Spaces:
Running
Running
| # Copyright 2022 The HuggingFace Team. All rights reserved. | |
| # | |
| # 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. | |
| import random | |
| from typing import List, Optional, Union | |
| import numpy as np | |
| import torch | |
| from ..state import AcceleratorState | |
| from .constants import CUDA_DISTRIBUTED_TYPES | |
| from .dataclasses import DistributedType, RNGType | |
| from .imports import is_mlu_available, is_npu_available, is_torch_xla_available, is_xpu_available | |
| if is_torch_xla_available(): | |
| import torch_xla.core.xla_model as xm | |
| def set_seed(seed: int, device_specific: bool = False, deterministic: bool = False): | |
| """ | |
| Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`. | |
| Args: | |
| seed (`int`): | |
| The seed to set. | |
| device_specific (`bool`, *optional*, defaults to `False`): | |
| Whether to differ the seed on each device slightly with `self.process_index`. | |
| deterministic (`bool`, *optional*, defaults to `False`): | |
| Whether to use deterministic algorithms where available. Can slow down training. | |
| """ | |
| if device_specific: | |
| seed += AcceleratorState().process_index | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| if is_xpu_available(): | |
| torch.xpu.manual_seed_all(seed) | |
| elif is_npu_available(): | |
| torch.npu.manual_seed_all(seed) | |
| elif is_mlu_available(): | |
| torch.mlu.manual_seed_all(seed) | |
| else: | |
| torch.cuda.manual_seed_all(seed) | |
| # ^^ safe to call this function even if cuda is not available | |
| if is_torch_xla_available(): | |
| xm.set_rng_state(seed) | |
| if deterministic: | |
| torch.use_deterministic_algorithms(True) | |
| def synchronize_rng_state(rng_type: Optional[RNGType] = None, generator: Optional[torch.Generator] = None): | |
| # Get the proper rng state | |
| if rng_type == RNGType.TORCH: | |
| rng_state = torch.get_rng_state() | |
| elif rng_type == RNGType.CUDA: | |
| rng_state = torch.cuda.get_rng_state() | |
| elif rng_type == RNGType.XLA: | |
| assert is_torch_xla_available(), "Can't synchronize XLA seeds as torch_xla is unavailable." | |
| rng_state = torch.tensor(xm.get_rng_state()) | |
| elif rng_type == RNGType.NPU: | |
| assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs." | |
| rng_state = torch.npu.get_rng_state() | |
| elif rng_type == RNGType.MLU: | |
| assert is_mlu_available(), "Can't synchronize MLU seeds on an environment without MLUs." | |
| rng_state = torch.mlu.get_rng_state() | |
| elif rng_type == RNGType.XPU: | |
| assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs." | |
| rng_state = torch.xpu.get_rng_state() | |
| elif rng_type == RNGType.GENERATOR: | |
| assert generator is not None, "Need a generator to synchronize its seed." | |
| rng_state = generator.get_state() | |
| # Broadcast the rng state from device 0 to other devices | |
| state = AcceleratorState() | |
| if state.distributed_type == DistributedType.XLA: | |
| rng_state = rng_state.to(xm.xla_device()) | |
| xm.collective_broadcast([rng_state]) | |
| xm.mark_step() | |
| rng_state = rng_state.cpu() | |
| elif ( | |
| state.distributed_type in CUDA_DISTRIBUTED_TYPES | |
| or state.distributed_type == DistributedType.MULTI_MLU | |
| or state.distributed_type == DistributedType.MULTI_NPU | |
| or state.distributed_type == DistributedType.MULTI_XPU | |
| ): | |
| rng_state = rng_state.to(state.device) | |
| torch.distributed.broadcast(rng_state, 0) | |
| rng_state = rng_state.cpu() | |
| elif state.distributed_type == DistributedType.MULTI_CPU: | |
| torch.distributed.broadcast(rng_state, 0) | |
| # Set the broadcast rng state | |
| if rng_type == RNGType.TORCH: | |
| torch.set_rng_state(rng_state) | |
| elif rng_type == RNGType.CUDA: | |
| torch.cuda.set_rng_state(rng_state) | |
| elif rng_type == RNGType.NPU: | |
| torch.npu.set_rng_state(rng_state) | |
| elif rng_type == RNGType.XPU: | |
| torch.xpu.set_rng_state(rng_state) | |
| elif rng_type == RNGType.XLA: | |
| xm.set_rng_state(rng_state.item()) | |
| elif rng_type == RNGType.GENERATOR: | |
| generator.set_state(rng_state) | |
| def synchronize_rng_states(rng_types: List[Union[str, RNGType]], generator: Optional[torch.Generator] = None): | |
| for rng_type in rng_types: | |
| synchronize_rng_state(RNGType(rng_type), generator=generator) | |