| // RUN: mlir-opt %s \ |
| // RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ |
| // 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 \ |
| // RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // 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: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \ |
| // RUN: --sparsification="vectorization-strategy=2 vl=2" --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 \ |
| // RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \ |
| // 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 |
| |
| #DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> |
| |
| // An example of a quantized sparse matmul. With the zero offset for the |
| // sparse input, the sparse compiler generates very efficient code for the |
| // x(i,j) += (ext(a(i,k)) - 2) * ext(b(k,j)) |
| // operation. |
| module { |
| |
| func @quantized_matmul(%input1: tensor<5x3xi8>, |
| %input2: tensor<3x6xi8, #DCSR>, |
| %output: tensor<5x6xi32>) -> tensor<5x6xi32> { |
| %c0 = arith.constant 0 : i32 |
| %c2 = arith.constant 2 : i32 |
| %0 = linalg.quantized_matmul |
| ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32) |
| outs(%output : tensor<5x6xi32>) -> tensor<5x6xi32> |
| return %0: tensor<5x6xi32> |
| } |
| |
| func @entry() { |
| %c0 = arith.constant 0 : index |
| %i0 = arith.constant 0 : i32 |
| |
| %input1 = arith.constant dense<[ |
| [ -128, 3, 127 ], |
| [ 0, 0, 0 ], |
| [ 11, 1, 0 ], |
| [ 0, 5, -1 ], |
| [ 13, 0, 3 ] |
| ]> : tensor<5x3xi8> |
| |
| %input2 = arith.constant dense<[ |
| [ 127, 0, -128, 0, 0, 3 ], |
| [ 0, 0, 0, 0, 0, 0 ], |
| [ 0, 0, 0, 100, 10, 0 ] |
| ]> : tensor<3x6xi8> |
| |
| %sparse_input2 = sparse_tensor.convert %input2 : tensor<3x6xi8> to tensor<3x6xi8, #DCSR> |
| |
| // Call the kernel. |
| %output = arith.constant dense<0> : tensor<5x6xi32> |
| %0 = call @quantized_matmul(%input1, %sparse_input2, %output) |
| : (tensor<5x3xi8>, |
| tensor<3x6xi8, #DCSR>, |
| tensor<5x6xi32>) -> tensor<5x6xi32> |
| |
| // |
| // Verify the output. |
| // |
| // CHECK: ( ( -16510, 0, 16640, 12500, 1250, -390 ), |
| // CHECK-SAME: ( -254, 0, 256, -200, -20, -6 ), |
| // CHECK-SAME: ( 1143, 0, -1152, -200, -20, 27 ), |
| // CHECK-SAME: ( -254, 0, 256, -300, -30, -6 ), |
| // CHECK-SAME: ( 1397, 0, -1408, 100, 10, 33 ) ) |
| // |
| %m = bufferization.to_memref %0 : memref<5x6xi32> |
| %v = vector.transfer_read %m[%c0, %c0], %i0 |
| : memref<5x6xi32>, vector<5x6xi32> |
| vector.print %v : vector<5x6xi32> |
| |
| // Release the resources. |
| sparse_tensor.release %sparse_input2 : tensor<3x6xi8, #DCSR> |
| memref.dealloc %m : memref<5x6xi32> |
| |
| return |
| } |
| } |