| 51 | } |
| 52 | |
| 53 | argument generate_argument(shape s, unsigned long seed, random_mode m) |
| 54 | { |
| 55 | argument result; |
| 56 | if(s.type() == shape::tuple_type) |
| 57 | { |
| 58 | const auto& sub_ss = s.sub_shapes(); |
| 59 | std::vector<argument> sub_args; |
| 60 | std::transform(sub_ss.begin(), sub_ss.end(), std::back_inserter(sub_args), [&](auto ss) { |
| 61 | return generate_argument(ss, seed, m); |
| 62 | }); |
| 63 | |
| 64 | result = argument(sub_args); |
| 65 | } |
| 66 | // special processing for non-computable type |
| 67 | else if(not s.computable()) |
| 68 | { |
| 69 | // NOTE: these values can be wrong (ex. not valid fp4x2) |
| 70 | auto v = generate_tensor_data<uint8_t>(s, seed, m); |
| 71 | result = {s, v}; |
| 72 | } |
| 73 | else |
| 74 | { |
| 75 | s.visit_type([&](auto as) { |
| 76 | // we use char type to store bool type internally, so bool_type |
| 77 | // needs special processing to generate data |
| 78 | if(s.type() == shape::bool_type) |
| 79 | { |
| 80 | auto v = generate_tensor_data<bool>(s, seed, m); |
| 81 | result = {s, v}; |
| 82 | } |
| 83 | else |
| 84 | { |
| 85 | using type = typename decltype(as)::type; |
| 86 | auto v = generate_tensor_data<type>(s, seed, m); |
| 87 | result = {s, v}; |
| 88 | } |
| 89 | }); |
| 90 | } |
| 91 | |
| 92 | return result; |
| 93 | } |
| 94 | |
| 95 | literal generate_literal(shape s, unsigned long seed) |
| 96 | { |