| // RUN: mlir-opt %s \ |
| // RUN: --sparsification --sparse-tensor-conversion \ |
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ |
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ |
| // RUN: --std-bufferize --finalizing-bufferize --lower-affine \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \ |
| // RUN: mlir-cpu-runner \ |
| // RUN: -e entry -entry-point-result=void \ |
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ |
| // RUN: FileCheck %s |
| // |
| // Do the same run, but now with SIMDization as well. This should not change the outcome. |
| // |
| // RUN: mlir-opt %s \ |
| // RUN: --sparsification="vectorization-strategy=2 vl=4" --sparse-tensor-conversion \ |
| // RUN: --convert-vector-to-scf --convert-scf-to-std \ |
| // RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \ |
| // RUN: --std-bufferize --finalizing-bufferize --lower-affine \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \ |
| // RUN: mlir-cpu-runner \ |
| // RUN: -e entry -entry-point-result=void \ |
| // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ |
| // RUN: FileCheck %s |
| |
| !Filename = type !llvm.ptr<i8> |
| |
| #SparseTensor = #sparse_tensor.encoding<{ |
| dimLevelType = [ "compressed", "compressed", "compressed", "compressed", |
| "compressed", "compressed", "compressed", "compressed" ], |
| // Note that any dimOrdering permutation should give the same results |
| // since, even though it impacts the sparse storage scheme layout, |
| // it should not change the semantics. |
| dimOrdering = affine_map<(i,j,k,l,m,n,o,p) -> (p,o,j,k,i,l,m,n)> |
| }> |
| |
| #trait_flatten = { |
| indexing_maps = [ |
| affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A |
| affine_map<(i,j,k,l,m,n,o,p) -> (i,j)> // X (out) |
| ], |
| iterator_types = [ "parallel", "parallel", "reduction", "reduction", |
| "reduction", "reduction", "reduction", "reduction" ], |
| doc = "X(i,j) += A(i,j,k,l,m,n,o,p)" |
| } |
| |
| // |
| // Integration test that lowers a kernel annotated as sparse to |
| // actual sparse code, initializes a matching sparse storage scheme |
| // from file, and runs the resulting code with the JIT compiler. |
| // |
| module { |
| // |
| // A kernel that flattens a rank 8 tensor into a dense matrix. |
| // |
| func @kernel_flatten(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, |
| %argx: tensor<7x3xf64> {linalg.inplaceable = true}) |
| -> tensor<7x3xf64> { |
| %0 = linalg.generic #trait_flatten |
| ins(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>) |
| outs(%argx: tensor<7x3xf64>) { |
| ^bb(%a: f64, %x: f64): |
| %0 = arith.addf %x, %a : f64 |
| linalg.yield %0 : f64 |
| } -> tensor<7x3xf64> |
| return %0 : tensor<7x3xf64> |
| } |
| |
| func private @getTensorFilename(index) -> (!Filename) |
| |
| // |
| // Main driver that reads tensor from file and calls the sparse kernel. |
| // |
| func @entry() { |
| %d0 = arith.constant 0.0 : f64 |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %c3 = arith.constant 3 : index |
| %c7 = arith.constant 7 : index |
| |
| // Setup matrix memory that is initialized to zero. |
| %xdata = memref.alloc() : memref<7x3xf64> |
| scf.for %i = %c0 to %c7 step %c1 { |
| scf.for %j = %c0 to %c3 step %c1 { |
| memref.store %d0, %xdata[%i, %j] : memref<7x3xf64> |
| } |
| } |
| %x = bufferization.to_tensor %xdata : memref<7x3xf64> |
| |
| // Read the sparse tensor from file, construct sparse storage. |
| %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) |
| %a = sparse_tensor.new %fileName : !Filename to tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor> |
| |
| // Call the kernel. |
| %0 = call @kernel_flatten(%a, %x) |
| : (tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, tensor<7x3xf64>) -> tensor<7x3xf64> |
| |
| // Print the result for verification. |
| // |
| // CHECK: ( 6.25, 0, 0 ) |
| // CHECK: ( 4.224, 6.21, 0 ) |
| // CHECK: ( 0, 0, 15.455 ) |
| // CHECK: ( 0, 0, 0 ) |
| // CHECK: ( 0, 0, 0 ) |
| // CHECK: ( 0, 0, 0 ) |
| // CHECK: ( 7, 0, 0 ) |
| // |
| %r = bufferization.to_memref %0 : memref<7x3xf64> |
| scf.for %i = %c0 to %c7 step %c1 { |
| %v = vector.transfer_read %r[%i, %c0], %d0: memref<7x3xf64>, vector<3xf64> |
| vector.print %v : vector<3xf64> |
| } |
| |
| // Release the resources. |
| memref.dealloc %xdata : memref<7x3xf64> |
| sparse_tensor.release %a : tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor> |
| |
| return |
| } |
| } |