add-encode-function (#5)

- add encode function (d10586fa186b6d1a28e5091507e53b198db9d8ed)

modeling_xlm_roberta.py

@@ -13,6 +13,7 @@ import re
 from collections import OrderedDict
 from collections.abc import Sequence
 from functools import partial
+import numpy as np
 
 import torch
 import torch.nn as nn
@@ -29,7 +30,7 @@ from transformers.models.bert.modeling_bert import (
     BertForPreTrainingOutput,
 )
 
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 from .xlm_padding import (
     index_first_axis,
@@ -61,6 +62,11 @@ try:
 except ImportError:
     CrossEntropyLoss = None
 
+try:
+    from tqdm.autonotebook import trange
+except ImportError:
+    trange = None
+
 
 logger = logging.getLogger(__name__)
 
@@ -422,6 +428,158 @@ class XLMRobertaModel(XLMRobertaPreTrainedModel):
 
         self.apply(partial(_init_weights, initializer_range=config.initializer_range))
 
+
+    @torch.inference_mode()
+    def encode(
+        self: 'XLMRobertaModel',
+        sentences: Union[str, List[str]],
+        batch_size: int = 32,
+        show_progress_bar: Optional[bool] = None,
+        output_value: str = 'sentence_embedding',
+        convert_to_numpy: bool = True,
+        convert_to_tensor: bool = False,
+        device: Optional[torch.device] = None,
+        normalize_embeddings: bool = False,
+        **tokenizer_kwargs,
+    ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
+        """
+        Computes sentence embeddings
+        Args:
+            sentences(`str` or `List[str]`):
+                Sentence or sentences to be encoded
+            batch_size(`int`, *optional*, defaults to 32):
+                Batch size for the computation
+            show_progress_bar(`bool`, *optional*, defaults to None):
+                Show a progress bar when encoding sentences.
+                If set to None, progress bar is only shown when
+                `logger.level == logging.INFO` or `logger.level == logging.DEBUG`.
+            output_value(`str`, *optional*, defaults to 'sentence_embedding'):
+                Default sentence_embedding, to get sentence embeddings.
+                Can be set to token_embeddings to get wordpiece token embeddings.
+                Set to None, to get all output values
+            convert_to_numpy(`bool`, *optional*, defaults to True):
+                If true, the output is a list of numpy vectors.
+                Else, it is a list of pytorch tensors.
+            convert_to_tensor(`bool`, *optional*, defaults to False):
+                If true, you get one large tensor as return.
+                Overwrites any setting from convert_to_numpy
+            device(`torch.device`, *optional*, defaults to None):
+                Which torch.device to use for the computation
+            normalize_embeddings(`bool`, *optional*, defaults to False):
+                If set to true, returned vectors will have length 1. In that case, the
+                faster dot-product (util.dot_score) instead of cosine similarity can
+                be used.
+            tokenizer_kwargs(`Dict[str, Any]`, *optional*, defaults to {}):
+                Keyword arguments for the tokenizer
+        Returns:
+            By default, a list of tensors is returned.
+            If convert_to_tensor, a stacked tensor is returned.
+            If convert_to_numpy, a numpy matrix is returned.
+        """
+        from transformers import AutoTokenizer
+
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.name_or_path, trust_remote_code=True
+        )
+
+        is_training = self.training
+        self.eval()
+
+        if show_progress_bar is None:
+            show_progress_bar = (
+                logger.getEffectiveLevel() == logging.INFO
+                or logger.getEffectiveLevel() == logging.DEBUG
+            )
+
+        if convert_to_tensor:
+            convert_to_numpy = False
+
+        if output_value != 'sentence_embedding':
+            convert_to_tensor = False
+            convert_to_numpy = False
+
+        input_was_string = False
+        if isinstance(sentences, str) or not hasattr(sentences, '__len__'):
+            sentences = [sentences]
+            input_was_string = True
+
+        if device is not None:
+            self.to(device)
+
+        permutation = np.argsort([-len(i) for i in sentences])
+        inverse_permutation = np.argsort(permutation)
+        sentences = [sentences[idx] for idx in permutation]
+
+        tokenizer_kwargs['padding'] = tokenizer_kwargs.get('padding', True)
+        tokenizer_kwargs['max_length'] = tokenizer_kwargs.get(
+            'max_length', self.tokenizer.init_kwargs.get('model_max_length', 8192)
+        )
+        tokenizer_kwargs['truncation'] = tokenizer_kwargs.get('truncation', True)
+
+        all_embeddings = []
+
+        if trange is not None:
+            range_iter = trange(
+                0,
+                len(sentences),
+                batch_size,
+                desc="Encoding",
+                disable=not show_progress_bar,
+            )
+        else:
+            range_iter = range(0, len(sentences), batch_size)
+
+        for i in range_iter:
+            encoded_input = self.tokenizer(
+                sentences[i : i + batch_size],
+                return_tensors='pt',
+                **tokenizer_kwargs,
+            ).to(self.device)
+            token_embs = self.forward(**encoded_input)[0]
+
+            # Accumulate in fp32 to avoid overflow
+            token_embs = token_embs.float()
+
+            if output_value == 'token_embeddings':
+                raise NotImplementedError
+            elif output_value is None:
+                raise NotImplementedError
+            else:
+                embeddings = self.mean_pooling(
+                    token_embs, encoded_input['attention_mask']
+                )
+
+                if normalize_embeddings:
+                    embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+
+                if convert_to_numpy:
+                    embeddings = embeddings.cpu()
+            all_embeddings.extend(embeddings)
+
+        all_embeddings = [all_embeddings[idx] for idx in inverse_permutation]
+
+        if convert_to_tensor:
+            all_embeddings = torch.stack(all_embeddings)
+        elif convert_to_numpy:
+            all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
+
+        if input_was_string:
+            all_embeddings = all_embeddings[0]
+
+        self.train(is_training)
+        return all_embeddings
+
+    def mean_pooling(
+        self, token_embeddings: torch.Tensor, attention_mask: torch.Tensor
+    ):
+        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
+        )
+
+
     def forward(
         self,
         input_ids,