#
# Copyright (c) 2017 Intel Corporation
#
# 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 os
import pickle
import numpy as np
try:
import annoy
from annoy import AnnoyIndex
except ImportError:
from rl_coach.logger import failed_imports
failed_imports.append("annoy")
class AnnoyDictionary(object):
def __init__(self, dict_size, key_width, new_value_shift_coefficient=0.1, batch_size=100, key_error_threshold=0.01,
num_neighbors=50, override_existing_keys=True, rebuild_on_every_update=False):
self.rebuild_on_every_update = rebuild_on_every_update
self.max_size = dict_size
self.curr_size = 0
self.new_value_shift_coefficient = new_value_shift_coefficient
self.num_neighbors = num_neighbors
self.override_existing_keys = override_existing_keys
self.index = AnnoyIndex(key_width, metric='euclidean')
self.index.set_seed(1)
self.embeddings = np.zeros((dict_size, key_width))
self.values = np.zeros(dict_size)
self.additional_data = [None] * dict_size
self.lru_timestamps = np.zeros(dict_size)
self.current_timestamp = 0.0
# keys that are in this distance will be considered as the same key
self.key_error_threshold = key_error_threshold
self.initial_update_size = batch_size
self.min_update_size = self.initial_update_size
self.key_dimension = key_width
self.value_dimension = 1
self._reset_buffer()
self.built_capacity = 0
def add(self, keys, values, additional_data=None, force_rebuild_tree=False):
if not additional_data:
additional_data = [None] * len(keys)
# Adds new embeddings and values to the dictionary
indices = []
indices_to_remove = []
for i in range(keys.shape[0]):
index = self._lookup_key_index(keys[i])
if index and self.override_existing_keys:
# update existing value
self.values[index] += self.new_value_shift_coefficient * (values[i] - self.values[index])
self.additional_data[index[0][0]] = additional_data[i]
self.lru_timestamps[index] = self.current_timestamp
indices_to_remove.append(i)
else:
# add new
if self.curr_size >= self.max_size:
# find the LRU entry
index = np.argmin(self.lru_timestamps)
else:
index = self.curr_size
self.curr_size += 1
self.lru_timestamps[index] = self.current_timestamp
indices.append(index)
for i in reversed(indices_to_remove):
keys = np.delete(keys, i, 0)
values = np.delete(values, i, 0)
del additional_data[i]
self.buffered_keys = np.vstack((self.buffered_keys, keys))
self.buffered_values = np.vstack((self.buffered_values, values))
self.buffered_indices = self.buffered_indices + indices
self.buffered_additional_data = self.buffered_additional_data + additional_data
if len(self.buffered_indices) >= self.min_update_size:
self.min_update_size = max(self.initial_update_size, int(self.curr_size * 0.02))
self._rebuild_index()
elif force_rebuild_tree or self.rebuild_on_every_update:
self._rebuild_index()
self.current_timestamp += 1
# Returns the stored embeddings and values of the closest embeddings
def query(self, keys, k):
if not self.has_enough_entries(k):
# this will only happen when the DND is not yet populated with enough entries, which is only during heatup
# these values won't be used and therefore they are meaningless
return [0.0], [0.0], [0], [None]
_, indices = self._get_k_nearest_neighbors_indices(keys, k)
embeddings = []
values = []
additional_data = []
for ind in indices:
self.lru_timestamps[ind] = self.current_timestamp
embeddings.append(self.embeddings[ind])
values.append(self.values[ind])
curr_additional_data = []
for sub_ind in ind:
curr_additional_data.append(self.additional_data[sub_ind])
additional_data.append(curr_additional_data)
self.current_timestamp += 1
return embeddings, values, indices, additional_data
def has_enough_entries(self, k):
return self.curr_size > k and (self.built_capacity > k)
def sample_embeddings(self, num_embeddings):
return self.embeddings[np.random.choice(self.curr_size, num_embeddings)]
def _get_k_nearest_neighbors_indices(self, keys, k):
distances = []
indices = []
for key in keys:
index, distance = self.index.get_nns_by_vector(key, k, include_distances=True)
distances.append(distance)
indices.append(index)
return distances, indices
def _rebuild_index(self):
self.index.unbuild()
self.embeddings[self.buffered_indices] = self.buffered_keys
self.values[self.buffered_indices] = np.squeeze(self.buffered_values)
for i, data in zip(self.buffered_indices, self.buffered_additional_data):
self.additional_data[i] = data
for idx, key in zip(self.buffered_indices, self.buffered_keys):
self.index.add_item(idx, key)
self._reset_buffer()
self.index.build(self.num_neighbors)
self.built_capacity = self.curr_size
def _reset_buffer(self):
self.buffered_keys = np.zeros((0, self.key_dimension))
self.buffered_values = np.zeros((0, self.value_dimension))
self.buffered_indices = []
self.buffered_additional_data = []
def _lookup_key_index(self, key):
distance, index = self._get_k_nearest_neighbors_indices([key], 1)
if distance != [[]] and distance[0][0] <= self.key_error_threshold:
return index
return None
[docs]class QDND(object):
def __init__(self, dict_size, key_width, num_actions, new_value_shift_coefficient=0.1, key_error_threshold=0.01,
learning_rate=0.01, num_neighbors=50, return_additional_data=False, override_existing_keys=False,
rebuild_on_every_update=False):
self.dict_size = dict_size
self.key_width = key_width
self.num_actions = num_actions
self.new_value_shift_coefficient = new_value_shift_coefficient
self.key_error_threshold = key_error_threshold
self.learning_rate = learning_rate
self.num_neighbors = num_neighbors
self.return_additional_data = return_additional_data
self.override_existing_keys = override_existing_keys
self.dicts = []
# create a dict for each action
for a in range(num_actions):
new_dict = AnnoyDictionary(dict_size, key_width, new_value_shift_coefficient,
key_error_threshold=key_error_threshold, num_neighbors=num_neighbors,
override_existing_keys=override_existing_keys,
rebuild_on_every_update=rebuild_on_every_update)
self.dicts.append(new_dict)
def add(self, embeddings, actions, values, additional_data=None):
# add a new set of embeddings and values to each of the underlining dictionaries
embeddings = np.array(embeddings)
actions = np.array(actions)
values = np.array(values)
for a in range(self.num_actions):
idx = np.where(actions == a)
curr_action_embeddings = embeddings[idx]
curr_action_values = np.expand_dims(values[idx], -1)
if additional_data:
curr_additional_data = []
for i in idx[0]:
curr_additional_data.append(additional_data[i])
else:
curr_additional_data = None
self.dicts[a].add(curr_action_embeddings, curr_action_values, curr_additional_data)
return True
def query(self, embeddings, action, k):
# query for nearest neighbors to the given embeddings
dnd_embeddings = []
dnd_values = []
dnd_indices = []
dnd_additional_data = []
for i in range(len(embeddings)):
embedding, value, indices, additional_data = self.dicts[action].query([embeddings[i]], k)
dnd_embeddings.append(embedding[0])
dnd_values.append(value[0])
dnd_indices.append(indices[0])
dnd_additional_data.append(additional_data[0])
if self.return_additional_data:
return dnd_embeddings, dnd_values, dnd_indices, dnd_additional_data
else:
return dnd_embeddings, dnd_values, dnd_indices
def has_enough_entries(self, k):
# check if each of the action dictionaries has at least k entries
for a in range(self.num_actions):
if not self.dicts[a].has_enough_entries(k):
return False
return True
def update_keys_and_values(self, actions, key_gradients, value_gradients, indices):
# Update DND keys and values
for batch_action, batch_keys, batch_values, batch_indices in zip(actions, key_gradients, value_gradients, indices):
# Update keys (embeddings) and values in DND
for i, index in enumerate(batch_indices):
self.dicts[batch_action].embeddings[index, :] -= self.learning_rate * batch_keys[i, :]
self.dicts[batch_action].values[index] -= self.learning_rate * batch_values[i]
def sample_embeddings(self, num_embeddings):
num_actions = len(self.dicts)
embeddings = []
num_embeddings_per_action = int(num_embeddings/num_actions)
for action in range(num_actions):
embeddings.append(self.dicts[action].sample_embeddings(num_embeddings_per_action))
embeddings = np.vstack(embeddings)
# the numbers did not divide nicely, let's just randomly sample some more embeddings
if num_embeddings_per_action * num_actions < num_embeddings:
action = np.random.randint(0, num_actions)
extra_embeddings = self.dicts[action].sample_embeddings(num_embeddings -
num_embeddings_per_action * num_actions)
embeddings = np.vstack([embeddings, extra_embeddings])
return embeddings
def clean(self):
# create a new dict for each action
self.dicts = []
for a in range(self.num_actions):
new_dict = AnnoyDictionary(self.dict_size, self.key_width, self.new_value_shift_coefficient,
key_error_threshold=self.key_error_threshold, num_neighbors=self.num_neighbors)
self.dicts.append(new_dict)
def load_dnd(model_dir):
latest_checkpoint_id = -1
latest_checkpoint = ''
# get all checkpoint files
for fname in os.listdir(model_dir):
path = os.path.join(model_dir, fname)
if os.path.isdir(path) or fname.split('.')[-1] != 'srs':
continue
checkpoint_id = int(fname.split('_')[0])
if checkpoint_id > latest_checkpoint_id:
latest_checkpoint = fname
latest_checkpoint_id = checkpoint_id
with open(os.path.join(model_dir, str(latest_checkpoint)), 'rb') as f:
DND = pickle.load(f)
for a in range(DND.num_actions):
DND.dicts[a].index = AnnoyIndex(512, metric='euclidean')
DND.dicts[a].index.set_seed(1)
for idx, key in zip(range(DND.dicts[a].curr_size), DND.dicts[a].embeddings[:DND.dicts[a].curr_size]):
DND.dicts[a].index.add_item(idx, key)
DND.dicts[a].index.build(50)
return DND