fix fp16 & full bug

README.md

@@ -120,7 +120,7 @@ Users can check the [documentation of SWIFT](docs/source/GetStarted/快速使用
     - bluelm series: [bluelm-7b](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Base/summary), [bluelm-7b-chat](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Chat/summary), [bluelm-7b-32k](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Base-32K/summary), [bluelm-7b-chat-32k](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Chat-32K/summary)
     - mistral series: [mistral-7b](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-v0.1/summary), [mistral-7b-chat](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-Instruct-v0.1/summary)
     - yi series: [yi-6b](https://modelscope.cn/models/01ai/Yi-6B/summary), [yi-34b](https://modelscope.cn/models/01ai/Yi-34B/summary), [yi-34b-chat](https://modelscope.cn/models/01ai/Yi-34B-Chat/summary)
-    - zephyr series: zephyr-7b-beta-chat(https://modelscope.cn/models/modelscope/zephyr-7b-beta/summary)
+    - zephyr series: [zephyr-7b-beta-chat](https://modelscope.cn/models/modelscope/zephyr-7b-beta/summary)
     - ziya series: [ziya2-13b](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary), [ziya2-13b-chat](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Chat/summary)
     - skywork series: [skywork-13b](https://modelscope.cn/models/skywork/Skywork-13B-base/summary), [skywork-13b-chat](https://modelscope.cn/models/skywork/Skywork-13B-chat/summary)
     - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)

README_CN.md

@@ -118,7 +118,7 @@ SWIFT（Scalable lightWeight Infrastructure for Fine-Tuning）是一个可扩展
     - bluelm 系列: [bluelm-7b](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Base/summary), [bluelm-7b-chat](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Chat/summary), [bluelm-7b-32k](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Base-32K/summary), [bluelm-7b-chat-32k](https://modelscope.cn/models/vivo-ai/BlueLM-7B-Chat-32K/summary)
     - mistral 系列: [mistral-7b](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-v0.1/summary), [mistral-7b-chat](https://modelscope.cn/models/AI-ModelScope/Mistral-7B-Instruct-v0.1/summary)
     - yi 系列: [yi-6b](https://modelscope.cn/models/01ai/Yi-6B/summary), [yi-34b](https://modelscope.cn/models/01ai/Yi-34B/summary), [yi-34b-chat](https://modelscope.cn/models/01ai/Yi-34B-Chat/summary)
-    - zephyr 系列: zephyr-7b-beta-chat(https://modelscope.cn/models/modelscope/zephyr-7b-beta/summary)
+    - zephyr 系列: [zephyr-7b-beta-chat](https://modelscope.cn/models/modelscope/zephyr-7b-beta/summary)
     - ziya 系列: [ziya2-13b](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Base/summary), [ziya2-13b-chat](https://modelscope.cn/models/Fengshenbang/Ziya2-13B-Chat/summary)
     - skywork 系列: [skywork-13b](https://modelscope.cn/models/skywork/Skywork-13B-base/summary), [skywork-13b-chat](https://modelscope.cn/models/skywork/Skywork-13B-chat/summary)
     - other: [polylm-13b](https://modelscope.cn/models/damo/nlp_polylm_13b_text_generation/summary), [seqgpt-560m](https://modelscope.cn/models/damo/nlp_seqgpt-560m/summary)

swift/llm/infer.py

@@ -14,7 +14,8 @@
 from swift.utils import (append_to_jsonl, get_logger, print_model_info,
                          read_multi_line, seed_everything, show_layers)
 from .utils import (InferArguments, Template, get_dataset, get_model_tokenizer,
-                    get_template, inference, inference_stream)
+                    get_template, inference, inference_stream,
+                    set_generation_config)
 
 logger = get_logger()
 
@@ -141,7 +142,7 @@ def prepare_model_template(
         pad_token_id=tokenizer.pad_token_id,
         eos_token_id=tokenizer.eos_token_id)
     logger.info(f'generation_config: {generation_config}')
-    model.generation_config = generation_config
+    set_generation_config(model, generation_config)
     return model, template
 
 

swift/llm/rome.py

@@ -8,7 +8,7 @@
                          show_layers)
 from ..tuners.rome import RomeConfig
 from .utils import (RomeArguments, Template, get_dataset, get_model_tokenizer,
-                    get_template, inference)
+                    get_template, inference, set_generation_config)
 
 logger = get_logger()
 
@@ -72,7 +72,7 @@ def rome_infer(args: RomeArguments) -> None:
     logger.info(f'generation_config: {generation_config}')
     if args.overwrite_generation_config:
         generation_config.save_pretrained(args.ckpt_dir)
-    model.generation_config = generation_config
+    set_generation_config(model, generation_config)
 
     # Inference
     if args.eval_human:

swift/llm/sft.py

@@ -18,8 +18,9 @@
                          seed_everything, show_layers)
 from .utils import (SftArguments, Template, add_self_cognition_dataset,
                     data_collate_fn, dataset_map, find_all_linear_for_lora,
-                    get_dataset, get_model_tokenizer, get_template,
-                    print_example, sort_by_max_length, stat_dataset)
+                    get_additional_saved_files, get_dataset,
+                    get_model_tokenizer, get_template, print_example,
+                    set_generation_config, sort_by_max_length, stat_dataset)
 
 logger = get_logger()
 
@@ -182,11 +183,15 @@ def llm_sft(args: SftArguments) -> str:
         pad_token_id=tokenizer.pad_token_id,
         eos_token_id=tokenizer.eos_token_id)
     logger.info(f'generation_config: {generation_config}')
+    set_generation_config(model, generation_config)
     evaluation_strategy = IntervalStrategy.STEPS
     load_best_model_at_end = True
     if val_dataset is None:
         evaluation_strategy = IntervalStrategy.NO
         load_best_model_at_end = False
+    additional_saved_files = []
+    if args.sft_type == 'full':
+        additional_saved_files = get_additional_saved_files(args.model_type)
     training_args = Seq2SeqTrainingArguments(
         output_dir=args.output_dir,
         evaluation_strategy=evaluation_strategy,
@@ -230,7 +235,8 @@ def llm_sft(args: SftArguments) -> str:
         only_save_model=args.only_save_model,
         train_sampler_random=args.train_sampler_random,
         report_to=args.report_to,
-        deepspeed=args.deepspeed)
+        deepspeed=args.deepspeed,
+        additional_saved_files=additional_saved_files)
 
     if args.gradient_checkpointing:
         model.enable_input_require_grads()

swift/llm/utils/__init__.py

@@ -5,9 +5,9 @@
                       get_dataset_from_repo, load_dataset_from_local,
                       load_ms_dataset, register_dataset)
 from .model import (MODEL_MAPPING, GetModelTokenizerFunction, LoRATM,
-                    ModelType, get_default_lora_target_modules,
-                    get_default_template_type, get_model_tokenizer,
-                    get_model_tokenizer_from_repo,
+                    ModelType, get_additional_saved_files,
+                    get_default_lora_target_modules, get_default_template_type,
+                    get_model_tokenizer, get_model_tokenizer_from_repo,
                     get_model_tokenizer_from_sdk, register_model)
 from .preprocess import (AlpacaPreprocessor, ClsPreprocessor,
                          ComposePreprocessor, ConversationsPreprocessor,
@@ -19,5 +19,5 @@
 from .utils import (data_collate_fn, dataset_map, download_dataset,
                     find_all_linear_for_lora, history_to_messages, inference,
                     inference_stream, limit_history_length,
-                    messages_to_history, print_example, sort_by_max_length,
-                    stat_dataset)
+                    messages_to_history, print_example, set_generation_config,
+                    sort_by_max_length, stat_dataset)

swift/llm/utils/argument.py

@@ -14,9 +14,8 @@
 from swift.hub import HubApi, ModelScopeConfig
 from swift.utils import (add_version_to_work_dir, broadcast_string,
                          get_dist_setting, is_dist, is_master)
-from .dataset import (DATASET_MAPPING, DatasetName, get_custom_dataset,
-                      register_dataset)
-from .model import (MODEL_MAPPING, ModelType, dtype_mapping,
+from .dataset import DATASET_MAPPING, get_custom_dataset, register_dataset
+from .model import (MODEL_MAPPING, dtype_mapping,
                     get_default_lora_target_modules, get_default_template_type)
 from .template import TEMPLATE_MAPPING, TemplateType
 
@@ -431,8 +430,13 @@ def select_dtype(
     assert torch_dtype in {torch.float16, torch.bfloat16, torch.float32}
     if torch_dtype == torch.float16:
         if isinstance(args, SftArguments) and args.sft_type == 'full':
+            args.dtype = 'fp32'
             torch_dtype = torch.float32
-            logger.warning('Setting torch_dtype: torch.float32')
+            logger.warning(
+                'Fine-tuning with full parameters does not support fp16, and is prone to NaN. '
+                'We will use the fp32 & AMP approach, which consumes approximately twice the memory of bf16.'
+            )
+            logger.info(f'Setting torch_dtype: {torch_dtype}')
         fp16, bf16 = True, False
     elif torch_dtype == torch.bfloat16:
         support_bf16 = torch.cuda.is_bf16_supported()

swift/llm/utils/model.py

@@ -827,6 +827,13 @@ def get_model_tokenizer_qwen_vl(model_dir: str,
         ]
     get_qwen_function = kwargs.pop('get_qwen_function',
                                    get_model_tokenizer_qwen_chat)
+    tokenizer_config = get_tokenizer_config(model_dir)
+    class_ref = tokenizer_config['auto_map']['AutoTokenizer'][0]
+    tokenizer_cls = get_class_from_dynamic_module(class_ref, model_dir)
+    tokenizer_cls._auto_class = 'AutoTokenizer'
+    tokenizer_cls.IMAGE_ST = ()  # fix no attr `self.IMAGE_ST` bug
+    kwargs['tokenizer'] = tokenizer_cls.from_pretrained(
+        model_dir, trust_remote_code=True)
     model, tokenizer = get_qwen_function(model_dir, torch_dtype, model_kwargs,
                                          load_model, **kwargs)
     if model is not None:
@@ -870,6 +877,13 @@ def get_model_tokenizer_qwen_audio(model_dir: str,
                                    load_model: bool = True,
                                    **kwargs):
     get_qwen_function = kwargs.pop('get_qwen_function')
+    tokenizer_config = get_tokenizer_config(model_dir)
+    class_ref = tokenizer_config['auto_map']['AutoTokenizer'][0]
+    tokenizer_cls = get_class_from_dynamic_module(class_ref, model_dir)
+    tokenizer_cls._auto_class = 'AutoTokenizer'
+    tokenizer_cls.AUDIO_ST = ()  # fix no attr `self.AUDIO_ST` bug
+    kwargs['tokenizer'] = tokenizer_cls.from_pretrained(
+        model_dir, trust_remote_code=True)
     model, tokenizer = get_qwen_function(model_dir, torch_dtype, model_kwargs,
                                          load_model, **kwargs)
     if model is not None:
@@ -1148,6 +1162,14 @@ def get_model_tokenizer(
     return model, tokenizer
 
 
+def get_additional_saved_files(model_type: str) -> List[str]:
+    if 'qwen-vl' in model_type:
+        return ['SimSun.ttf']
+    elif 'qwen-audio' in model_type:
+        return ['mel_filters.npz']
+    return []
+
+
 def get_default_template_type(model_type: str) -> Optional[str]:
     return MODEL_MAPPING[model_type].get('template')
 

swift/llm/utils/template.py

@@ -1,6 +1,6 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 from copy import deepcopy
-from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
+from typing import Any, Dict, List, Literal, Optional, Tuple, Union
 
 from torch import Tensor
 from transformers import PreTrainedTokenizerBase, StoppingCriteria

swift/llm/utils/utils.py

@@ -28,8 +28,9 @@
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import Dataset
 from tqdm.auto import tqdm
-from transformers import (PreTrainedModel, PreTrainedTokenizerBase,
-                          StoppingCriteriaList, TextStreamer, trainer)
+from transformers import (GenerationConfig, PreTrainedModel,
+                          PreTrainedTokenizerBase, StoppingCriteriaList,
+                          TextStreamer, trainer)
 
 from swift.hub import ModelScopeConfig
 from swift.utils import (get_dist_setting, get_logger, is_ddp_plus_mp, is_dist,
@@ -540,6 +541,16 @@ def messages_to_history(messages: Messages) -> Dict[str, Any]:
     }
 
 
+def set_generation_config(model: Module,
+                          generation_config: GenerationConfig) -> None:
+    if hasattr(model, 'generation_config'):
+        old_generation_config = model.generation_config
+        for k, v in old_generation_config.__dict__.items():
+            if k not in generation_config.__dict__:
+                setattr(generation_config, k, v)
+    model.geneartion_config = generation_config
+
+
 # monkey patching
 MsDataset.load = _msdataset_ddp_load
 if is_ddp_plus_mp():

swift/trainers/arguments.py

@@ -1,6 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 from dataclasses import dataclass, field
+from typing import List, Optional
 
 from transformers.training_args import TrainingArguments as HfTrainingArguments
 from transformers.training_args_seq2seq import \
@@ -18,6 +19,12 @@ class SwiftArgumentsMixin:
             'choices':
             {'end', 'push_best', 'push_last', 'checkpoint', 'all_checkpoints'}
         })
+    additional_saved_files: Optional[List[str]] = None
+
+    def __post_init__(self):
+        if self.additional_saved_files is None:
+            self.additional_saved_files = []
+        super().__post_init__()
 
 
 @dataclass

swift/trainers/mixin.py

@@ -411,6 +411,14 @@ def _save(self, output_dir: Optional[str] = None, state_dict=None):
             self.tokenizer.save_pretrained(output_dir)
         # training_args.bin
         torch.save(self.args, os.path.join(output_dir, 'training_args.bin'))
+        # additional files
+        additional_files = getattr(self.args, 'additional_saved_files', [])
+        if model_dir is not None:
+            for file in additional_files:
+                src_path = os.path.join(model_dir, file)
+                dst_path = os.path.join(output_dir, file)
+                if os.path.exists(src_path):
+                    shutil.copy(src_path, dst_path)
 
     def _save_checkpoint(self, model, trial, metrics=None):
         only_save_model = self.args.only_save_model