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modeling.py
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modeling.py
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import copy
import os
from dataclasses import dataclass
from pathlib import Path
import joblib
import numpy as np
import requests
import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
from sentence_transformers import InputExample, SentenceTransformer, models
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier
from sklearn.multioutput import ClassifierChain, MultiOutputClassifier
from . import logging
logging.set_verbosity_info()
logger = logging.get_logger(__name__)
MODEL_HEAD_NAME = "model_head.pkl"
class SetFitBaseModel:
def __init__(self, model, max_seq_length: int, add_normalization_layer: bool) -> None:
self.model = SentenceTransformer(model)
self.model_original_state = copy.deepcopy(self.model.state_dict())
self.model.max_seq_length = max_seq_length
if add_normalization_layer:
self.model._modules["2"] = models.Normalize()
@dataclass
class SetFitModel(PyTorchModelHubMixin):
"""A SetFit model with integration to the Hugging Face Hub."""
def __init__(self, model_body=None, model_head=None, multi_target_strategy=None):
super(SetFitModel, self).__init__()
self.model_body = model_body
self.model_head = model_head
self.multi_target_strategy = multi_target_strategy
self.model_original_state = copy.deepcopy(self.model_body.state_dict())
def fit(self, x_train, y_train):
embeddings = self.model_body.encode(x_train)
self.model_head.fit(embeddings, y_train)
def predict(self, x_test):
embeddings = self.model_body.encode(x_test)
return self.model_head.predict(embeddings)
def predict_proba(self, x_test):
embeddings = self.model_body.encode(x_test)
return self.model_head.predict_proba(embeddings)
def __call__(self, inputs):
embeddings = self.model_body.encode(inputs)
return self.model_head.predict(embeddings)
def _save_pretrained(self, save_directory):
self.model_body.save(path=save_directory)
joblib.dump(self.model_head, f"{save_directory}/{MODEL_HEAD_NAME}")
@classmethod
def _from_pretrained(
cls,
model_id: str,
revision=None,
cache_dir=None,
force_download=None,
proxies=None,
resume_download=None,
local_files_only=None,
use_auth_token=None,
multi_target_strategy=None,
**model_kwargs,
):
model_body = SentenceTransformer(model_id, cache_folder=cache_dir)
if os.path.isdir(model_id):
if MODEL_HEAD_NAME in os.listdir(model_id):
model_head_file = os.path.join(model_id, MODEL_HEAD_NAME)
else:
logger.info(
f"{MODEL_HEAD_NAME} not found in {Path(model_id).resolve()},"
" initialising classification head with random weights."
" You should TRAIN this model on a downstream task to use it for predictions and inference."
)
model_head_file = None
else:
try:
model_head_file = hf_hub_download(
repo_id=model_id,
filename=MODEL_HEAD_NAME,
revision=revision,
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
use_auth_token=use_auth_token,
local_files_only=local_files_only,
)
except requests.exceptions.RequestException:
logger.info(
f"{MODEL_HEAD_NAME} not found on HuggingFace Hub, initialising classification head with random weights."
" You should TRAIN this model on a downstream task to use it for predictions and inference."
)
model_head_file = None
if model_head_file is not None:
model_head = joblib.load(model_head_file)
else:
if "head_params" in model_kwargs.keys():
clf = LogisticRegression(**model_kwargs["head_params"])
else:
clf = LogisticRegression()
if multi_target_strategy is not None:
if multi_target_strategy == "one-vs-rest":
multilabel_classifier = OneVsRestClassifier(clf)
elif multi_target_strategy == "multi-output":
multilabel_classifier = MultiOutputClassifier(clf)
elif multi_target_strategy == "classifier-chain":
multilabel_classifier = ClassifierChain(clf)
else:
raise ValueError(f"multi_target_strategy {multi_target_strategy} is not supported.")
model_head = multilabel_classifier
else:
model_head = LogisticRegression()
return SetFitModel(model_body=model_body, model_head=model_head, multi_target_strategy=multi_target_strategy)
class SupConLoss(nn.Module):
"""Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
It also supports the unsupervised contrastive loss in SimCLR.
"""
def __init__(self, model, temperature=0.07, contrast_mode="all", base_temperature=0.07):
super(SupConLoss, self).__init__()
self.model = model
self.temperature = temperature
self.contrast_mode = contrast_mode
self.base_temperature = base_temperature
def forward(self, sentence_features, labels=None, mask=None):
"""Computes loss for model.
If both `labels` and `mask` are None, it degenerates to SimCLR unsupervised loss:
https://arxiv.org/pdf/2002.05709.pdf
Args:
features: hidden vector of shape [bsz, n_views, ...].
labels: ground truth of shape [bsz].
mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
has the same class as sample i. Can be asymmetric.
Returns:
A loss scalar.
"""
features = self.model(sentence_features[0])["sentence_embedding"]
# Normalize embeddings
features = torch.nn.functional.normalize(features, p=2, dim=1)
# Add n_views dimension
features = torch.unsqueeze(features, 1)
device = features.device
if len(features.shape) < 3:
raise ValueError("`features` needs to be [bsz, n_views, ...]," "at least 3 dimensions are required")
if len(features.shape) > 3:
features = features.view(features.shape[0], features.shape[1], -1)
batch_size = features.shape[0]
if labels is not None and mask is not None:
raise ValueError("Cannot define both `labels` and `mask`")
elif labels is None and mask is None:
mask = torch.eye(batch_size, dtype=torch.float32).to(device)
elif labels is not None:
labels = labels.contiguous().view(-1, 1)
if labels.shape[0] != batch_size:
raise ValueError("Num of labels does not match num of features")
mask = torch.eq(labels, labels.T).float().to(device)
else:
mask = mask.float().to(device)
contrast_count = features.shape[1]
contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
if self.contrast_mode == "one":
anchor_feature = features[:, 0]
anchor_count = 1
elif self.contrast_mode == "all":
anchor_feature = contrast_feature
anchor_count = contrast_count
else:
raise ValueError("Unknown mode: {}".format(self.contrast_mode))
# Compute logits
anchor_dot_contrast = torch.div(torch.matmul(anchor_feature, contrast_feature.T), self.temperature)
# For numerical stability
logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
logits = anchor_dot_contrast - logits_max.detach()
# Tile mask
mask = mask.repeat(anchor_count, contrast_count)
# Mask-out self-contrast cases
logits_mask = torch.scatter(
torch.ones_like(mask),
1,
torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
0,
)
mask = mask * logits_mask
# Compute log_prob
exp_logits = torch.exp(logits) * logits_mask
log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
# Compute mean of log-likelihood over positive
mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
# Loss
loss = -(self.temperature / self.base_temperature) * mean_log_prob_pos
loss = loss.view(anchor_count, batch_size).mean()
return loss
def sentence_pairs_generation(sentences, labels, pairs):
# Initialize two empty lists to hold the (sentence, sentence) pairs and
# labels to indicate if a pair is positive or negative
num_classes = np.unique(labels)
idx = [np.where(labels == i)[0] for i in num_classes]
for first_idx in range(len(sentences)):
current_sentence = sentences[first_idx]
label = labels[first_idx]
second_idx = np.random.choice(idx[np.where(num_classes == label)[0][0]])
positive_sentence = sentences[second_idx]
# Prepare a positive pair and update the sentences and labels
# lists, respectively
pairs.append(InputExample(texts=[current_sentence, positive_sentence], label=1.0))
negative_idx = np.where(labels != label)[0]
negative_sentence = sentences[np.random.choice(negative_idx)]
# Prepare a negative pair of images and update our lists
pairs.append(InputExample(texts=[current_sentence, negative_sentence], label=0.0))
# Return a 2-tuple of our image pairs and labels
return pairs
def sentence_pairs_generation_multilabel(sentences, labels, pairs):
# Initialize two empty lists to hold the (sentence, sentence) pairs and
# labels to indicate if a pair is positive or negative
for first_idx in range(len(sentences)):
current_sentence = sentences[first_idx]
sample_labels = np.where(labels[first_idx, :] == 1)[0]
if len(np.where(labels.dot(labels[first_idx, :].T) == 0)[0]) == 0:
continue
else:
for _label in sample_labels:
second_idx = np.random.choice(np.where(labels[:, _label] == 1)[0])
positive_sentence = sentences[second_idx]
# Prepare a positive pair and update the sentences and labels
# lists, respectively
pairs.append(InputExample(texts=[current_sentence, positive_sentence], label=1.0))
# Search for sample that don't have a label in common with current
# sentence
negative_idx = np.where(labels.dot(labels[first_idx, :].T) == 0)[0]
negative_sentence = sentences[np.random.choice(negative_idx)]
# Prepare a negative pair of images and update our lists
pairs.append(InputExample(texts=[current_sentence, negative_sentence], label=0.0))
# Return a 2-tuple of our image pairs and labels
return pairs
def sentence_pairs_generation_cos_sim(sentences, pairs, cos_sim_matrix):
# initialize two empty lists to hold the (sentence, sentence) pairs and
# labels to indicate if a pair is positive or negative
idx = list(range(len(sentences)))
for first_idx in range(len(sentences)):
current_sentence = sentences[first_idx]
second_idx = int(np.random.choice([x for x in idx if x != first_idx]))
cos_sim = float(cos_sim_matrix[first_idx][second_idx])
paired_sentence = sentences[second_idx]
pairs.append(InputExample(texts=[current_sentence, paired_sentence], label=cos_sim))
third_idx = np.random.choice([x for x in idx if x != first_idx])
cos_sim = float(cos_sim_matrix[first_idx][third_idx])
paired_sentence = sentences[third_idx]
pairs.append(InputExample(texts=[current_sentence, paired_sentence], label=cos_sim))
return pairs
class SKLearnWrapper:
def __init__(self, st_model=None, clf=None):
self.st_model = st_model
self.clf = clf
def fit(self, x_train, y_train):
embeddings = self.st_model.encode(x_train)
self.clf.fit(embeddings, y_train)
def predict(self, x_test):
embeddings = self.st_model.encode(x_test)
return self.clf.predict(embeddings)
def predict_proba(self, x_test):
embeddings = self.st_model.encode(x_test)
return self.clf.predict_proba(embeddings)
def save(self, path):
self.st_model.save(path=path)
joblib.dump(self.clf, f"{path}/setfit_head.pkl")
def load(self, path):
self.st_model = SentenceTransformer(model_name_or_path=path)
self.clf = joblib.load(f"{path}/setfit_head.pkl")