6b30fb7653
Once we have tensor_to_memref ops suitable for type materializations, this pass can be split into a generic type conversion pattern. Part of the refactor discussed in: https://llvm.discourse.group/t/what-is-the-strategy-for-tensor-memref-conversion-bufferization/1938/17 Differential Revision: https://reviews.llvm.org/D89258
17 lines
502 B
MLIR
17 lines
502 B
MLIR
// RUN: mlir-opt -split-input-file -shape-bufferize <%s | FileCheck %s
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// -----
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// Check that shape.assuming returns a memref.
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//
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// CHECK-LABEL: @shape_assuming_returns_memref
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func @shape_assuming_returns_memref() {
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%0 = shape.const_witness true
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// CHECK: shape.assuming %{{.*}} -> (memref<2xf16>) {
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%1 = shape.assuming %0 -> (tensor<2xf16>) {
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%2 = "test.source"() : () -> (tensor<2xf16>)
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shape.assuming_yield %2 : tensor<2xf16>
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}
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"test.sink"(%1) : (tensor<2xf16>) -> ()
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return
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}
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