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
112 lines
3.9 KiB
MLIR
112 lines
3.9 KiB
MLIR
// RUN: mlir-opt %s --sparse-compiler | \
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// RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \
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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=4" | \
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// RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \
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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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#SparseTensor = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed", "compressed", "compressed",
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"compressed", "compressed", "compressed", "compressed" ],
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// Note that any dimOrdering permutation should give the same results
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// since, even though it impacts the sparse storage scheme layout,
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// it should not change the semantics.
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dimOrdering = affine_map<(i,j,k,l,m,n,o,p) -> (p,o,j,k,i,l,m,n)>
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}>
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#trait_flatten = {
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indexing_maps = [
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j)> // X (out)
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],
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iterator_types = [ "parallel", "parallel", "reduction", "reduction",
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"reduction", "reduction", "reduction", "reduction" ],
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doc = "X(i,j) += A(i,j,k,l,m,n,o,p)"
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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 flattens a rank 8 tensor into a dense matrix.
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//
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func.func @kernel_flatten(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>,
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%argx: tensor<7x3xf64> {linalg.inplaceable = true})
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-> tensor<7x3xf64> {
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%0 = linalg.generic #trait_flatten
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ins(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>)
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outs(%argx: tensor<7x3xf64>) {
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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<7x3xf64>
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return %0 : tensor<7x3xf64>
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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 tensor 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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%c1 = arith.constant 1 : index
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%c3 = arith.constant 3 : index
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%c7 = arith.constant 7 : index
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// Setup matrix memory that is initialized to zero.
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%xdata = memref.alloc() : memref<7x3xf64>
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scf.for %i = %c0 to %c7 step %c1 {
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scf.for %j = %c0 to %c3 step %c1 {
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memref.store %d0, %xdata[%i, %j] : memref<7x3xf64>
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}
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}
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%x = bufferization.to_tensor %xdata : memref<7x3xf64>
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// Read the sparse tensor 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<7x3x3x3x3x3x5x3xf64, #SparseTensor>
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// Call the kernel.
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%0 = call @kernel_flatten(%a, %x)
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: (tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, tensor<7x3xf64>) -> tensor<7x3xf64>
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// Print the result for verification.
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//
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// CHECK: ( 6.25, 0, 0 )
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// CHECK: ( 4.224, 6.21, 0 )
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// CHECK: ( 0, 0, 15.455 )
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// CHECK: ( 0, 0, 0 )
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// CHECK: ( 0, 0, 0 )
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// CHECK: ( 0, 0, 0 )
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// CHECK: ( 7, 0, 0 )
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//
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%r = bufferization.to_memref %0 : memref<7x3xf64>
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scf.for %i = %c0 to %c7 step %c1 {
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%v = vector.transfer_read %r[%i, %c0], %d0: memref<7x3xf64>, vector<3xf64>
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vector.print %v : vector<3xf64>
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}
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// Release the resources.
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memref.dealloc %xdata : memref<7x3xf64>
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sparse_tensor.release %a : tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>
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return
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}
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}
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