//-------------------------------------------------------------------------------------------------- // WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS. // // Set-up that's shared across all tests in this directory. In principle, this // config could be moved to lit.local.cfg. However, there are downstream users that // do not use these LIT config files. Hence why this is kept inline. // // DEFINE: %{sparsifier_opts} = enable-runtime-library=true // DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts} // DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}" // DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}" // DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils // DEFINE: %{run_opts} = -e main -entry-point-result=void // DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs} // DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs} // // DEFINE: %{env} = //-------------------------------------------------------------------------------------------------- // RUN: %{compile} | %{run} | FileCheck %s // // Do the same run, but now with direct IR generation. // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true // RUN: %{compile} | %{run} | FileCheck %s // // Do the same run, but now with parallelization strategy. // REDEFINE: %{sparsifier_opts} = enable-runtime-library=true parallelization-strategy=any-storage-any-loop // RUN: %{compile} | %{run} | FileCheck %s // // Do the same run, but now with direct IR generation and parallelization strategy. // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true parallelization-strategy=any-storage-any-loop // RUN: %{compile} | %{run} | FileCheck %s // // Do the same run, but now with direct IR generation and vectorization. // REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true // RUN: %{compile} | %{run} | FileCheck %s // // Do the same run, but now with direct IR generation and VLA vectorization. // RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %} // TODO: Investigate the output generated for SVE, see https://github.com/llvm/llvm-project/issues/60626 #CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }> #DCSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }> module { func.func private @printMemrefF64(%ptr : tensor<*xf64>) func.func private @printMemref1dF64(%ptr : memref) attributes { llvm.emit_c_interface } // // Computes C = A x B with all matrices dense. // func.func @matmul1(%A: tensor<4x8xf64>, %B: tensor<8x4xf64>, %C: tensor<4x4xf64>) -> tensor<4x4xf64> { %D = linalg.matmul ins(%A, %B: tensor<4x8xf64>, tensor<8x4xf64>) outs(%C: tensor<4x4xf64>) -> tensor<4x4xf64> return %D: tensor<4x4xf64> } // // Computes C = A x B with all matrices sparse (SpMSpM) in CSR. // func.func @matmul2(%A: tensor<4x8xf64, #CSR>, %B: tensor<8x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> { %C = tensor.empty() : tensor<4x4xf64, #CSR> %D = linalg.matmul ins(%A, %B: tensor<4x8xf64, #CSR>, tensor<8x4xf64, #CSR>) outs(%C: tensor<4x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> return %D: tensor<4x4xf64, #CSR> } // // Computes C = A x B with all matrices sparse (SpMSpM) in DCSR. // func.func @matmul3(%A: tensor<4x8xf64, #DCSR>, %B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> { %C = tensor.empty() : tensor<4x4xf64, #DCSR> %D = linalg.matmul ins(%A, %B: tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>) outs(%C: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> return %D: tensor<4x4xf64, #DCSR> } // // Main driver. // func.func @main() { %c0 = arith.constant 0 : index // Initialize various matrices, dense for stress testing, // and sparse to verify correct nonzero structure. %da = arith.constant dense<[ [ 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1 ], [ 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2 ], [ 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3 ], [ 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 ] ]> : tensor<4x8xf64> %db = arith.constant dense<[ [ 10.1, 11.1, 12.1, 13.1 ], [ 10.2, 11.2, 12.2, 13.2 ], [ 10.3, 11.3, 12.3, 13.3 ], [ 10.4, 11.4, 12.4, 13.4 ], [ 10.5, 11.5, 12.5, 13.5 ], [ 10.6, 11.6, 12.6, 13.6 ], [ 10.7, 11.7, 12.7, 13.7 ], [ 10.8, 11.8, 12.8, 13.8 ] ]> : tensor<8x4xf64> %sa = arith.constant dense<[ [ 0.0, 2.1, 0.0, 0.0, 0.0, 6.1, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 2.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ] ]> : tensor<4x8xf64> %sb = arith.constant dense<[ [ 0.0, 0.0, 0.0, 1.0 ], [ 0.0, 0.0, 2.0, 0.0 ], [ 0.0, 3.0, 0.0, 0.0 ], [ 4.0, 0.0, 0.0, 0.0 ], [ 0.0, 0.0, 0.0, 0.0 ], [ 0.0, 5.0, 0.0, 0.0 ], [ 0.0, 0.0, 6.0, 0.0 ], [ 0.0, 0.0, 7.0, 8.0 ] ]> : tensor<8x4xf64> %zero = arith.constant dense<0.0> : tensor<4x4xf64> // Convert all these matrices to sparse format. %a1 = sparse_tensor.convert %da : tensor<4x8xf64> to tensor<4x8xf64, #CSR> %a2 = sparse_tensor.convert %da : tensor<4x8xf64> to tensor<4x8xf64, #DCSR> %a3 = sparse_tensor.convert %sa : tensor<4x8xf64> to tensor<4x8xf64, #CSR> %a4 = sparse_tensor.convert %sa : tensor<4x8xf64> to tensor<4x8xf64, #DCSR> %b1 = sparse_tensor.convert %db : tensor<8x4xf64> to tensor<8x4xf64, #CSR> %b2 = sparse_tensor.convert %db : tensor<8x4xf64> to tensor<8x4xf64, #DCSR> %b3 = sparse_tensor.convert %sb : tensor<8x4xf64> to tensor<8x4xf64, #CSR> %b4 = sparse_tensor.convert %sb : tensor<8x4xf64> to tensor<8x4xf64, #DCSR> // // Sanity check before going into the computations. // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 32 // CHECK-NEXT: dim = ( 4, 8 ) // CHECK-NEXT: lvl = ( 4, 8 ) // CHECK-NEXT: pos[1] : ( 0, 8, 16, 24, 32 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7 // CHECK-NEXT: values : ( 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 // CHECK-NEXT: ---- // sparse_tensor.print %a1 : tensor<4x8xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 32 // CHECK-NEXT: dim = ( 4, 8 ) // CHECK-NEXT: lvl = ( 4, 8 ) // CHECK-NEXT: pos[0] : ( 0, 4 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 // CHECK-NEXT: pos[1] : ( 0, 8, 16, 24, 32 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7, 0, 1, 2, 3, 4, 5, 6, 7 // CHECK-NEXT: values : ( 1.1, 2.1, 3.1, 4.1, 5.1, 6.1, 7.1, 8.1, 1.2, 2.2, 3.2, 4.2, 5.2, 6.2, 7.2, 8.2, 1.3, 2.3, 3.3, 4.3, 5.3, 6.3, 7.3, 8.3, 1.4, 2.4, 3.4, 4.4, 5.4, 6.4, 7.4, 8.4 // CHECK-NEXT: ---- // sparse_tensor.print %a2 : tensor<4x8xf64, #DCSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 4 // CHECK-NEXT: dim = ( 4, 8 ) // CHECK-NEXT: lvl = ( 4, 8 ) // CHECK-NEXT: pos[1] : ( 0, 2, 2, 3, 4 // CHECK-NEXT: crd[1] : ( 1, 5, 1, 7 // CHECK-NEXT: values : ( 2.1, 6.1, 2.3, 1 // CHECK-NEXT: ---- // sparse_tensor.print %a3 : tensor<4x8xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 4 // CHECK-NEXT: dim = ( 4, 8 ) // CHECK-NEXT: lvl = ( 4, 8 ) // CHECK-NEXT: pos[0] : ( 0, 3 // CHECK-NEXT: crd[0] : ( 0, 2, 3 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 4 // CHECK-NEXT: crd[1] : ( 1, 5, 1, 7 // CHECK-NEXT: values : ( 2.1, 6.1, 2.3, 1 // CHECK-NEXT: ---- // sparse_tensor.print %a4 : tensor<4x8xf64, #DCSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 32 // CHECK-NEXT: dim = ( 8, 4 ) // CHECK-NEXT: lvl = ( 8, 4 ) // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12, 16, 20, 24, 28, 32 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 10.1, 11.1, 12.1, 13.1, 10.2, 11.2, 12.2, 13.2, 10.3, 11.3, 12.3, 13.3, 10.4, 11.4, 12.4, 13.4, 10.5, 11.5, 12.5, 13.5, 10.6, 11.6, 12.6, 13.6, 10.7, 11.7, 12.7, 13.7, 10.8, 11.8, 12.8, 13.8 // CHECK-NEXT: ---- // sparse_tensor.print %b1 : tensor<8x4xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 32 // CHECK-NEXT: dim = ( 8, 4 ) // CHECK-NEXT: lvl = ( 8, 4 ) // CHECK-NEXT: pos[0] : ( 0, 8 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7 // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12, 16, 20, 24, 28, 32 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 10.1, 11.1, 12.1, 13.1, 10.2, 11.2, 12.2, 13.2, 10.3, 11.3, 12.3, 13.3, 10.4, 11.4, 12.4, 13.4, 10.5, 11.5, 12.5, 13.5, 10.6, 11.6, 12.6, 13.6, 10.7, 11.7, 12.7, 13.7, 10.8, 11.8, 12.8, 13.8 // CHECK-NEXT: ---- // sparse_tensor.print %b2 : tensor<8x4xf64, #DCSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 8 // CHECK-NEXT: dim = ( 8, 4 ) // CHECK-NEXT: lvl = ( 8, 4 ) // CHECK-NEXT: pos[1] : ( 0, 1, 2, 3, 4, 4, 5, 6, 8 // CHECK-NEXT: crd[1] : ( 3, 2, 1, 0, 1, 2, 2, 3 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8 // CHECK-NEXT: ---- // sparse_tensor.print %b3 : tensor<8x4xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 8 // CHECK-NEXT: dim = ( 8, 4 ) // CHECK-NEXT: lvl = ( 8, 4 ) // CHECK-NEXT: pos[0] : ( 0, 7 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 5, 6, 7 // CHECK-NEXT: pos[1] : ( 0, 1, 2, 3, 4, 5, 6, 8 // CHECK-NEXT: crd[1] : ( 3, 2, 1, 0, 1, 2, 2, 3 // CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8 // CHECK-NEXT: ---- // sparse_tensor.print %b4 : tensor<8x4xf64, #DCSR> // Call kernels with dense. %0 = call @matmul1(%da, %db, %zero) : (tensor<4x8xf64>, tensor<8x4xf64>, tensor<4x4xf64>) -> tensor<4x4xf64> %1 = call @matmul2(%a1, %b1) : (tensor<4x8xf64, #CSR>, tensor<8x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> %2 = call @matmul3(%a2, %b2) : (tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> // Call kernels with one sparse. %3 = call @matmul1(%sa, %db, %zero) : (tensor<4x8xf64>, tensor<8x4xf64>, tensor<4x4xf64>) -> tensor<4x4xf64> %4 = call @matmul2(%a3, %b1) : (tensor<4x8xf64, #CSR>, tensor<8x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> %5 = call @matmul3(%a4, %b2) : (tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> // Call kernels with sparse. %6 = call @matmul1(%sa, %sb, %zero) : (tensor<4x8xf64>, tensor<8x4xf64>, tensor<4x4xf64>) -> tensor<4x4xf64> %7 = call @matmul2(%a3, %b3) : (tensor<4x8xf64, #CSR>, tensor<8x4xf64, #CSR>) -> tensor<4x4xf64, #CSR> %8 = call @matmul3(%a4, %b4) : (tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> // // CHECK: {{\[}}[388.76, 425.56, 462.36, 499.16], // CHECK-NEXT: [397.12, 434.72, 472.32, 509.92], // CHECK-NEXT: [405.48, 443.88, 482.28, 520.68], // CHECK-NEXT: [413.84, 453.04, 492.24, 531.44]] // %u0 = tensor.cast %0 : tensor<4x4xf64> to tensor<*xf64> call @printMemrefF64(%u0) : (tensor<*xf64>) -> () // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 16 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12, 16 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 388.76, 425.56, 462.36, 499.16, 397.12, 434.72, 472.32, 509.92, 405.48, 443.88, 482.28, 520.68, 413.84, 453.04, 492.24, 531.44 // CHECK-NEXT: ---- // sparse_tensor.print %1 : tensor<4x4xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 16 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[0] : ( 0, 4 // CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12, 16 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 388.76, 425.56, 462.36, 499.16, 397.12, 434.72, 472.32, 509.92, 405.48, 443.88, 482.28, 520.68, 413.84, 453.04, 492.24, 531.44 // CHECK-NEXT: ---- // sparse_tensor.print %2 : tensor<4x4xf64, #DCSR> // // CHECK: {{\[}}[86.08, 94.28, 102.48, 110.68], // CHECK-NEXT: [0, 0, 0, 0], // CHECK-NEXT: [23.46, 25.76, 28.06, 30.36], // CHECK-NEXT: [10.8, 11.8, 12.8, 13.8]] // %u3 = tensor.cast %3 : tensor<4x4xf64> to tensor<*xf64> call @printMemrefF64(%u3) : (tensor<*xf64>) -> () // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 12 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[1] : ( 0, 4, 4, 8, 12 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 86.08, 94.28, 102.48, 110.68, 23.46, 25.76, 28.06, 30.36, 10.8, 11.8, 12.8, 13.8 // CHECK-NEXT: ---- // sparse_tensor.print %4 : tensor<4x4xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 12 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[0] : ( 0, 3 // CHECK-NEXT: crd[0] : ( 0, 2, 3 // CHECK-NEXT: pos[1] : ( 0, 4, 8, 12 // CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 // CHECK-NEXT: values : ( 86.08, 94.28, 102.48, 110.68, 23.46, 25.76, 28.06, 30.36, 10.8, 11.8, 12.8, 13.8 // CHECK-NEXT: ---- // sparse_tensor.print %5 : tensor<4x4xf64, #DCSR> // // CHECK: {{\[}}[0, 30.5, 4.2, 0], // CHECK-NEXT: [0, 0, 0, 0], // CHECK-NEXT: [0, 0, 4.6, 0], // CHECK-NEXT: [0, 0, 7, 8]] // %u6 = tensor.cast %6 : tensor<4x4xf64> to tensor<*xf64> call @printMemrefF64(%u6) : (tensor<*xf64>) -> () // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 5 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[1] : ( 0, 2, 2, 3, 5 // CHECK-NEXT: crd[1] : ( 1, 2, 2, 2, 3 // CHECK-NEXT: values : ( 30.5, 4.2, 4.6, 7, 8 // CHECK-NEXT: ---- // sparse_tensor.print %7 : tensor<4x4xf64, #CSR> // // CHECK: ---- Sparse Tensor ---- // CHECK-NEXT: nse = 5 // CHECK-NEXT: dim = ( 4, 4 ) // CHECK-NEXT: lvl = ( 4, 4 ) // CHECK-NEXT: pos[0] : ( 0, 3 // CHECK-NEXT: crd[0] : ( 0, 2, 3 // CHECK-NEXT: pos[1] : ( 0, 2, 3, 5 // CHECK-NEXT: crd[1] : ( 1, 2, 2, 2, 3 // CHECK-NEXT: values : ( 30.5, 4.2, 4.6, 7, 8 // CHECK-NEXT: ---- // sparse_tensor.print %8 : tensor<4x4xf64, #DCSR> // Release the resources. bufferization.dealloc_tensor %a1 : tensor<4x8xf64, #CSR> bufferization.dealloc_tensor %a2 : tensor<4x8xf64, #DCSR> bufferization.dealloc_tensor %a3 : tensor<4x8xf64, #CSR> bufferization.dealloc_tensor %a4 : tensor<4x8xf64, #DCSR> bufferization.dealloc_tensor %b1 : tensor<8x4xf64, #CSR> bufferization.dealloc_tensor %b2 : tensor<8x4xf64, #DCSR> bufferization.dealloc_tensor %b3 : tensor<8x4xf64, #CSR> bufferization.dealloc_tensor %b4 : tensor<8x4xf64, #DCSR> bufferization.dealloc_tensor %0 : tensor<4x4xf64> bufferization.dealloc_tensor %1 : tensor<4x4xf64, #CSR> bufferization.dealloc_tensor %2 : tensor<4x4xf64, #DCSR> bufferization.dealloc_tensor %3 : tensor<4x4xf64> bufferization.dealloc_tensor %4 : tensor<4x4xf64, #CSR> bufferization.dealloc_tensor %5 : tensor<4x4xf64, #DCSR> bufferization.dealloc_tensor %6 : tensor<4x4xf64> bufferization.dealloc_tensor %7 : tensor<4x4xf64, #CSR> bufferization.dealloc_tensor %8 : tensor<4x4xf64, #DCSR> return } }