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