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import torch
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
class EnglishScoringModel(torch.nn.Module):
def __init__(self, model, tokenizer, device):
super().__init__()
self.device = device
self.model = model.to(self.device)
self._tokenizer = tokenizer
@staticmethod
def load(
model_path: str, type: str, state_dict_path: str = None, device="cpu"
) -> "EnglishScoringModel":
"""
Load the model from the given path and return the model instance.
Args:
model_path (str): The path to the model.
type (str): The type of the model. It should be either 'biencoder' or 'crossencoder'.
state_dict_path (str): The path to the state dict. Default is None.
device (str): The device to use. Default is 'cpu'.
Returns:
EnglishScoringModel: The model instance.
"""
tokenizer = AutoTokenizer.from_pretrained(model_path)
if type == "biencoder":
model = AutoModel.from_pretrained(model_path)
competence_model = BiEncoder(model, tokenizer, device)
elif type == "crossencoder":
model = AutoModelForSequenceClassification.from_pretrained(model_path)
competence_model = CrossEncoder(model, tokenizer, device)
else:
raise NotImplementedError(
"Model type is only implemented for biencoder and crossencoder"
)
if state_dict_path:
competence_model.load_state_dict(torch.load(state_dict_path), strict=False)
return competence_model
def save_state_dict(self, state_dict_path: str) -> None:
torch.save(self.state_dict(), state_dict_path)
def tokenizer(self, *args, **kwargs):
"""
Tokenize the given arguments and return the tokenized tensors.
Default options are padding=True, truncation=True, and return_tensors='pt'.
"""
kwargs.setdefault("padding", True)
kwargs.setdefault("truncation", True)
kwargs.setdefault("return_tensors", "pt")
return self._tokenizer(*args, **kwargs).to(self.device)
def forward(self, *args, type: str = "set", **kwargs) -> torch.Tensor:
"""
Forward pass of the model.
Forward type should be either 'single' or 'set'.
Args:
type (str): The type of the forward pass. Default is 'set'.
"""
if type == "single":
return self.forward_single(*args, **kwargs)
elif type == "set":
return self.forward_set(*args, **kwargs)
else:
raise ValueError("Forward type should be either 'single' or 'set'.")
def forward_single(
self, transcripts: list[str], competences: list[str], **kwargs
) -> torch.Tensor:
"""
Forward pass of the model for each transcript and competence pair.
Args:
transcripts (list[str]): The list of transcripts.
competences (list[str]): The list of competences.
Returns:
torch.Tensor: The predicted probabilities from each pair.
"""
assert len(transcripts) == len(competences)
raise NotImplementedError
def forward_set(
self, transcripts: list[str], competence_sets: list[list[str]], **kwargs
) -> torch.Tensor:
"""
Forward pass of the model for each transcript and set of competences.
Args:
transcripts (list[str]): The list of transcripts.
competence_sets (list[list[str]]): The list of sets of competences.
Returns:
torch.Tensor: The predicted probabilities from each transcript across the set of competences.
"""
assert len(transcripts) == len(competence_sets)
device = self.device
lc_list = [len(competences) for competences in competence_sets]
max_lc = max(lc_list)
flat_t = [t for i, t in enumerate(transcripts) for _ in range(lc_list[i])]
flat_c = [c for cs in competence_sets for c in cs]
sims = self(flat_t, flat_c, type="single", **kwargs)
mask = torch.tensor(
[[1] * lc + [0] * (max_lc - lc) for lc in lc_list],
device=device,
dtype=torch.bool,
)
padded = torch.full(
(len(lc_list), max_lc), fill_value=float("-inf"), device=device
)
idx = 0
for r, lc in enumerate(lc_list):
padded[r, :lc] = sims[idx : idx + lc]
idx += lc
T = 0.30
tau = 0.30
alpha = 12.0
level_logits = padded / T
with torch.no_grad():
sim_padded = padded.clone()
sim_padded[~mask] = float("-inf")
max_sim, _ = sim_padded.max(dim=1)
max_sim[max_sim == float("-inf")] = -1.0
none_logit = torch.nn.functional.softplus(alpha * (tau - max_sim))
all_logits = torch.zeros((len(lc_list), 1 + max_lc), device=device)
all_logits[:, 0] = none_logit
all_logits[:, 1:] = level_logits
probs = torch.softmax(all_logits, dim=1)
probs[:, 1:][~mask] = 0.0
row_sums = probs.sum(dim=1, keepdim=True).clamp_min(1e-12)
probs = probs / row_sums
return probs
class BiEncoder(EnglishScoringModel):
def __init__(self, model, tokenizer, device="cpu"):
super().__init__(model, tokenizer, device)
def forward_single(
self, transcripts: list[str], competences: list[str], tokenizer_padding=True
) -> torch.Tensor:
assert len(transcripts) == len(competences)
features_t = self.tokenizer(transcripts, padding=tokenizer_padding)
features_c = self.tokenizer(competences, padding=tokenizer_padding)
embeddings_t = self.model(**features_t)
embeddings_c = self.model(**features_c)
embeddings_t = self.pooling(embeddings_t, features_t["attention_mask"])
embeddings_c = self.pooling(embeddings_c, features_c["attention_mask"])
prob = torch.nn.functional.cosine_similarity(embeddings_t, embeddings_c, dim=1)
prob = torch.clamp(prob, min=1e-20)
return prob
@staticmethod
def pooling(model_output, attention_mask: torch.Tensor) -> torch.Tensor:
"""
Pool the model output using the attention mask with normalized mean pooling.
Args:
model_output (torch.Tensor): The model output tensor.
attention_mask (torch.Tensor): The attention mask tensor.
Returns:
torch.Tensor: The pooled embeddings.
"""
token_embeddings = model_output[0]
input_mask_expanded = (
attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
)
pooled_embeddings = torch.sum(
token_embeddings * input_mask_expanded, 1
) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
return torch.nn.functional.normalize(pooled_embeddings, p=2, dim=1)
class CrossEncoder(EnglishScoringModel):
def __init__(self, model, tokenizer, device="cpu"):
super().__init__(model, tokenizer, device)
def forward_single(
self, transcripts: list[str], competences: list[str], tokenizer_padding=True
) -> torch.Tensor:
assert len(transcripts) == len(competences)
features = self.tokenizer(transcripts, competences, padding=tokenizer_padding)
logits = self.model(**features).logits
prob = torch.nn.functional.softmax(logits, dim=1)
prob = prob[:, 1]
return prob
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