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| # Copyright 2022 The T5X Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Adafactor logical rules for Mixture of Experts models.""" | |
| from flax import core as flax_core | |
| from t5x import adafactor | |
| FactorDim = adafactor.FactorDim | |
| FrozenDict = flax_core.FrozenDict | |
| def logical_factor_rules() -> FrozenDict: | |
| """Logical factor rules for Mixture of Experts.""" | |
| rules = flax_core.unfreeze(adafactor.standard_logical_factor_rules()) | |
| rules.update({ | |
| 'expert': FactorDim.BATCH, | |
| 'expert_mlp': FactorDim.COLUMN, | |
| 'unmodeled': FactorDim.NONE | |
| }) | |
| return flax_core.freeze(rules) | |