105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Union
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import numpy as np
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import torch
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SCHEDULER_CONFIG_NAME = "scheduler_config.json"
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class SchedulerMixin:
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config_name = SCHEDULER_CONFIG_NAME
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ignore_for_config = ["tensor_format"]
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def set_format(self, tensor_format="pt"):
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self.tensor_format = tensor_format
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if tensor_format == "pt":
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for key, value in vars(self).items():
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if isinstance(value, np.ndarray):
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setattr(self, key, torch.from_numpy(value))
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return self
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def clip(self, tensor, min_value=None, max_value=None):
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tensor_format = getattr(self, "tensor_format", "pt")
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if tensor_format == "np":
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return np.clip(tensor, min_value, max_value)
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elif tensor_format == "pt":
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return torch.clamp(tensor, min_value, max_value)
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raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
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def log(self, tensor):
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tensor_format = getattr(self, "tensor_format", "pt")
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if tensor_format == "np":
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return np.log(tensor)
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elif tensor_format == "pt":
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return torch.log(tensor)
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raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
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def match_shape(self, values: Union[np.ndarray, torch.Tensor], broadcast_array: Union[np.ndarray, torch.Tensor]):
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"""
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Turns a 1-D array into an array or tensor with len(broadcast_array.shape) dims.
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Args:
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values: an array or tensor of values to extract.
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broadcast_array: an array with a larger shape of K dimensions with the batch
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dimension equal to the length of timesteps.
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Returns:
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a tensor of shape [batch_size, 1, ...] where the shape has K dims.
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"""
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tensor_format = getattr(self, "tensor_format", "pt")
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values = values.flatten()
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while len(values.shape) < len(broadcast_array.shape):
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values = values[..., None]
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if tensor_format == "pt":
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values = values.to(broadcast_array.device)
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return values
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def norm(self, tensor):
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tensor_format = getattr(self, "tensor_format", "pt")
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if tensor_format == "np":
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return np.linalg.norm(tensor)
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elif tensor_format == "pt":
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return torch.norm(tensor.reshape(tensor.shape[0], -1), dim=-1).mean()
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raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
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def randn_like(self, tensor, generator=None):
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tensor_format = getattr(self, "tensor_format", "pt")
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if tensor_format == "np":
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return np.random.randn(*np.shape(tensor))
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elif tensor_format == "pt":
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# return torch.randn_like(tensor)
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return torch.randn(tensor.shape, layout=tensor.layout, generator=generator).to(tensor.device)
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raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
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def zeros_like(self, tensor):
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tensor_format = getattr(self, "tensor_format", "pt")
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if tensor_format == "np":
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return np.zeros_like(tensor)
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elif tensor_format == "pt":
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return torch.zeros_like(tensor)
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raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
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