| 105 | |
| 106 | @R.function |
| 107 | def main(x: R.Tensor((2, 4), dtype="float32")) -> R.Tensor((10,), dtype="float32"): |
| 108 | R.func_attr({"relax.force_pure": True}) |
| 109 | cls = Expected |
| 110 | storage: R.Object = R.memory.alloc_storage(R.shape([32]), virtual_device_index=0, storage_scope="global", dtype="float32") |
| 111 | alloc: R.Tensor((2, 4), dtype="float32") = R.memory.alloc_tensor(storage, 0, R.shape([2, 4]), dtype="float32") |
| 112 | _ = cls.exp(x, alloc) |
| 113 | lv: R.Tensor((2, 4), dtype="float32") = alloc |
| 114 | lv1: R.Tensor((8,), dtype="float32") = R.reshape(lv, (8,)) |
| 115 | storage1: R.Object = R.memory.alloc_storage(R.shape([40]), virtual_device_index=0, storage_scope="global", dtype="float32") |
| 116 | alloc1: R.Tensor((8,), dtype="float32") = R.memory.alloc_tensor(storage1, 0, R.shape([8]), dtype="float32") |
| 117 | _ = cls.relu(lv1, alloc1) |
| 118 | lv2: R.Tensor((8,), dtype="float32") = alloc1 |
| 119 | alloc2: R.Tensor((8,), dtype="float32") = R.memory.alloc_tensor(storage, 0, R.shape([8]), dtype="float32") |
| 120 | _ = cls.add(lv2, R.const(1, "float32"), alloc2) |
| 121 | lv3: R.Tensor((8,), dtype="float32") = alloc2 |
| 122 | alloc3: R.Tensor((10,), dtype="float32") = R.memory.alloc_tensor(storage1, 0, R.shape([10]), dtype="float32") |
| 123 | _ = cls.pad(lv3, alloc3) |
| 124 | lv4: R.Tensor((10,), dtype="float32") = alloc3 |
| 125 | alloc4: R.Tensor((10,), dtype="float32") = R.builtin.alloc_tensor(R.shape([10]), dtype="float32", runtime_device_index=0) |
| 126 | _ = cls.log(lv4, alloc4) |
| 127 | gv5: R.Tensor((10,), dtype="float32") = alloc4 |
| 128 | return gv5 |
| 129 | |
| 130 | @I.ir_module |
| 131 | class ExpectedLowered: |