a6cef03f66
This was carry over from LLVM IR where the alias definition can be ambiguous, but MLIR type aliases have no such problems. Having the `type` keyword is superfluous and doesn't add anything. This commit drops it, which also nicely aligns with the syntax for attribute aliases (which doesn't have a keyword). Differential Revision: https://reviews.llvm.org/D125501
91 lines
2.8 KiB
MLIR
91 lines
2.8 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
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// RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.mtx" \
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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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//
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// Do the same run, but now with SIMDization as well. This should not change the outcome.
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//
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// RUN: mlir-opt %s --sparse-compiler="vectorization-strategy=2 vl=2" | \
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// RUN: TENSOR0="%mlir_integration_test_dir/data/test_symmetric.mtx" \
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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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!Filename = !llvm.ptr<i8>
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#SparseMatrix = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ]
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}>
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#trait_sum_reduce = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> ()> // x (out)
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],
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iterator_types = ["reduction", "reduction"],
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doc = "x += A(i,j)"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to
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// actual sparse code, initializes a matching sparse storage scheme
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// from file, and runs the resulting code with the JIT compiler.
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//
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module {
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//
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// A kernel that sum-reduces a matrix to a single scalar.
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//
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func.func @kernel_sum_reduce(%arga: tensor<?x?xf64, #SparseMatrix>,
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%argx: tensor<f64> {linalg.inplaceable = true}) -> tensor<f64> {
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%0 = linalg.generic #trait_sum_reduce
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ins(%arga: tensor<?x?xf64, #SparseMatrix>)
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outs(%argx: tensor<f64>) {
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^bb(%a: f64, %x: f64):
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%0 = arith.addf %x, %a : f64
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linalg.yield %0 : f64
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} -> tensor<f64>
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return %0 : tensor<f64>
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}
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func.func private @getTensorFilename(index) -> (!Filename)
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//
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// Main driver that reads matrix from file and calls the sparse kernel.
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//
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func.func @entry() {
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%d0 = arith.constant 0.0 : f64
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%c0 = arith.constant 0 : index
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// Setup memory for a single reduction scalar,
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// initialized to zero.
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%xdata = memref.alloc() : memref<f64>
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memref.store %d0, %xdata[] : memref<f64>
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%x = bufferization.to_tensor %xdata : memref<f64>
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// Read the sparse matrix from file, construct sparse storage.
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%a = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #SparseMatrix>
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// Call the kernel.
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%0 = call @kernel_sum_reduce(%a, %x)
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: (tensor<?x?xf64, #SparseMatrix>, tensor<f64>) -> tensor<f64>
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// Print the result for verification.
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//
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// CHECK: 30.2
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//
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%m = bufferization.to_memref %0 : memref<f64>
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%v = memref.load %m[] : memref<f64>
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vector.print %v : f64
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// Release the resources.
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memref.dealloc %xdata : memref<f64>
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sparse_tensor.release %a : tensor<?x?xf64, #SparseMatrix>
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return
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}
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}
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