68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. 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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import torch
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from torch import nn
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def populate_queues(queues, batch):
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for key in batch:
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# Ignore keys not in the queues already (leaving the responsibility to the caller to make sure the
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# queues have the keys they want).
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if key not in queues:
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continue
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if len(queues[key]) != queues[key].maxlen:
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# initialize by copying the first observation several times until the queue is full
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while len(queues[key]) != queues[key].maxlen:
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queues[key].append(batch[key])
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else:
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# add latest observation to the queue
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queues[key].append(batch[key])
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return queues
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def get_device_from_parameters(module: nn.Module) -> torch.device:
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"""Get a module's device by checking one of its parameters.
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Note: assumes that all parameters have the same device
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"""
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return next(iter(module.parameters())).device
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def get_dtype_from_parameters(module: nn.Module) -> torch.dtype:
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"""Get a module's parameter dtype by checking one of its parameters.
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Note: assumes that all parameters have the same dtype.
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"""
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return next(iter(module.parameters())).dtype
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def get_output_shape(module: nn.Module, input_shape: tuple) -> tuple:
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"""
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Calculates the output shape of a PyTorch module given an input shape.
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Args:
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module (nn.Module): a PyTorch module
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input_shape (tuple): A tuple representing the input shape, e.g., (batch_size, channels, height, width)
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Returns:
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tuple: The output shape of the module.
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"""
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dummy_input = torch.zeros(size=input_shape)
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with torch.inference_mode():
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output = module(dummy_input)
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return tuple(output.shape)
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