9d1db3d4a1
This revison lifts the artificial restriction on having exact matches between source and destination type shapes. A static size may become dynamic. We still reject changing a dynamic size into a static size to avoid the need for a runtime "assert" on the conversion. This revision also refactors some of the conversion code to share same-content buffers. Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D111915
92 lines
3.6 KiB
MLIR
92 lines
3.6 KiB
MLIR
// RUN: mlir-opt %s \
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// RUN: --sparsification --sparse-tensor-conversion \
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// RUN: --linalg-bufferize --convert-linalg-to-loops \
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// RUN: --convert-vector-to-scf --convert-scf-to-std \
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// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
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// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
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// RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-math-to-llvm \
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// RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \
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// RUN: mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
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// RUN: FileCheck %s
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#DCSR = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ]
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}>
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#DCSC = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ],
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dimOrdering = affine_map<(i,j) -> (j,i)>
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}>
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//
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// Integration test that tests conversions between sparse tensors,
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// where the dynamic sizes of the shape of the enveloping tensor
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// may change (the actual underlying sizes obviously never change).
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//
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module {
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//
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// Helper method to print values array. The transfer actually
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// reads more than required to verify size of buffer as well.
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//
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func @dump(%arg0: memref<?xf64>) {
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%c = arith.constant 0 : index
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%d = arith.constant -1.0 : f64
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%0 = vector.transfer_read %arg0[%c], %d: memref<?xf64>, vector<8xf64>
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vector.print %0 : vector<8xf64>
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return
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}
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func @entry() {
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%t1 = arith.constant sparse<
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[ [0,0], [0,1], [0,63], [1,0], [1,1], [31,0], [31,63] ],
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[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 ]> : tensor<32x64xf64>
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%t2 = tensor.cast %t1 : tensor<32x64xf64> to tensor<?x?xf64>
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// Four dense to sparse conversions.
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%1 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSR>
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%2 = sparse_tensor.convert %t1 : tensor<32x64xf64> to tensor<?x?xf64, #DCSC>
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%3 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSR>
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%4 = sparse_tensor.convert %t2 : tensor<?x?xf64> to tensor<?x?xf64, #DCSC>
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// Two cross conversions.
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%5 = sparse_tensor.convert %3 : tensor<?x?xf64, #DCSR> to tensor<?x?xf64, #DCSC>
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%6 = sparse_tensor.convert %4 : tensor<?x?xf64, #DCSC> to tensor<?x?xf64, #DCSR>
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//
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// All proper row-/column-wise?
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//
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// CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
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// CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
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// CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
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// CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
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// CHECK: ( 1, 4, 6, 2, 5, 3, 7, -1 )
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// CHECK: ( 1, 2, 3, 4, 5, 6, 7, -1 )
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//
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%m1 = sparse_tensor.values %1 : tensor<?x?xf64, #DCSR> to memref<?xf64>
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%m2 = sparse_tensor.values %2 : tensor<?x?xf64, #DCSC> to memref<?xf64>
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%m3 = sparse_tensor.values %3 : tensor<?x?xf64, #DCSR> to memref<?xf64>
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%m4 = sparse_tensor.values %4 : tensor<?x?xf64, #DCSC> to memref<?xf64>
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%m5 = sparse_tensor.values %5 : tensor<?x?xf64, #DCSC> to memref<?xf64>
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%m6 = sparse_tensor.values %6 : tensor<?x?xf64, #DCSR> to memref<?xf64>
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call @dump(%m1) : (memref<?xf64>) -> ()
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call @dump(%m2) : (memref<?xf64>) -> ()
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call @dump(%m3) : (memref<?xf64>) -> ()
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call @dump(%m4) : (memref<?xf64>) -> ()
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call @dump(%m5) : (memref<?xf64>) -> ()
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call @dump(%m6) : (memref<?xf64>) -> ()
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// Release the resources.
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sparse_tensor.release %1 : tensor<?x?xf64, #DCSR>
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sparse_tensor.release %2 : tensor<?x?xf64, #DCSC>
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sparse_tensor.release %3 : tensor<?x?xf64, #DCSR>
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sparse_tensor.release %4 : tensor<?x?xf64, #DCSC>
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sparse_tensor.release %5 : tensor<?x?xf64, #DCSC>
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sparse_tensor.release %6 : tensor<?x?xf64, #DCSR>
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return
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}
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}
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