| // Test matmuls of specific sizes using global load lds option for GPU. |
| |
| !A_size = tensor<16x64xi8> |
| !B_size = tensor<64x16xi8> |
| !C_size = tensor<16x16xi32> |
| !out_size = tensor<16x16xi8> |
| |
| func.func @matmul_16x64x16_i8() { |
| %lhs = util.unfoldable_constant dense<1> : !A_size |
| %rhs = util.unfoldable_constant dense<1> : !B_size |
| %cst = arith.constant 0.000000e+00 : f32 |
| %empty = tensor.empty() : !C_size |
| %C = linalg.fill ins(%cst : f32) outs(%empty : !C_size) -> !C_size |
| %0 = linalg.matmul ins(%lhs, %rhs : !A_size, !B_size) |
| outs(%C : !C_size) -> !C_size |
| %E = tensor.empty() : !out_size |
| %1 = linalg.generic { |
| indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>], |
| iterator_types = ["parallel", "parallel"]} |
| ins(%0 : !C_size) outs(%E : !out_size) { |
| ^bb0(%in: i32, %out: i8): |
| %trunc = arith.trunci %in : i32 to i8 |
| linalg.yield %trunc : i8 |
| } -> !out_size |
| check.expect_eq_const(%1, dense<64> : !out_size) : !out_size |
| return |
| } |
