185 lines
6.3 KiB
Python
185 lines
6.3 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 random
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from typing import Iterator, List, Optional, Union
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import torch
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from torch.utils.data import Sampler
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class EpisodeAwareSampler:
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def __init__(
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self,
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episode_data_index: dict,
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episode_indices_to_use: Union[list, None] = None,
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drop_n_first_frames: int = 0,
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drop_n_last_frames: int = 0,
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shuffle: bool = False,
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):
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"""Sampler that optionally incorporates episode boundary information.
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Args:
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episode_data_index: Dictionary with keys 'from' and 'to' containing the start and end indices of each episode.
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episode_indices_to_use: List of episode indices to use. If None, all episodes are used.
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Assumes that episodes are indexed from 0 to N-1.
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drop_n_first_frames: Number of frames to drop from the start of each episode.
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drop_n_last_frames: Number of frames to drop from the end of each episode.
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shuffle: Whether to shuffle the indices.
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"""
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indices = []
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for episode_idx, (start_index, end_index) in enumerate(
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zip(episode_data_index["from"], episode_data_index["to"], strict=True)
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):
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if episode_indices_to_use is None or episode_idx in episode_indices_to_use:
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indices.extend(
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range(start_index.item() + drop_n_first_frames, end_index.item() - drop_n_last_frames)
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)
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self.indices = indices
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self.shuffle = shuffle
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def __iter__(self) -> Iterator[int]:
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if self.shuffle:
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for i in torch.randperm(len(self.indices)):
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yield self.indices[i]
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else:
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for i in self.indices:
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yield i
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def __len__(self) -> int:
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return len(self.indices)
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class SumTree:
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"""
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A classic sum-tree data structure for storing priorities.
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Each leaf stores a sample's priority, and internal nodes store sums of children.
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"""
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def __init__(self, capacity: int):
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"""
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Args:
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capacity: Maximum number of elements.
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"""
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self.capacity = capacity
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self.size = capacity
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self.tree = [0.0] * (2 * self.size)
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def initialize_tree(self, priorities: List[float]):
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"""
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Initializes the sum tree
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"""
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# Set leaf values
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for i, priority in enumerate(priorities):
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self.tree[i + self.size] = priority
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# Compute internal node values
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for i in range(self.size - 1, 0, -1):
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self.tree[i] = self.tree[2 * i] + self.tree[2 * i + 1]
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def update(self, idx: int, priority: float):
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"""
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Update the priority at leaf index `idx` and propagate changes upwards.
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"""
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tree_idx = idx + self.size
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self.tree[tree_idx] = priority # Set new priority
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# Propagate up, explicitly summing children
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tree_idx //= 2
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while tree_idx >= 1:
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self.tree[tree_idx] = self.tree[2 * tree_idx] + self.tree[2 * tree_idx + 1]
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tree_idx //= 2
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def total_priority(self) -> float:
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"""Returns the sum of all priorities (stored at root)."""
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return self.tree[1]
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def sample(self, value: float) -> int:
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"""
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Samples an index where the prefix sum up to that leaf is >= `value`.
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"""
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value = min(max(value, 0), self.total_priority()) # Clamp value
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idx = 1
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while idx < self.size:
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left = 2 * idx
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if self.tree[left] >= value:
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idx = left
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else:
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value -= self.tree[left]
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idx = left + 1
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return idx - self.size # Convert tree index to data index
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class PrioritizedSampler(Sampler[int]):
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"""
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PyTorch Sampler that draws samples in proportion to their priority using a SumTree.
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"""
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def __init__(
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self,
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data_len: int,
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alpha: float = 0.6,
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eps: float = 1e-6,
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num_samples_per_epoch: Optional[int] = None,
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):
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"""
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Args:
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data_len: Total number of samples in the dataset.
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alpha: Exponent for priority scaling. Default is 0.6.
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beta: Used in important sampling (IS). Default is 0.4,
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eps: Small constant to avoid zero priorities.
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num_samples_per_epoch: Number of samples per epoch (default is data_len).
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"""
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self.data_len = data_len
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self.alpha = alpha
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self.eps = eps
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self.num_samples_per_epoch = num_samples_per_epoch or data_len
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# Initialize difficulties and sum-tree
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self.difficulties = [1.0] * data_len
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self.priorities = [0.0] * data_len
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initial_priorities = [(1.0 + eps) ** alpha] * data_len
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self.sumtree = SumTree(data_len)
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self.sumtree.initialize_tree(initial_priorities)
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for i, p in enumerate(initial_priorities):
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self.priorities[i] = p
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def update_priorities(self, indices: List[int], difficulties: List[float]):
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"""
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Updates the priorities in the sum-tree.
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"""
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for idx, diff in zip(indices, difficulties, strict=False):
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self.difficulties[idx] = diff
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new_priority = (diff + self.eps) ** self.alpha
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self.priorities[idx] = new_priority
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self.sumtree.update(idx, new_priority)
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def __iter__(self) -> Iterator[int]:
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"""
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Samples indices based on their priority weights.
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"""
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total_p = self.sumtree.total_priority()
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for _ in range(self.num_samples_per_epoch):
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r = random.random() * total_p
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idx = self.sumtree.sample(r)
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yield idx
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def __len__(self) -> int:
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return self.num_samples_per_epoch
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