# Copyright 2021 The HuggingFace 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 math
from os.path import exists
from os.path import join as pjoin
import plotly.graph_objects as go
import torch
import transformers
from datasets import load_from_disk
from plotly.io import read_json
from tqdm import tqdm
from .dataset_utils import EMBEDDING_FIELD
def sentence_mean_pooling(model_output, attention_mask):
"""Mean pooling of token embeddings for a sentence."""
token_embeddings = model_output[
0
] # First element of model_output contains all token embeddings
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
input_mask_expanded.sum(1), min=1e-9
)
class Embeddings:
def __init__(
self,
dstats=None,
text_dset=None,
text_field_name="text",
cache_path="",
use_cache=False,
):
"""Item embeddings and clustering"""
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
self.model_name = "sentence-transformers/all-mpnet-base-v2"
self.tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_name)
self.model = transformers.AutoModel.from_pretrained(self.model_name).to(
self.device
)
self.text_dset = text_dset if dstats is None else dstats.text_dset
self.text_field_name = (
text_field_name if dstats is None else dstats.our_text_field
)
self.cache_path = cache_path if dstats is None else dstats.cache_path
self.embeddings_dset_fid = pjoin(self.cache_path, "embeddings_dset")
self.embeddings_dset = None
self.node_list_fid = pjoin(self.cache_path, "node_list.th")
self.node_list = None
self.nid_map = None
self.fig_tree_fid = pjoin(self.cache_path, "node_figure.json")
self.fig_tree = None
self.cached_clusters = {}
self.use_cache = use_cache
def compute_sentence_embeddings(self, sentences):
"""
Takes a list of sentences and computes their embeddings
using self.tokenizer and self.model (with output dimension D)
followed by mean pooling of the token representations and normalization
Args:
sentences ([string]): list of N input sentences
Returns:
torch.Tensor: sentence embeddings, dimension NxD
"""
batch = self.tokenizer(
sentences, padding=True, truncation=True, return_tensors="pt"
)
batch = {k: v.to(self.device) for k, v in batch.items()}
with torch.no_grad():
model_output = self.model(**batch)
sentence_embeds = sentence_mean_pooling(
model_output, batch["attention_mask"]
)
sentence_embeds /= sentence_embeds.norm(dim=-1, keepdim=True)
return sentence_embeds
def make_embeddings(self):
"""
Batch computes the embeddings of the Dataset self.text_dset,
using the field self.text_field_name as input.
Returns:
Dataset: HF dataset object with a single EMBEDDING_FIELD field
corresponding to the embeddings (list of floats)
"""
def batch_embed_sentences(sentences):
return {
EMBEDDING_FIELD: [
embed.tolist()
for embed in self.compute_sentence_embeddings(
sentences[self.text_field_name]
)
]
}
self.embeddings_dset = self.text_dset.map(
batch_embed_sentences,
batched=True,
batch_size=32,
remove_columns=[self.text_field_name],
)
return self.embeddings_dset
def make_text_embeddings(self):
"""Load embeddings dataset from cache or compute it."""
if self.use_cache and exists(self.embeddings_dset_fid):
self.embeddings_dset = load_from_disk(self.embeddings_dset_fid)
else:
self.embeddings_dset = self.make_embeddings()
self.embeddings_dset.save_to_disk(self.embeddings_dset_fid)
def make_hierarchical_clustering(
self,
batch_size=1000,
approx_neighbors=1000,
min_cluster_size=10,
):
if self.use_cache and exists(self.node_list_fid):
self.node_list, self.nid_map = torch.load(self.node_list_fid)
else:
self.make_text_embeddings()
embeddings = torch.Tensor(self.embeddings_dset[EMBEDDING_FIELD])
self.node_list = fast_cluster(
embeddings, batch_size, approx_neighbors, min_cluster_size
)
self.nid_map = dict(
[(node["nid"], nid) for nid, node in enumerate(self.node_list)]
)
torch.save((self.node_list, self.nid_map), self.node_list_fid)
print(exists(self.fig_tree_fid), self.fig_tree_fid)
if self.use_cache and exists(self.fig_tree_fid):
self.fig_tree = read_json(self.fig_tree_fid)
else:
self.fig_tree = make_tree_plot(
self.node_list, self.nid_map, self.text_dset, self.text_field_name
)
self.fig_tree.write_json(self.fig_tree_fid)
def find_cluster_beam(self, sentence, beam_size=20):
"""
This function finds the `beam_size` leaf clusters that are closest to the
proposed sentence and returns the full path from the root to the cluster
along with the dot product between the sentence embedding and the
cluster centroid
Args:
sentence (string): input sentence for which to find clusters
beam_size (int): this is a beam size algorithm to explore the tree
Returns:
[([int], float)]: list of (path_from_root, score) sorted by score
"""
embed = self.compute_sentence_embeddings([sentence])[0].to("cpu")
active_paths = [([0], torch.dot(embed, self.node_list[0]["centroid"]).item())]
finished_paths = []
children_ids_list = [
[
self.nid_map[nid]
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
for path, score in active_paths
]
while len(active_paths) > 0:
next_ids = sorted(
[
(
beam_id,
nid,
torch.dot(embed, self.node_list[nid]["centroid"]).item(),
)
for beam_id, children_ids in enumerate(children_ids_list)
for nid in children_ids
],
key=lambda x: x[2],
reverse=True,
)[:beam_size]
paths = [
(active_paths[beam_id][0] + [next_id], score)
for beam_id, next_id, score in next_ids
]
active_paths = []
for path, score in paths:
if (
len(
[
nid
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
)
> 0
):
active_paths += [(path, score)]
else:
finished_paths += [(path, score)]
children_ids_list = [
[
self.nid_map[nid]
for nid in self.node_list[path[-1]]["children_ids"]
if nid in self.nid_map
]
for path, score in active_paths
]
return sorted(
finished_paths,
key=lambda x: x[-1],
reverse=True,
)[:beam_size]
def prepare_merges(embeddings, batch_size=1000, approx_neighbors=1000, low_thres=0.5):
"""
Prepares an initial list of merges for hierarchical
clustering. First compute the `approx_neighbors` nearest neighbors,
then propose a merge for any two points that are closer than `low_thres`
Note that if a point has more than `approx_neighbors` neighbors
closer than `low_thres`, this approach will miss some of those merges
Args:
embeddings (toch.Tensor): Tensor of sentence embeddings - dimension NxD
batch_size (int): compute nearest neighbors of `batch_size` points at a time
approx_neighbors (int): only keep `approx_neighbors` nearest neighbors of a point
low_thres (float): only return merges where the dot product is greater than `low_thres`
Returns:
torch.LongTensor: proposed merges ([i, j] with i>j) - dimension: Mx2
torch.Tensor: merge scores - dimension M
"""
top_idx_pre = torch.cat(
[torch.LongTensor(range(embeddings.shape[0]))[:, None]] * batch_size, dim=1
)
top_val_all = torch.Tensor(0, approx_neighbors)
top_idx_all = torch.LongTensor(0, approx_neighbors)
n_batches = math.ceil(len(embeddings) / batch_size)
for b in tqdm(range(n_batches)):
# TODO: batch across second dimension
cos_scores = torch.mm(
embeddings[b * batch_size : (b + 1) * batch_size], embeddings.t()
)
for i in range(cos_scores.shape[0]):
cos_scores[i, (b * batch_size) + i :] = -1
top_val_large, top_idx_large = cos_scores.topk(
k=approx_neighbors, dim=-1, largest=True
)
top_val_all = torch.cat([top_val_all, top_val_large], dim=0)
top_idx_all = torch.cat([top_idx_all, top_idx_large], dim=0)
max_neighbor_dist = top_val_large[:, -1].max().item()
if max_neighbor_dist > low_thres:
print(
f"WARNING: with the current set of neireast neighbor, the farthest is {max_neighbor_dist}"
)
all_merges = torch.cat(
[
top_idx_pre[top_val_all > low_thres][:, None],
top_idx_all[top_val_all > low_thres][:, None],
],
dim=1,
)
all_merge_scores = top_val_all[top_val_all > low_thres]
return (all_merges, all_merge_scores)
def merge_nodes(nodes, current_thres, previous_thres, all_merges, all_merge_scores):
"""
Merge all nodes if the max dot product between any of their descendants
is greater than current_thres.
Args:
nodes ([dict]): list of dicts representing the current set of nodes
current_thres (float): merge all nodes closer than current_thres
previous_thres (float): nodes closer than previous_thres are already merged
all_merges (torch.LongTensor): proposed merges ([i, j] with i>j) - dimension: Mx2
all_merge_scores (torch.Tensor): merge scores - dimension M
Returns:
[dict]: extended list with the newly created internal nodes
"""
merge_ids = (all_merge_scores <= previous_thres) * (
all_merge_scores > current_thres
)
if merge_ids.sum().item() > 0:
merges = all_merges[merge_ids]
for a, b in merges.tolist():
node_a = nodes[a]
while node_a["parent_id"] != -1:
node_a = nodes[node_a["parent_id"]]
node_b = nodes[b]
while node_b["parent_id"] != -1:
node_b = nodes[node_b["parent_id"]]
if node_a["nid"] == node_b["nid"]:
continue
else:
# merge if threshold allows
if (node_a["depth"] + node_b["depth"]) > 0 and min(
node_a["merge_threshold"], node_b["merge_threshold"]
) == current_thres:
merge_to = None
merge_from = None
if node_a["nid"] < node_b["nid"]:
merge_from = node_a
merge_to = node_b
if node_a["nid"] > node_b["nid"]:
merge_from = node_b
merge_to = node_a
merge_to["depth"] = max(merge_to["depth"], merge_from["depth"])
merge_to["weight"] += merge_from["weight"]
merge_to["children_ids"] += (
merge_from["children_ids"]
if merge_from["depth"] > 0
else [merge_from["nid"]]
)
for cid in merge_from["children_ids"]:
nodes[cid]["parent_id"] = merge_to["nid"]
merge_from["parent_id"] = merge_to["nid"]
# else new node
else:
new_nid = len(nodes)
new_node = {
"nid": new_nid,
"parent_id": -1,
"depth": max(node_a["depth"], node_b["depth"]) + 1,
"weight": node_a["weight"] + node_b["weight"],
"children": [],
"children_ids": [node_a["nid"], node_b["nid"]],
"example_ids": [],
"merge_threshold": current_thres,
}
node_a["parent_id"] = new_nid
node_b["parent_id"] = new_nid
nodes += [new_node]
return nodes
def finalize_node(node, nodes, min_cluster_size):
"""Post-process nodes to sort children by descending weight,
get full list of leaves in the sub-tree, and direct links
to the cildren nodes, then recurses to all children.
Nodes with fewer than `min_cluster_size` descendants are collapsed
into a single leaf.
"""
node["children"] = sorted(
[
finalize_node(nodes[cid], nodes, min_cluster_size)
for cid in node["children_ids"]
],
key=lambda x: x["weight"],
reverse=True,
)
if node["depth"] > 0:
node["example_ids"] = [
eid for child in node["children"] for eid in child["example_ids"]
]
node["children"] = [
child for child in node["children"] if child["weight"] >= min_cluster_size
]
assert node["weight"] == len(node["example_ids"]), print(node)
return node
def fast_cluster(
embeddings,
batch_size=1000,
approx_neighbors=1000,
min_cluster_size=10,
low_thres=0.5,
):
"""
Computes an approximate hierarchical clustering based on example
embeddings. The join criterion is min clustering, i.e. two clusters
are joined if any pair of their descendants are closer than a threshold
The approximate comes from the fact that only the `approx_neighbors` nearest
neighbors of an example are considered for merges
"""
batch_size = min(embeddings.shape[0], batch_size)
all_merges, all_merge_scores = prepare_merges(
embeddings, batch_size, approx_neighbors, low_thres
)
# prepare leaves
nodes = [
{
"nid": nid,
"parent_id": -1,
"depth": 0,
"weight": 1,
"children": [],
"children_ids": [],
"example_ids": [nid],
"merge_threshold": 1.0,
}
for nid in range(embeddings.shape[0])
]
# one level per threshold range
for i in range(10):
p_thres = 1 - i * 0.05
c_thres = 0.95 - i * 0.05
nodes = merge_nodes(nodes, c_thres, p_thres, all_merges, all_merge_scores)
# make root
root_children = [
node
for node in nodes
if node["parent_id"] == -1 and node["weight"] >= min_cluster_size
]
root = {
"nid": len(nodes),
"parent_id": -1,
"depth": max([node["depth"] for node in root_children]) + 1,
"weight": sum([node["weight"] for node in root_children]),
"children": [],
"children_ids": [node["nid"] for node in root_children],
"example_ids": [],
"merge_threshold": -1.0,
}
nodes += [root]
for node in root_children:
node["parent_id"] = root["nid"]
# finalize tree
tree = finalize_node(root, nodes, min_cluster_size)
node_list = []
def rec_map_nodes(node, node_list):
node_list += [node]
for child in node["children"]:
rec_map_nodes(child, node_list)
rec_map_nodes(tree, node_list)
# get centroids and distances
for node in node_list:
node_embeds = embeddings[node["example_ids"]]
node["centroid"] = node_embeds.sum(dim=0)
node["centroid"] /= node["centroid"].norm()
node["centroid_dot_prods"] = torch.mv(node_embeds, node["centroid"])
node["sorted_examples_centroid"] = sorted(
[
(eid, edp.item())
for eid, edp in zip(node["example_ids"], node["centroid_dot_prods"])
],
key=lambda x: x[1],
reverse=True,
)
return node_list
def make_tree_plot(node_list, nid_map, text_dset, text_field_name):
"""
Makes a graphical representation of the tree encoded
in node-list. The hover label for each node shows the number
of descendants and the 5 examples that are closest to the centroid
"""
for nid, node in enumerate(node_list):
# get list of
node_examples = {}
for sid, score in node["sorted_examples_centroid"]:
node_examples[text_dset[sid][text_field_name]] = score
if len(node_examples) >= 5:
break
node["label"] = node.get(
"label",
f"{nid:2d} - {node['weight']:5d} items <br>"
+ "<br>".join(
[
f" {score:.2f} > {txt[:64]}" + ("..." if len(txt) >= 63 else "")
for txt, score in node_examples.items()
]
),
)
# make plot nodes
labels = [node["label"] for node in node_list]
root = node_list[0]
root["X"] = 0
root["Y"] = 0
def rec_make_coordinates(node):
total_weight = 0
add_weight = len(node["example_ids"]) - sum(
[child["weight"] for child in node["children"]]
)
for child in node["children"]:
child["X"] = node["X"] + total_weight
child["Y"] = node["Y"] - 1
total_weight += child["weight"] + add_weight / len(node["children"])
rec_make_coordinates(child)
rec_make_coordinates(root)
E = [] # list of edges
Xn = []
Yn = []
Xe = []
Ye = []
for nid, node in enumerate(node_list):
Xn += [node["X"]]
Yn += [node["Y"]]
for child in node["children"]:
E += [(nid, nid_map[child["nid"]])]
Xe += [node["X"], child["X"], None]
Ye += [node["Y"], child["Y"], None]
# make figure
fig = go.Figure()
fig.add_trace(
go.Scatter(
x=Xe,
y=Ye,
mode="lines",
line=dict(color="rgb(210,210,210)", width=1),
hoverinfo="none",
)
)
fig.add_trace(
go.Scatter(
x=Xn,
y=Yn,
mode="markers",
name="nodes",
marker=dict(
symbol="circle-dot",
size=18,
color="#6175c1",
line=dict(color="rgb(50,50,50)", width=1)
# '#DB4551',
),
text=labels,
hoverinfo="text",
opacity=0.8,
)
)
return fig