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[mlir][tensor][bufferize] tensor.empty bufferizes to an allocation #68080

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Oct 3, 2023
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[mlir][tensor][bufferize] tensor.empty bufferizes to an allocation #68080

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f486f82
[mlir][tensor][bufferize] `tensor.empty` bufferizes to an allocation
matthias-springer Oct 3, 2023
1b0808f
Update mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl…
matthias-springer Oct 3, 2023
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21 changes: 11 additions & 10 deletions mlir/lib/Dialect/Tensor/Transforms/BufferizableOpInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -253,10 +253,7 @@ struct DimOpInterface
}
};

/// Bufferization of tensor.empty. This op does not bufferize, but we need an
/// interface implementation, so that the result of this op is considered
/// "writable" (default impl. of `isWritable`). Results of ops that do not
/// implement `BufferizableOpInterface` are not writable.
/// Bufferization of "tensor.empty". Replace with "bufferization.alloc_tensor".
struct EmptyOpInterface
: public BufferizableOpInterface::ExternalModel<EmptyOpInterface,
tensor::EmptyOp> {
Expand All @@ -268,17 +265,21 @@ struct EmptyOpInterface

LogicalResult bufferize(Operation *op, RewriterBase &rewriter,
const BufferizationOptions &options) const {
auto emptyOp = cast<tensor::EmptyOp>(op);

// Optimization: Fold away the op if it has no uses.
if (op->getUses().empty()) {
rewriter.eraseOp(op);
return success();
}

// tensor.empty ops are used to indicate the shape of a tensor. They have
// no defined contents and cannot be bufferized. However, they can be
// converted to bufferization.alloc_tensor ops, which then bufferize to an
// allocation (--empty-tensor-to-alloc-tensor).
return op->emitOpError("cannot be bufferized, but can be converted to "
"bufferization.alloc_tensor");
// Allocate a tensor. This emits a "bufferization.alloc_tensor" op.
FailureOr<Value> allocTensor = allocateTensorForShapedValue(
rewriter, op->getLoc(), emptyOp.getResult(), options, /*copy=*/false);
if (failed(allocTensor))
return failure();
rewriter.replaceOp(op, *allocTensor);
return success();
}
};

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9 changes: 6 additions & 3 deletions mlir/test/Dialect/Tensor/bufferize.mlir
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// RUN: mlir-opt %s -tensor-bufferize -cse -split-input-file -verify-diagnostics | FileCheck %s
// RUN: mlir-opt %s -tensor-bufferize -cse -split-input-file | FileCheck %s

// CHECK-LABEL: func @dim(
// CHECK-SAME: %[[TENSOR:.*]]: tensor<*xf32>,
Expand Down Expand Up @@ -62,9 +62,12 @@ func.func @tensor.cast_to_unranked(%arg0: tensor<2xf32>) -> tensor<*xf32> {
}

// -----

// CHECK-LABEL: func @tensor.empty(
// CHECK: %[[ALLOC:.*]] = memref.alloc() {{.*}} : memref<5xf32>
// CHECK: %[[RET:.*]] = bufferization.to_tensor %[[ALLOC]] : memref<5xf32>
// CHECK: return %[[RET]] : tensor<5xf32>
func.func @tensor.empty() -> tensor<5xf32> {
// expected-error@+2 {{failed to bufferize op}}
// expected-error@+1 {{cannot be bufferized, but can be converted to bufferization.alloc_tensor}}
%0 = tensor.empty() : tensor<5xf32>
return %0 : tensor<5xf32>
}
Expand Down