27a431f5e9
This op used to belong to the sparse dialect, but there are use cases for dense bufferization as well. (E.g., when a tensor alloc is returned from a function and should be deallocated at the call site.) This change moves the op to the bufferization dialect, which now has an `alloc_tensor` and a `dealloc_tensor` op. Differential Revision: https://reviews.llvm.org/D129985
56 lines
1.9 KiB
MLIR
56 lines
1.9 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
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// RUN: mlir-cpu-runner -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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#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
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module {
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//
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// Sparse kernel.
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//
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func.func @sparse_dot(%a: tensor<1024xf32, #SparseVector>,
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%b: tensor<1024xf32, #SparseVector>,
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%x: tensor<f32>) -> tensor<f32> {
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%dot = linalg.dot ins(%a, %b: tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>)
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outs(%x: tensor<f32>) -> tensor<f32>
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return %dot : tensor<f32>
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}
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//
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// Main driver.
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//
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func.func @entry() {
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// Setup two sparse vectors.
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%d1 = arith.constant sparse<
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[ [0], [1], [22], [23], [1022] ], [1.0, 2.0, 3.0, 4.0, 5.0]
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> : tensor<1024xf32>
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%d2 = arith.constant sparse<
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[ [22], [1022], [1023] ], [6.0, 7.0, 8.0]
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> : tensor<1024xf32>
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%s1 = sparse_tensor.convert %d1 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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%s2 = sparse_tensor.convert %d2 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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// Call the kernel and verify the output.
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//
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// CHECK: 53
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//
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%t = bufferization.alloc_tensor() : tensor<f32>
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%z = arith.constant 0.0 : f32
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%x = tensor.insert %z into %t[] : tensor<f32>
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%0 = call @sparse_dot(%s1, %s2, %x) : (tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>,
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tensor<f32>) -> tensor<f32>
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%1 = tensor.extract %0[] : tensor<f32>
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vector.print %1 : f32
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// Release the resources.
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bufferization.dealloc_tensor %s1 : tensor<1024xf32, #SparseVector>
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bufferization.dealloc_tensor %s2 : tensor<1024xf32, #SparseVector>
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return
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}
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}
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