : tensor<1xi64>
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} : (tensor<3xf32>) -> tensor<3x3xf32>
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%output_buf = alloc() : memref<3x3xf32>
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tensor_store %output, %output_buf : memref<3x3xf32>
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%output_buf = tensor_to_memref %output : memref<3x3xf32>
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%unraked_output = memref_cast %output_buf : memref<3x3xf32> to memref<*xf32>
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call @print_memref_f32(%unraked_output) : (memref<*xf32>) -> ()
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@ -501,8 +482,7 @@ func @broadcast_1d_to_2d_with_transpose() {
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broadcast_dimensions = dense<1> : tensor<1xi64>
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} : (tensor<3xf32>, tensor<2xindex>) -> tensor<3x3xf32>
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%dyn_output_buf = alloc() : memref<3x3xf32>
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tensor_store %dyn_output, %dyn_output_buf : memref<3x3xf32>
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%dyn_output_buf = tensor_to_memref %dyn_output : memref<3x3xf32>
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%unranked_dyn_output = memref_cast %dyn_output_buf
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: memref<3x3xf32> to memref<*xf32>
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@ -45,8 +45,7 @@ func @reduce_add() {
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}) {dimensions = dense<1> : tensor<1xi64>}
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: (tensor<2x3xf32>, tensor<f32>) -> tensor<2xf32>
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%output = alloc() : memref<2xf32>
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tensor_store %reduce, %output : memref<2xf32>
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%output = tensor_to_memref %reduce : memref<2xf32>
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%unranked_output = memref_cast %output : memref<2xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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// CHECK: rank = 1 offset = 0 sizes = [2] strides = [1]
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@ -83,8 +82,7 @@ func @reduce_max() {
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}) {dimensions = dense<1> : tensor<1xi64>}
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: (tensor<2x3xf32>, tensor<f32>) -> tensor<2xf32>
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%output = alloc() : memref<2xf32>
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tensor_store %reduce, %output : memref<2xf32>
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%output = tensor_to_memref %reduce : memref<2xf32>
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%unranked_output = memref_cast %output : memref<2xf32> to memref<*xf32>
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call @print_memref_f32(%unranked_output) : (memref<*xf32>) -> ()
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// CHECK: rank = 1 offset = 0 sizes = [2] strides = [1]
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@ -3,14 +3,12 @@
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// RUN: | FILECHECK_OPTS="" FileCheck %s
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// CHECK-LABEL: func @attrs
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func @attrs_copy(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
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%tensor_operand = tensor_load %operand : memref<2x2xf32>
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%tensor_result = "mhlo.exponential"(%tensor_operand)
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func @attrs_copy(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
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%result = "mhlo.exponential"(%operand)
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{some_attr_1 = "exp.1", some_attr_2 = dense<1> : tensor<1xi64>}
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: (tensor<2x2xf32>) -> tensor<2x2xf32>
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// CHECK: "lmhlo.exponential"(%{{.*}}, %{{.*}}) {some_attr_1 = "exp.1", some_attr_2 = dense<1> : tensor<1xi64>}
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tensor_store %tensor_result, %result : memref<2x2xf32>
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return
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return %result : tensor<2x2xf32>
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}
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// -----
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@ -19,7 +17,6 @@ func @return_func(%arg0: tensor<4xf32>) -> tensor<4xf32> {
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return %arg0 : tensor<4xf32>
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}
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// CHECK: (%[[ARG0:.*]]: [[TYPE:.*]]) -> [[TYPE]]
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// CHECK-NOT: "lmhlo.copy"
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// CHECK-NEXT: return %[[ARG0]]
|
||||
|
||||
// -----
|
||||
|
|
@ -53,104 +50,81 @@ func @func_op_long(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32>
|
|||
// -----
|
||||
|
||||
// CHECK-LABEL: func @fusion
|
||||
func @fusion(%multiplier: memref<2x2xf32>, %summand_1: memref<2x2xf32>,
|
||||
%summand_2: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
// CHECK: (%{{.*}}: {{.*}}, {{.*}}: {{.*}}, {{.*}}: {{.*}}, %[[RESULT:.*]]: {{.*}})
|
||||
func @fusion(%multiplier: tensor<2x2xf32>, %summand_1: tensor<2x2xf32>,
|
||||
%summand_2: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
// CHECK: (%{{.*}}: {{.*}}, {{.*}}: {{.*}}, {{.*}}: {{.*}})
|
||||
// CHECK-NEXT: %[[ADD_RESULT:.*]] = alloc() : memref<2x2xf32>
|
||||
%tensor_summand_1 = tensor_load %summand_1 : memref<2x2xf32>
|
||||
%tensor_summand_2 = tensor_load %summand_2 : memref<2x2xf32>
|
||||
%sum = "mhlo.add"(%tensor_summand_1, %tensor_summand_2)
|
||||
%sum = "mhlo.add"(%summand_1, %summand_2)
|
||||
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK-NEXT: "lmhlo.add"(%{{.*}}, %{{.*}}, %[[ADD_RESULT]])
|
||||
// CHECK-NEXT: %[[MUL_RESULT:.*]] = alloc() : memref<2x2xf32>
|
||||
%tensor_multiplier = tensor_load %multiplier : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.multiply"(%sum, %tensor_multiplier)
|
||||
%result = "mhlo.multiply"(%sum, %multiplier)
|
||||
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK-NEXT: "lmhlo.multiply"(%[[ADD_RESULT]], %{{.*}}, %[[MUL_RESULT]])
|
||||
// CHECK-NEXT: dealloc %[[ADD_RESULT]] : memref<2x2xf32>
|
||||
// CHECK-NEXT: "lmhlo.copy"(%[[MUL_RESULT]], %[[RESULT]])
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
// CHECK-NEXT: dealloc %[[MUL_RESULT]] : memref<2x2xf32>
|
||||
// CHECK-NEXT: return
|
||||
return
|
||||
// CHECK-NEXT: return %[[MUL_RESULT]] : memref<2x2xf32>
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @copy
|
||||
func @copy(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.copy"(%tensor_operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.copy"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
func @copy(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.copy"(%operand) : (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// TODO(herhut): An explicit copy should not be removed.
|
||||
// TODO-CHECK: "lmhlo.copy"(%{{.*}}, %{{.*}})
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @exp
|
||||
func @exp(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.exponential"(%tensor_operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
func @exp(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.exponential"(%operand) : (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.exponential"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @log
|
||||
func @log(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.log"(%tensor_operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
func @log(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.log"(%operand) : (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.log"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @select
|
||||
func @select(%pred: memref<2x2xi1>, %lhs: memref<2x2xf32>,
|
||||
%rhs: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_pred = tensor_load %pred : memref<2x2xi1>
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xf32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.select"(%tensor_pred, %tensor_lhs, %tensor_rhs)
|
||||
func @select(%pred: tensor<2x2xi1>, %lhs: tensor<2x2xf32>,
|
||||
%rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.select"(%pred, %lhs, %rhs)
|
||||
: (tensor<2x2xi1>, tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.select"(%{{.*}}, %{{.*}}, %{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @compare
|
||||
func @compare(%lhs: memref<2x2xf32>, %rhs: memref<2x2xf32>, %result: memref<2x2xi1>) {
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xf32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.compare"(%tensor_lhs, %tensor_rhs)
|
||||
func @compare(%lhs: tensor<2x2xf32>, %rhs: tensor<2x2xf32>) -> tensor<2x2xi1> {
|
||||
%result = "mhlo.compare"(%lhs, %rhs)
|
||||
{comparison_direction = "EQ"}
|
||||
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xi1>
|
||||
// CHECK: "lmhlo.compare"(%{{.*}}, %{{.*}}, %{{.*}}) {comparison_direction = "EQ"}
|
||||
tensor_store %tensor_result, %result : memref<2x2xi1>
|
||||
return
|
||||
return %result : tensor<2x2xi1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @broadcast
|
||||
func @broadcast(%operand: memref<5xf32>, %result: memref<10x5xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<5xf32>
|
||||
%tensor_result = "mhlo.broadcast_in_dim"(%tensor_operand)
|
||||
func @broadcast(%operand: tensor<5xf32>) -> tensor<10x5xf32> {
|
||||
%result = "mhlo.broadcast_in_dim"(%operand)
|
||||
{broadcast_dimensions = dense<1> : tensor<1xi64>}
|
||||
: (tensor<5xf32>) -> tensor<10x5xf32>
|
||||
// CHECK: "lmhlo.broadcast_in_dim"(%{{.*}}, %{{.*}}) {broadcast_dimensions = dense<1> : tensor<1xi64>}
|
||||
tensor_store %tensor_result, %result : memref<10x5xf32>
|
||||
return
|
||||
return %result : tensor<10x5xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
|
@ -158,16 +132,14 @@ func @broadcast(%operand: memref<5xf32>, %result: memref<10x5xf32>) {
|
|||
// CHECK: #[[MAP:.*]] = affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0 * s0 + d1 * s1 + d2 * s2)>
|
||||
|
||||
// CHECK-LABEL: func @dyn_broadcast
|
||||
func @dyn_broadcast(%operand: memref<?x?xf32>) -> index {
|
||||
func @dyn_broadcast(%operand: tensor<?x?xf32>) -> tensor<?x?x?xf32> {
|
||||
// CHECK-SAME: %[[OPERAND:.*]]: memref<?x?xf32>
|
||||
%tensor_operand = tensor_load %operand : memref<?x?xf32>
|
||||
%c1 = constant 1 : i64
|
||||
%shape = tensor_from_elements %c1, %c1, %c1 : tensor<3xi64>
|
||||
%tensor_result = "mhlo.dynamic_broadcast_in_dim"(%tensor_operand, %shape) {
|
||||
%result = "mhlo.dynamic_broadcast_in_dim"(%operand, %shape) {
|
||||
broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>
|
||||
} : (tensor<?x?xf32>, tensor<3xi64>) -> tensor<?x?x?xf32>
|
||||
%rank = rank %tensor_result : tensor<?x?x?xf32>
|
||||
return %rank : index
|
||||
return %result : tensor<?x?x?xf32>
|
||||
}
|
||||
// CHECK: %[[SHAPE:.*]] = tensor_from_elements
|
||||
|
||||
|
|
@ -196,82 +168,67 @@ func @dyn_broadcast(%operand: memref<?x?xf32>) -> index {
|
|||
// CHECK: %[[RESULT:.*]] = alloc(%[[SIZE_0]], %[[SIZE_1]], %[[SIZE_2]]) : memref<?x?x?xf32>
|
||||
|
||||
// CHECK: "lmhlo.copy"(%[[TRANSFORMED_MEMREF]], %[[RESULT]]) : (memref<?x?x?xf32, #map>, memref<?x?x?xf32>) -> ()
|
||||
// CHECK: dealloc %[[RESULT]] : memref<?x?x?xf32>
|
||||
// CHECK: return %[[RESULT]] : memref<?x?x?xf32>
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @complex
|
||||
func @complex(%real: memref<2x2xf32>,
|
||||
%imag: memref<2x2xf32>,
|
||||
%result: memref<2x2xcomplex<f32>>) {
|
||||
%tensor_real = tensor_load %real : memref<2x2xf32>
|
||||
%tensor_imag = tensor_load %imag : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.complex"(%tensor_real, %tensor_imag)
|
||||
func @complex(%real: tensor<2x2xf32>, %imag: tensor<2x2xf32>)
|
||||
-> tensor<2x2xcomplex<f32>> {
|
||||
%result = "mhlo.complex"(%real, %imag)
|
||||
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xcomplex<f32>>
|
||||
// CHECK: "lmhlo.complex"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xcomplex<f32>>
|
||||
return
|
||||
return %result : tensor<2x2xcomplex<f32>>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @complex_dyn
|
||||
func @complex_dyn(%real: memref<?xf32>,
|
||||
%imag: memref<?xf32>,
|
||||
%result: memref<?xcomplex<f32>>) {
|
||||
%tensor_real = tensor_load %real : memref<?xf32>
|
||||
%tensor_imag = tensor_load %imag : memref<?xf32>
|
||||
%tensor_result = "mhlo.complex"(%tensor_real, %tensor_imag)
|
||||
func @complex_dyn(%real: tensor<?xf32>, %imag: tensor<?xf32>)
|
||||
-> tensor<?xcomplex<f32>> {
|
||||
%result = "mhlo.complex"(%real, %imag)
|
||||
: (tensor<?xf32>, tensor<?xf32>) -> tensor<?xcomplex<f32>>
|
||||
// CHECK: "lmhlo.complex"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<?xcomplex<f32>>
|
||||
return
|
||||
return %result : tensor<?xcomplex<f32>>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @real
|
||||
func @real(%operand: memref<2x2xcomplex<f32>>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xcomplex<f32>>
|
||||
%tensor_result = "mhlo.real"(%tensor_operand)
|
||||
func @real(%operand: tensor<2x2xcomplex<f32>>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.real"(%operand)
|
||||
: (tensor<2x2xcomplex<f32>>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.real"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @real_dyn
|
||||
func @real_dyn(%operand: memref<?xcomplex<f32>>, %result: memref<?xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<?xcomplex<f32>>
|
||||
%tensor_result = "mhlo.real"(%tensor_operand)
|
||||
func @real_dyn(%operand: tensor<?xcomplex<f32>>) -> tensor<?xf32> {
|
||||
%result = "mhlo.real"(%operand)
|
||||
: (tensor<?xcomplex<f32>>) -> tensor<?xf32>
|
||||
// CHECK: "lmhlo.real"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<?xf32>
|
||||
return
|
||||
return %result : tensor<?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @imag
|
||||
func @imag(%operand: memref<2x2xcomplex<f32>>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xcomplex<f32>>
|
||||
%tensor_result = "mhlo.imag"(%tensor_operand)
|
||||
func @imag(%operand: tensor<2x2xcomplex<f32>>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.imag"(%operand)
|
||||
: (tensor<2x2xcomplex<f32>>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.imag"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @gather
|
||||
func @gather(%operand: memref<13x7xf32>, %idxs: memref<5xi32>, %result: memref<5x7xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<13x7xf32>
|
||||
%tensor_idxs = tensor_load %idxs : memref<5xi32>
|
||||
%tensor_result =
|
||||
"mhlo.gather"(%tensor_operand, %tensor_idxs)
|
||||
func @gather(%operand: tensor<13x7xf32>, %idxs: tensor<5xi32>)
|
||||
-> tensor<5x7xf32> {
|
||||
%result =
|
||||
"mhlo.gather"(%operand, %idxs)
|
||||
{ dimension_numbers =
|
||||
{ collapsed_slice_dims = dense<0> : tensor<1xi64>
|
||||
, index_vector_dim = 1 : i64
|
||||
|
|
@ -282,269 +239,222 @@ func @gather(%operand: memref<13x7xf32>, %idxs: memref<5xi32>, %result: memref<5
|
|||
, slice_sizes = dense<[1, 7]> : tensor<2xi64> }
|
||||
: (tensor<13x7xf32>, tensor<5xi32>) -> tensor<5x7xf32>
|
||||
// CHECK: "lmhlo.gather"(%{{.*}}, %{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<5x7xf32>
|
||||
return
|
||||
return %result : tensor<5x7xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @imag_dyn
|
||||
func @imag_dyn(%operand: memref<?xcomplex<f32>>, %result: memref<?xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<?xcomplex<f32>>
|
||||
%tensor_result = "mhlo.imag"(%tensor_operand)
|
||||
func @imag_dyn(%operand: tensor<?xcomplex<f32>>) -> tensor<?xf32> {
|
||||
%result = "mhlo.imag"(%operand)
|
||||
: (tensor<?xcomplex<f32>>) -> tensor<?xf32>
|
||||
// CHECK: "lmhlo.imag"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<?xf32>
|
||||
return
|
||||
return %result : tensor<?xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @iota
|
||||
func @iota(%result: memref<10xi32>) {
|
||||
%tensor_result = "mhlo.iota"()
|
||||
// TODO(herhut): Dummy should not be required here.
|
||||
func @iota(%dummy: tensor<?xf32>) -> tensor<10xi32> {
|
||||
%result = "mhlo.iota"()
|
||||
{iota_dimension = 0 : i64} : () -> tensor<10xi32>
|
||||
// CHECK: "lmhlo.iota"(%{{.*}}) {iota_dimension = 0 : i64}
|
||||
tensor_store %tensor_result, %result : memref<10xi32>
|
||||
return
|
||||
return %result : tensor<10xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @abs
|
||||
func @abs(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.abs"(%tensor_operand)
|
||||
func @abs(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.abs"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.abs"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @and
|
||||
func @and(%operand0: memref<2x2xi32>, %operand1: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_operand0 = tensor_load %operand0 : memref<2x2xi32>
|
||||
%tensor_operand1 = tensor_load %operand1 : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.and"(%tensor_operand0, %tensor_operand1)
|
||||
func @and(%operand0: tensor<2x2xi32>, %operand1: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.and"(%operand0, %operand1)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.and"(%{{.*}}, %{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @ceil
|
||||
func @ceil(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.ceil"(%tensor_operand)
|
||||
func @ceil(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.ceil"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.ceil"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @convert
|
||||
func @convert(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.convert"(%tensor_operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.copy"(%{{.*}}, %{{.*}})
|
||||
// CHECK-NOT: tensor_store
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
func @convert(%operand: tensor<2x2xf32>) -> tensor<2x2xi32> {
|
||||
%result = "mhlo.convert"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.convert"(%{{.*}}, %{{.*}})
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @cos
|
||||
func @cos(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.cosine"(%tensor_operand)
|
||||
func @cos(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.cosine"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.cosine"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @floor
|
||||
func @floor(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.floor"(%tensor_operand)
|
||||
func @floor(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.floor"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.floor"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @neg
|
||||
func @neg(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.negate"(%tensor_operand)
|
||||
func @neg(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.negate"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.negate"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @not
|
||||
func @not(%operand: memref<2x2xi32>, %result: memref<2x2xi32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.not"(%tensor_operand)
|
||||
func @not(%operand: tensor<2x2xi32>) -> tensor<2x2xi32> {
|
||||
%result = "mhlo.not"(%operand)
|
||||
: (tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.not"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @or
|
||||
func @or(%operand0: memref<2x2xi32>, %operand1: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_operand0 = tensor_load %operand0 : memref<2x2xi32>
|
||||
%tensor_operand1 = tensor_load %operand1 : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.or"(%tensor_operand0, %tensor_operand1)
|
||||
func @or(%operand0: tensor<2x2xi32>, %operand1: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.or"(%operand0, %operand1)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.or"(%{{.*}}, %{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @rsqrt
|
||||
func @rsqrt(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.rsqrt"(%tensor_operand)
|
||||
func @rsqrt(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.rsqrt"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.rsqrt"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @sign
|
||||
func @sign(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.sign"(%tensor_operand)
|
||||
func @sign(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.sign"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.sign"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @sqrt
|
||||
func @sqrt(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.sqrt"(%tensor_operand)
|
||||
func @sqrt(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.sqrt"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.sqrt"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @shift_left
|
||||
func @shift_left(%lhs: memref<2x2xi32>, %rhs: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xi32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.shift_left"(%tensor_lhs, %tensor_rhs)
|
||||
func @shift_left(%lhs: tensor<2x2xi32>, %rhs: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.shift_left"(%lhs, %rhs)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.shift_left"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @shift_right_arithmetic
|
||||
func @shift_right_arithmetic(%lhs: memref<2x2xi32>, %rhs: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xi32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.shift_right_arithmetic"(%tensor_lhs, %tensor_rhs)
|
||||
func @shift_right_arithmetic(%lhs: tensor<2x2xi32>, %rhs: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.shift_right_arithmetic"(%lhs, %rhs)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.shift_right_arithmetic"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @shift_right_logical
|
||||
func @shift_right_logical(%lhs: memref<2x2xi32>, %rhs: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xi32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.shift_right_logical"(%tensor_lhs, %tensor_rhs)
|
||||
func @shift_right_logical(%lhs: tensor<2x2xi32>, %rhs: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.shift_right_logical"(%lhs, %rhs)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.shift_right_logical"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @tanh
|
||||
func @tanh(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.tanh"(%tensor_operand)
|
||||
func @tanh(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.tanh"(%operand)
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.tanh"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @remainder
|
||||
func @remainder(%lhs: memref<2x2xf32>, %rhs: memref<2x2xf32>,
|
||||
%result: memref<2x2xf32>) {
|
||||
%tensor_lhs = tensor_load %lhs : memref<2x2xf32>
|
||||
%tensor_rhs = tensor_load %rhs : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.remainder"(%tensor_lhs, %tensor_rhs)
|
||||
func @remainder(%lhs: tensor<2x2xf32>, %rhs: tensor<2x2xf32>)
|
||||
-> tensor<2x2xf32> {
|
||||
%result = "mhlo.remainder"(%lhs, %rhs)
|
||||
: (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.remainder"(%{{.*}}, %{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @xor
|
||||
func @xor(%operand0: memref<2x2xi32>, %operand1: memref<2x2xi32>,
|
||||
%result: memref<2x2xi32>) {
|
||||
%tensor_operand0 = tensor_load %operand0 : memref<2x2xi32>
|
||||
%tensor_operand1 = tensor_load %operand1 : memref<2x2xi32>
|
||||
%tensor_result = "mhlo.xor"(%tensor_operand0, %tensor_operand1)
|
||||
func @xor(%operand0: tensor<2x2xi32>, %operand1: tensor<2x2xi32>)
|
||||
-> tensor<2x2xi32> {
|
||||
%result = "mhlo.xor"(%operand0, %operand1)
|
||||
: (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
|
||||
// CHECK: "lmhlo.xor"(%{{.*}}, %{{.*}})
|
||||
tensor_store %tensor_result, %result : memref<2x2xi32>
|
||||
return
|
||||
return %result : tensor<2x2xi32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// Dynamic shape binary element-wise operation.
|
||||
// CHECK-LABEL: func @add_dyn
|
||||
func @add_dyn(%lhs: tensor<?x?xf32>, %rhs: tensor<?x?xf32>) {
|
||||
func @add_dyn(%lhs: tensor<?x?xf32>, %rhs: tensor<?x?xf32>) -> tensor<?x?xf32> {
|
||||
%result = "mhlo.add"(%lhs, %rhs)
|
||||
: (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[C0:.*]] = constant 0 : index
|
||||
|
|
@ -560,14 +470,15 @@ func @add_dyn(%lhs: tensor<?x?xf32>, %rhs: tensor<?x?xf32>) {
|
|||
// CHECK: %[[ICS1:.*]] = index_cast %[[EE1]] : i64 to index
|
||||
// CHECK: %[[RESULT:.*]] = alloc(%[[ICS0]], %[[ICS1]])
|
||||
// CHECK: "lmhlo.add"(%arg0, %arg1, %[[RESULT]]) : (memref<?x?xf32>, memref<?x?xf32>, memref<?x?xf32>) -> ()
|
||||
return
|
||||
return %result : tensor<?x?xf32>
|
||||
// CHECK: return %[[RESULT]]
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// Dynamic shape unary element-wise operation.
|
||||
// CHECK-LABEL: func @tanh_dyn
|
||||
func @tanh_dyn(%arg0: tensor<?x?xf32>) {
|
||||
func @tanh_dyn(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
|
||||
%result = "mhlo.tanh"(%arg0)
|
||||
: (tensor<?x?xf32>) -> tensor<?x?xf32>
|
||||
// CHECK: %[[C0:.*]] = constant 0 : index
|
||||
|
|
@ -583,7 +494,8 @@ func @tanh_dyn(%arg0: tensor<?x?xf32>) {
|
|||
// CHECK: %[[ICS1:.*]] = index_cast %[[EE1]] : i64 to index
|
||||
// CHECK: %[[RESULT:.*]] = alloc(%[[ICS0]], %[[ICS1]])
|
||||
// CHECK: "lmhlo.tanh"(%arg0, %[[RESULT]]) : (memref<?x?xf32>, memref<?x?xf32>) -> ()
|
||||
return
|
||||
return %result : tensor<?x?xf32>
|
||||
// CHECK: return %[[RESULT]]
|
||||
}
|
||||
|
||||
// -----
|
||||
|
|
@ -600,7 +512,8 @@ func @dot(%arg0: tensor<1024x1024xf32>) -> tensor<1024x1024xf32> {
|
|||
// rhs_contracting_dimensions = dense<0> : tensor<1xi64>}}
|
||||
// : ([[TYPE]], [[TYPE]], [[TYPE]]) -> ()
|
||||
%dot = "mhlo.dot"(%arg0, %arg0)
|
||||
: (tensor<1024x1024xf32>, tensor<1024x1024xf32>) -> tensor<1024x1024xf32>
|
||||
: (tensor<1024x1024xf32>, tensor<1024x1024xf32>)
|
||||
-> tensor<1024x1024xf32>
|
||||
// CHECK: return %[[ALLOC]]
|
||||
return %dot : tensor<1024x1024xf32>
|
||||
}
|
||||
|
|
@ -608,7 +521,8 @@ func @dot(%arg0: tensor<1024x1024xf32>) -> tensor<1024x1024xf32> {
|
|||
// -----
|
||||
|
||||
// CHECK-LABEL: func @conv
|
||||
func @conv(%input: tensor<3x5x5x3xf32>, %filter : tensor<2x2x3x4xf32>) -> tensor<3x5x5x4xf32> {
|
||||
func @conv(%input: tensor<3x5x5x3xf32>, %filter : tensor<2x2x3x4xf32>)
|
||||
-> tensor<3x5x5x4xf32> {
|
||||
%c0 = constant 0 : index
|
||||
// CHECK: %[[OUT:.*]] = alloc() : memref<3x5x5x4xf32>
|
||||
// CHECK: "lmhlo.convolution"(%{{.+}}, %{{.+}}, %[[OUT]])
|
||||
|
|
@ -663,63 +577,52 @@ func @reduce(%arg0: tensor<1x8xf32>, %arg1: tensor<f32>) -> tensor<1xf32> {
|
|||
// -----
|
||||
|
||||
// CHECK-LABEL: func @transpose
|
||||
func @transpose(%operand: memref<2x2xf32>, %result: memref<2x2xf32>) {
|
||||
%tensor_operand = tensor_load %operand : memref<2x2xf32>
|
||||
%tensor_result = "mhlo.transpose"(%tensor_operand) {permutation = dense<[1, 0]> : tensor<2xi64>}
|
||||
func @transpose(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
|
||||
%result = "mhlo.transpose"(%operand) {permutation = dense<[1, 0]> : tensor<2xi64>}
|
||||
: (tensor<2x2xf32>) -> tensor<2x2xf32>
|
||||
// CHECK: "lmhlo.transpose"(%{{.*}}, %{{.*}}) {permutation = dense<[1, 0]> : tensor<2xi64>}
|
||||
// CHECK-NOT: tensor_store
|
||||
tensor_store %tensor_result, %result : memref<2x2xf32>
|
||||
return
|
||||
return %result : tensor<2x2xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @custom_call
|
||||
// CHECK-SAME:([[ARG0:%.*]]: memref<2x2xf32>, [[ARG1:%.*]]: memref<2x3xf32>, [[RESULT:%.*]]: memref<4x4xf16>)
|
||||
func @custom_call(%arg0: memref<2x2xf32>, %arg1: memref<2x3xf32>, %result: memref<4x4xf16>) {
|
||||
%arg0_tensor = tensor_load %arg0 : memref<2x2xf32>
|
||||
%arg1_tensor = tensor_load %arg1 : memref<2x3xf32>
|
||||
// CHECK-SAME:([[ARG0:%.*]]: memref<2x2xf32>, [[ARG1:%.*]]: memref<2x3xf32>)
|
||||
func @custom_call(%arg0: tensor<2x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<4x4xf16> {
|
||||
// CHECK: "lmhlo.custom_call"([[ARG0]], [[ARG1]], %{{.*}}) {backend_config = "", call_target_name = "foo", has_side_effect = false, operand_segment_sizes = dense<[2, 1]> : vector<2xi32>}
|
||||
%result_tensor = "mhlo.custom_call"(%arg0_tensor, %arg1_tensor)
|
||||
%result = "mhlo.custom_call"(%arg0, %arg1)
|
||||
{backend_config = "", call_target_name = "foo", has_side_effect = false}
|
||||
: (tensor<2x2xf32>, tensor<2x3xf32>) -> tensor<4x4xf16>
|
||||
tensor_store %result_tensor, %result: memref<4x4xf16>
|
||||
return
|
||||
}
|
||||
|
||||
// ----
|
||||
|
||||
// CHECK-LABEL: func @custom_call_multiout
|
||||
// CHECK-SAME:([[ARG0:%.*]]: memref<2x2xf32>, [[ARG1:%.*]]: memref<2x3xf32>, [[RESULT:%.*]]: memref<4x4xf16>)
|
||||
func @custom_call_multiout(%arg0: memref<2x2xf32>, %arg1: memref<2x3xf32>, %result: memref<4x4xf16>) {
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%arg0_tensor = tensor_load %arg0 : memref<2x2xf32>
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%arg1_tensor = tensor_load %arg1 : memref<2x3xf32>
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// CHECK: "lmhlo.custom_call"([[ARG0]], [[ARG1]], %{{.*}}, %{{.*}}) {backend_config = "", call_target_name = "foo", has_side_effect = false, operand_segment_sizes = dense<2> : vector<2xi32>}
|
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%temp:2 = "mhlo.custom_call"(%arg0_tensor, %arg1_tensor)
|
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{backend_config = "", call_target_name = "foo", has_side_effect = false}
|
||||
: (tensor<2x2xf32>, tensor<2x3xf32>) -> (tensor<4x4xf16>, tensor<4x4xf16>)
|
||||
%result_tensor = "mhlo.add"(%temp#0, %temp#1) : (tensor<4x4xf16>, tensor<4x4xf16>) -> tensor<4x4xf16>
|
||||
tensor_store %result_tensor, %result: memref<4x4xf16>
|
||||
return
|
||||
}
|
||||
|
||||
// ----
|
||||
|
||||
// CHECK-LABEL: func @isfinite
|
||||
func @isfinite(%arg0: memref<2x2xf32>, %result: memref<2x2xi1>) {
|
||||
%arg0_tensor = tensor_load %arg0 : memref<2x2xf32>
|
||||
// CHECK: "lmhlo.is_finite"(%{{.*}}, %{{.*}})
|
||||
%result_tensor = "mhlo.is_finite"(%arg0_tensor) : (tensor<2x2xf32>) -> tensor<2x2xi1>
|
||||
tensor_store %result_tensor, %result: memref<2x2xi1>
|
||||
return
|
||||
return %result : tensor<4x4xf16>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// Test that assuming ops propagate memref types.
|
||||
// CHECK-LABEL: func @shape_assuming_memref
|
||||
func @shape_assuming_memref(%arg0: tensor<?xf16>) -> tensor<?xf16> {
|
||||
// CHECK-LABEL: func @custom_call_multiout
|
||||
// CHECK-SAME:([[ARG0:%.*]]: memref<2x2xf32>, [[ARG1:%.*]]: memref<2x3xf32>)
|
||||
func @custom_call_multiout(%arg0: tensor<2x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<4x4xf16> {
|
||||
// CHECK: "lmhlo.custom_call"([[ARG0]], [[ARG1]], %{{.*}}, %{{.*}}) {backend_config = "", call_target_name = "foo", has_side_effect = false, operand_segment_sizes = dense<2> : vector<2xi32>}
|
||||
%temp:2 = "mhlo.custom_call"(%arg0, %arg1)
|
||||
{backend_config = "", call_target_name = "foo", has_side_effect = false}
|
||||
: (tensor<2x2xf32>, tensor<2x3xf32>) -> (tensor<4x4xf16>, tensor<4x4xf16>)
|
||||
%result = "mhlo.add"(%temp#0, %temp#1) : (tensor<4x4xf16>, tensor<4x4xf16>) -> tensor<4x4xf16>
|
||||
return %result : tensor<4x4xf16>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// CHECK-LABEL: func @isfinite
|
||||
func @isfinite(%arg0: tensor<2x2xf32>) -> tensor<2x2xi1> {
|
||||
// CHECK: "lmhlo.is_finite"(%{{.*}}, %{{.*}})
|
||||
%result = "mhlo.is_finite"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xi1>
|
||||
return %result : tensor<2x2xi1>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// Test that assuming ops propagate tensor types.
|
||||
// CHECK-LABEL: func @shape_assuming_tensor
|
||||
func @shape_assuming_tensor(%arg0: tensor<?xf16>) -> tensor<?xf16> {
|
||||
%0 = mhlo.constant dense<0.000000e+00> : tensor<f16>
|
||||
%1 = shape.const_witness true
|
||||
// CHECK: shape.assuming %{{.*}} -> (memref<?xf16>)
|
||||
|
|
|
|||
Loading…
Reference in New Issue