Decompose composite operators that are composed by other operators during training. For example, the result of batch norm (a triple) will be simplified. Attention, tensor_to_shape, etc. can be also decomposed into a number of simplified operators as well. Parameters ---------- f
(func_name: str | None = None)
| 1284 | |
| 1285 | |
| 1286 | def DecomposeOpsForTraining(func_name: str | None = None) -> tvm.ir.transform.Pass: |
| 1287 | """Decompose composite operators that are composed by other operators during training. |
| 1288 | For example, the result of batch norm (a triple) will be simplified. Attention, tensor_to_shape, |
| 1289 | etc. can be also decomposed into a number of simplified operators as well. |
| 1290 | |
| 1291 | Parameters |
| 1292 | ---------- |
| 1293 | func_name: Optional[str] |
| 1294 | The name of the specified function. If not specified, the pass will run in |
| 1295 | all functions. |
| 1296 | |
| 1297 | Returns |
| 1298 | ------- |
| 1299 | ret : tvm.transform.Pass |
| 1300 | The registered pass |
| 1301 | """ |
| 1302 | return _ffi_api.DecomposeOpsForTraining(func_name) # type: ignore |
| 1303 | |
| 1304 | |
| 1305 | def AlterOpImpl( |
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