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README.md

@@ -74,8 +74,10 @@ We provide an example code to run InternVL2-26B using `transformers`.
 > Please use transformers==4.37.2 to ensure the model works normally.
 
 ```python
+import numpy as np
 import torch
 import torchvision.transforms as T
+from decord import VideoReader, cpu
 from PIL import Image
 from torchvision.transforms.functional import InterpolationMode
 from transformers import AutoModel, AutoTokenizer
@@ -160,11 +162,21 @@ def load_image(image_file, input_size=448, max_num=6):
 
 
 path = 'OpenGVLab/InternVL2-26B'
+# If you have an 80G A100 GPU, you can put the entire model on a single GPU.
 model = AutoModel.from_pretrained(
     path,
     torch_dtype=torch.bfloat16,
     low_cpu_mem_usage=True,
     trust_remote_code=True).eval().cuda()
+# Otherwise, you need to set device_map='auto' to use multiple GPUs for inference.
+# import os
+# os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+# model = AutoModel.from_pretrained(
+#     path,
+#     torch_dtype=torch.bfloat16,
+#     low_cpu_mem_usage=True,
+#     trust_remote_code=True,
+#     device_map='auto').eval()
 
 tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
 # set the max number of tiles in `max_num`
@@ -204,7 +216,22 @@ response, history = model.chat(tokenizer, pixel_values, question, generation_con
 print(f'User: {question}')
 print(f'Assistant: {response}')
 
-# multi-image multi-round conversation (多图多轮对话)
+# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
+pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
+pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
+
+question = '<image>\nDescribe the two images in detail.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               history=None, return_history=True)
+
+question = 'What are the similarities and differences between these two images.'
+response, history = model.chat(tokenizer, pixel_values, question, generation_config,
+                               history=history, return_history=True)
+print(f'User: {question}')
+print(f'Assistant: {response}')
+
+# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
 pixel_values1 = load_image('./examples/image1.jpg', max_num=6).to(torch.bfloat16).cuda()
 pixel_values2 = load_image('./examples/image2.jpg', max_num=6).to(torch.bfloat16).cuda()
 pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
@@ -286,7 +313,7 @@ response, history = model.chat(tokenizer, pixel_values, question, generation_con
 print(f'User: {question}')
 print(f'Assistant: {response}')
 
-question = 'Describe this video in detail.'
+question = 'Describe this video in detail. Don\'t repeat.'
 response, history = model.chat(tokenizer, pixel_values, question, generation_config,
                                num_patches_list=num_patches_list,
                                history=history, return_history=True)
@@ -315,7 +342,7 @@ from lmdeploy.vl import load_image
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
 image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -336,7 +363,7 @@ from lmdeploy.vl.constants import IMAGE_TOKEN
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -362,7 +389,7 @@ from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -386,7 +413,7 @@ from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -509,7 +536,7 @@ from lmdeploy.vl import load_image
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
 image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -530,7 +557,7 @@ from lmdeploy.vl.constants import IMAGE_TOKEN
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -555,7 +582,7 @@ from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -579,7 +606,7 @@ from lmdeploy.vl import load_image
 
 model = 'OpenGVLab/InternVL2-26B'
 system_prompt = '我是书生·万象，英文名是InternVL，是由上海人工智能实验室及多家合作单位联合开发的多模态基础模型。人工智能实验室致力于原始技术创新，开源开放，共享共创，推动科技进步和产业发展。'
-chat_template_config = ChatTemplateConfig('internlm2-chat')
+chat_template_config = ChatTemplateConfig('internvl-internlm2')
 chat_template_config.meta_instruction = system_prompt
 pipe = pipeline(model, chat_template_config=chat_template_config,
                 backend_config=TurbomindEngineConfig(session_len=8192))
@@ -613,4 +640,4 @@ print(sess.response.text)
   journal={arXiv preprint arXiv:2404.16821},
   year={2024}
 }
-```
+```

config.json

@@ -12,7 +12,7 @@
   "dynamic_image_size": true,
   "force_image_size": 448,
   "llm_config": {
-    "_name_or_path": "pretrained/internlm2-chat-20b/",
+    "_name_or_path": "internlm/internlm2-chat-20b",
     "add_cross_attention": false,
     "architectures": [
       "InternLM2ForCausalLM"
@@ -111,86 +111,32 @@
   "use_llm_lora": 0,
   "use_thumbnail": true,
   "vision_config": {
-    "_name_or_path": "",
-    "add_cross_attention": false,
     "architectures": [
       "InternVisionModel"
     ],
     "attention_dropout": 0.0,
-    "bad_words_ids": null,
-    "begin_suppress_tokens": null,
-    "bos_token_id": null,
-    "chunk_size_feed_forward": 0,
-    "cross_attention_hidden_size": null,
-    "decoder_start_token_id": null,
-    "diversity_penalty": 0.0,
-    "do_sample": false,
     "drop_path_rate": 0.0,
     "dropout": 0.0,
-    "early_stopping": false,
-    "encoder_no_repeat_ngram_size": 0,
-    "eos_token_id": null,
-    "exponential_decay_length_penalty": null,
-    "finetuning_task": null,
-    "forced_bos_token_id": null,
-    "forced_eos_token_id": null,
     "hidden_act": "gelu",
     "hidden_size": 3200,
-    "id2label": {
-      "0": "LABEL_0",
-      "1": "LABEL_1"
-    },
     "image_size": 448,
     "initializer_factor": 0.1,
     "initializer_range": 1e-10,
     "intermediate_size": 12800,
-    "is_decoder": false,
-    "is_encoder_decoder": false,
-    "label2id": {
-      "LABEL_0": 0,
-      "LABEL_1": 1
-    },
     "layer_norm_eps": 1e-06,
-    "length_penalty": 1.0,
-    "max_length": 20,
-    "min_length": 0,
     "model_type": "intern_vit_6b",
-    "no_repeat_ngram_size": 0,
     "norm_type": "rms_norm",
     "num_attention_heads": 25,
-    "num_beam_groups": 1,
-    "num_beams": 1,
     "num_channels": 3,
     "num_hidden_layers": 45,
-    "num_return_sequences": 1,
     "output_attentions": false,
     "output_hidden_states": false,
-    "output_scores": false,
-    "pad_token_id": null,
     "patch_size": 14,
-    "prefix": null,
-    "problem_type": null,
-    "pruned_heads": {},
     "qk_normalization": true,
     "qkv_bias": false,
-    "remove_invalid_values": false,
-    "repetition_penalty": 1.0,
     "return_dict": true,
-    "return_dict_in_generate": false,
-    "sep_token_id": null,
-    "suppress_tokens": null,
-    "task_specific_params": null,
-    "temperature": 1.0,
-    "tf_legacy_loss": false,
-    "tie_encoder_decoder": false,
-    "tie_word_embeddings": true,
-    "tokenizer_class": null,
-    "top_k": 50,
-    "top_p": null,
     "torch_dtype": "bfloat16",
-    "torchscript": false,
     "transformers_version": "4.37.2",
-    "typical_p": 1.0,
     "use_bfloat16": true,
     "use_flash_attn": true
   }

modeling_internlm2.py

@@ -709,6 +709,7 @@ class InternLM2PreTrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = True
     _no_split_modules = ['InternLM2DecoderLayer']
     _skip_keys_device_placement = 'past_key_values'
+    _supports_flash_attn_2 = True
 
     def _init_weights(self, module):
         std = self.config.initializer_range

modeling_internvl_chat.py

@@ -7,6 +7,7 @@ import warnings
 from typing import Any, List, Optional, Tuple, Union
 
 import torch.utils.checkpoint
+import transformers
 from torch import nn
 from torch.nn import CrossEntropyLoss
 from transformers import (AutoModel, GenerationConfig, LlamaForCausalLM,
@@ -23,6 +24,14 @@ from .modeling_internlm2 import InternLM2ForCausalLM
 logger = logging.get_logger(__name__)
 
 
+def version_cmp(v1, v2, op='eq'):
+    import operator
+
+    from packaging import version
+    op_func = getattr(operator, op)
+    return op_func(version.parse(v1), version.parse(v2))
+
+
 class InternVLChatModel(PreTrainedModel):
     config_class = InternVLChatConfig
     main_input_name = 'pixel_values'
@@ -31,6 +40,7 @@ class InternVLChatModel(PreTrainedModel):
     def __init__(self, config: InternVLChatConfig, vision_model=None, language_model=None):
         super().__init__(config)
 
+        assert version_cmp(transformers.__version__, '4.36.2', 'ge')
         image_size = config.force_image_size or config.vision_config.image_size
         patch_size = config.vision_config.patch_size
         self.patch_size = patch_size
@@ -183,36 +193,44 @@ class InternVLChatModel(PreTrainedModel):
         vit_embeds = self.mlp1(vit_embeds)
         return vit_embeds
 
-    def batch_chat(self, tokenizer, pixel_values, num_patches_list, questions, generation_config, history=None,
-                         return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
-                         IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False):
+    def batch_chat(self, tokenizer, pixel_values, questions, generation_config, num_patches_list=None,
+                   history=None, return_history=False, IMG_START_TOKEN='<img>', IMG_END_TOKEN='</img>',
+                   IMG_CONTEXT_TOKEN='<IMG_CONTEXT>', verbose=False, image_counts=None):
         if history is not None or return_history:
             print('Now multi-turn chat is not supported in batch_chat.')
             raise NotImplementedError
+
+        if image_counts is not None:
+            num_patches_list = image_counts
+            print('Warning: `image_counts` is deprecated. Please use `num_patches_list` instead.')
+
         img_context_token_id = tokenizer.convert_tokens_to_ids(IMG_CONTEXT_TOKEN)
         self.img_context_token_id = img_context_token_id
 
-        from .conversation import get_conv_template
+        if verbose and pixel_values is not None:
+            image_bs = pixel_values.shape[0]
+            print(f'dynamic ViT batch size: {image_bs}')
 
         queries = []
-        if verbose:
-            image_bs = pixel_values.shape[0]
-            print(f'dynamic ViT batch size: {image_bs}, num_patches_list: {num_patches_list}')
         for idx, num_patches in enumerate(num_patches_list):
-            image_token = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
-            question = image_token + '\n' + questions[idx]
+            question = questions[idx]
+            if pixel_values is not None and '<image>' not in question:
+                question = '<image>\n' + question
             template = get_conv_template(self.template)
             template.append_message(template.roles[0], question)
             template.append_message(template.roles[1], None)
             query = template.get_prompt()
+
+            image_tokens = IMG_START_TOKEN + IMG_CONTEXT_TOKEN * self.num_image_token * num_patches + IMG_END_TOKEN
+            query = query.replace('<image>', image_tokens, 1)
             queries.append(query)
+
         tokenizer.padding_side = 'left'
         model_inputs = tokenizer(queries, return_tensors='pt', padding=True)
         input_ids = model_inputs['input_ids'].cuda()
         attention_mask = model_inputs['attention_mask'].cuda()
         eos_token_id = tokenizer.convert_tokens_to_ids(template.sep)
         generation_config['eos_token_id'] = eos_token_id
-
         generation_output = self.generate(
             pixel_values=pixel_values,
             input_ids=input_ids,

preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "crop_size": 448,
+  "do_center_crop": true,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "CLIPFeatureExtractor",
+  "image_mean": [
+    0.485,
+    0.456,
+    0.406
+  ],
+  "image_std": [
+    0.229,
+    0.224,
+    0.225
+  ],
+  "resample": 3,
+  "size": 448
+}

tokenization_internlm2_fast.py

@@ -0,0 +1,211 @@
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama_fast.py
+#
+# 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.
+
+"""Tokenization Fast class for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, Optional, Tuple
+
+from tokenizers import Tokenizer, decoders, normalizers, processors
+from tokenizers.models import BPE
+from transformers.convert_slow_tokenizer import (SLOW_TO_FAST_CONVERTERS,
+                                                 SentencePieceExtractor,
+                                                 SpmConverter)
+from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
+from transformers.utils import logging
+
+from .tokenization_internlm2 import InternLM2Tokenizer
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {'vocab_file': './tokenizer.model'}
+
+
+# Modified from transformers.convert_slow_tokenizer.LlamaConverter
+class InternLM2Converter(SpmConverter):
+    handle_byte_fallback = True
+
+    def vocab(self, proto):
+        vocab = [
+            ('<unk>', 0.0),
+            ('<s>', 0.0),
+            ('</s>', 0.0),
+        ]
+        vocab += [(piece.piece, piece.score) for piece in proto.pieces[3:]]
+        return vocab
+
+    def unk_id(self, proto):
+        unk_id = 0
+        return unk_id
+
+    def decoder(self, replacement, add_prefix_space):
+        return decoders.Sequence(
+            [
+                decoders.Replace('▁', ' '),
+                decoders.ByteFallback(),
+                decoders.Fuse(),
+                decoders.Strip(content=' ', left=1),
+            ]
+        )
+
+    def tokenizer(self, proto):
+        model_type = proto.trainer_spec.model_type
+        vocab_scores = self.vocab(proto)
+        # special tokens
+        added_tokens = self.original_tokenizer.added_tokens_decoder
+        for i in range(len(vocab_scores)):
+            piece, score = vocab_scores[i]
+            if i in added_tokens:
+                vocab_scores[i] = (added_tokens[i].content, score)
+        if model_type == 1:
+            raise RuntimeError('InternLM2 is supposed to be a BPE model!')
+
+        elif model_type == 2:
+            _, merges = SentencePieceExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
+            bpe_vocab = {word: i for i, (word, _score) in enumerate(vocab_scores)}
+            tokenizer = Tokenizer(
+                BPE(bpe_vocab, merges, unk_token=proto.trainer_spec.unk_piece, fuse_unk=True, byte_fallback=True)
+            )
+            tokenizer.add_special_tokens(
+                [ added_token for index, added_token in added_tokens.items()]
+            )
+        else:
+            raise Exception(
+                "You're trying to run a `Unigram` model but you're file was trained with a different algorithm"
+            )
+
+        return tokenizer
+
+    def normalizer(self, proto):
+        normalizers_list = []
+        if proto.normalizer_spec.add_dummy_prefix:
+            normalizers_list.append(normalizers.Prepend(prepend='▁'))
+        normalizers_list.append(normalizers.Replace(pattern=' ', content='▁'))
+        return normalizers.Sequence(normalizers_list)
+
+    def pre_tokenizer(self, replacement, add_prefix_space):
+        return None
+
+
+SLOW_TO_FAST_CONVERTERS['InternLM2Tokenizer'] = InternLM2Converter
+
+
+# Modified from transformers.model.llama.tokenization_llama_fast.LlamaTokenizerFast -> InternLM2TokenizerFast
+class InternLM2TokenizerFast(PreTrainedTokenizerFast):
+    vocab_files_names = VOCAB_FILES_NAMES
+    slow_tokenizer_class = InternLM2Tokenizer
+    padding_side = 'left'
+    model_input_names = ['input_ids', 'attention_mask']
+    _auto_class = 'AutoTokenizer'
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token='<unk>',
+        bos_token='<s>',
+        eos_token='</s>',
+        pad_token='</s>',
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_file=vocab_file,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            sp_model_kwargs=sp_model_kwargs,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            decode_with_prefix_space=decode_with_prefix_space,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+        self._add_bos_token = add_bos_token
+        self._add_eos_token = add_eos_token
+        self.update_post_processor()
+        self.vocab_file = vocab_file
+
+    @property
+    def can_save_slow_tokenizer(self) -> bool:
+        return os.path.isfile(self.vocab_file) if self.vocab_file else False
+
+    def update_post_processor(self):
+        """
+        Updates the underlying post processor with the current `bos_token` and `eos_token`.
+        """
+        bos = self.bos_token
+        bos_token_id = self.bos_token_id
+        if bos is None and self.add_bos_token:
+            raise ValueError('add_bos_token = True but bos_token = None')
+
+        eos = self.eos_token
+        eos_token_id = self.eos_token_id
+        if eos is None and self.add_eos_token:
+            raise ValueError('add_eos_token = True but eos_token = None')
+
+        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
+        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
+
+        special_tokens = []
+        if self.add_bos_token:
+            special_tokens.append((bos, bos_token_id))
+        if self.add_eos_token:
+            special_tokens.append((eos, eos_token_id))
+        self._tokenizer.post_processor = processors.TemplateProcessing(
+            single=single, pair=pair, special_tokens=special_tokens
+        )
+
+    @property
+    def add_eos_token(self):
+        return self._add_eos_token
+
+    @property
+    def add_bos_token(self):
+        return self._add_bos_token
+
+    @add_eos_token.setter
+    def add_eos_token(self, value):
+        self._add_eos_token = value
+        self.update_post_processor()
+
+    @add_bos_token.setter
+    def add_bos_token(self, value):
+        self._add_bos_token = value
+        self.update_post_processor()
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
+                'tokenizer.'
+            )
+
+        if not os.path.isdir(save_directory):
+            logger.error(f'Vocabulary path ({save_directory}) should be a directory')
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+
+        return (out_vocab_file,)