| // 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 \ |
| // RUN: --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // RUN: TENSOR0="%mlir_integration_test_dir/data/test.mtx" \ |
| // 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 enable-simd-index32" --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.mtx" \ |
| // 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> |
| |
| #SparseMatrix = #sparse_tensor.encoding<{ |
| dimLevelType = [ "compressed", "compressed" ], |
| pointerBitWidth = 32, |
| indexBitWidth = 32 |
| }> |
| |
| #trait_sampled_dense_dense = { |
| indexing_maps = [ |
| affine_map<(i,j,k) -> (i,j)>, // S |
| affine_map<(i,j,k) -> (i,k)>, // A |
| affine_map<(i,j,k) -> (k,j)>, // B |
| affine_map<(i,j,k) -> (i,j)> // X (out) |
| ], |
| iterator_types = ["parallel", "parallel", "reduction"], |
| doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)" |
| } |
| |
| // |
| // 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 computes a sampled matrix matrix multiplication. |
| // |
| func @sampled_dense_dense(%args: tensor<?x?xf32, #SparseMatrix>, |
| %arga: tensor<?x?xf32>, |
| %argb: tensor<?x?xf32>, |
| %argx: tensor<?x?xf32> {linalg.inplaceable = true}) -> tensor<?x?xf32> { |
| %0 = linalg.generic #trait_sampled_dense_dense |
| ins(%args, %arga, %argb: tensor<?x?xf32, #SparseMatrix>, tensor<?x?xf32>, tensor<?x?xf32>) |
| outs(%argx: tensor<?x?xf32>) { |
| ^bb(%s: f32, %a: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %a, %b : f32 |
| %1 = arith.mulf %s, %0 : f32 |
| %2 = arith.addf %x, %1 : f32 |
| linalg.yield %2 : f32 |
| } -> tensor<?x?xf32> |
| return %0 : tensor<?x?xf32> |
| } |
| |
| func private @getTensorFilename(index) -> (!Filename) |
| |
| // |
| // Main driver that reads matrix from file and calls the sparse kernel. |
| // |
| func @entry() { |
| %d0 = arith.constant 0.0 : f32 |
| %c0 = arith.constant 0 : index |
| %c1 = arith.constant 1 : index |
| %c5 = arith.constant 5 : index |
| %c10 = arith.constant 10 : index |
| |
| // Setup memory for the dense matrices and initialize. |
| %adata = memref.alloc(%c5, %c10) : memref<?x?xf32> |
| %bdata = memref.alloc(%c10, %c5) : memref<?x?xf32> |
| %xdata = memref.alloc(%c5, %c5) : memref<?x?xf32> |
| scf.for %i = %c0 to %c5 step %c1 { |
| scf.for %j = %c0 to %c5 step %c1 { |
| memref.store %d0, %xdata[%i, %j] : memref<?x?xf32> |
| } |
| %p = arith.addi %i, %c1 : index |
| %q = arith.index_cast %p : index to i32 |
| %d = arith.sitofp %q : i32 to f32 |
| scf.for %j = %c0 to %c10 step %c1 { |
| memref.store %d, %adata[%i, %j] : memref<?x?xf32> |
| memref.store %d, %bdata[%j, %i] : memref<?x?xf32> |
| } |
| } |
| %a = bufferization.to_tensor %adata : memref<?x?xf32> |
| %b = bufferization.to_tensor %bdata : memref<?x?xf32> |
| %x = bufferization.to_tensor %xdata : memref<?x?xf32> |
| |
| // Read the sparse matrix from file, construct sparse storage. |
| %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename) |
| %s = sparse_tensor.new %fileName : !Filename to tensor<?x?xf32, #SparseMatrix> |
| |
| // Call the kernel. |
| %0 = call @sampled_dense_dense(%s, %a, %b, %x) |
| : (tensor<?x?xf32, #SparseMatrix>, |
| tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32> |
| |
| // Print the result for verification. |
| // |
| // CHECK: ( 10, 0, 0, 56, 0 ) |
| // CHECK: ( 0, 80, 0, 0, 250 ) |
| // CHECK: ( 0, 0, 270, 0, 0 ) |
| // CHECK: ( 164, 0, 0, 640, 0 ) |
| // CHECK: ( 0, 520, 0, 0, 1250 ) |
| // |
| %r = bufferization.to_memref %0 : memref<?x?xf32> |
| scf.for %i = %c0 to %c5 step %c1 { |
| %v = vector.transfer_read %r[%i, %c0], %d0: memref<?x?xf32>, vector<5xf32> |
| vector.print %v : vector<5xf32> |
| } |
| |
| // Release the resources. |
| memref.dealloc %adata : memref<?x?xf32> |
| memref.dealloc %bdata : memref<?x?xf32> |
| memref.dealloc %xdata : memref<?x?xf32> |
| sparse_tensor.release %s : tensor<?x?xf32, #SparseMatrix> |
| |
| return |
| } |
| } |