Merge branch 'master-github' into merge_github_0912

modelscope/metainfo.py

@@ -563,6 +563,7 @@ class Pipelines(object):
     efficient_diffusion_tuning = 'efficient-diffusion-tuning'
     multimodal_dialogue = 'multimodal-dialogue'
     llama2_text_generation_pipeline = 'llama2-text-generation-pipeline'
+    llama2_text_generation_chat_pipeline = 'llama2-text-generation-chat-pipeline'
     image_to_video_task_pipeline = 'image-to-video-task-pipeline'
     video_to_video_pipeline = 'video-to-video-pipeline'
 

modelscope/models/nlp/llama/text_generation.py

@@ -72,6 +72,7 @@ def get_chat_prompt(system: str, text: str, history: List[Tuple[str, str]],
 
 # This file is mainly copied from the llama code of transformers
 @MODELS.register_module(Tasks.text_generation, module_name=Models.llama2)
+@MODELS.register_module(Tasks.chat, module_name=Models.llama2)
 @MODELS.register_module(Tasks.text_generation, module_name=Models.llama)
 class LlamaForTextGeneration(MsModelMixin, LlamaForCausalLM, TorchModel):
 

modelscope/pipelines/builder.py

@@ -19,7 +19,10 @@ from .util import is_official_hub_path
 PIPELINES = Registry('pipelines')
 
 
-def normalize_model_input(model, model_revision, third_party=None):
+def normalize_model_input(model,
+                          model_revision,
+                          third_party=None,
+                          ignore_file_pattern=None):
     """ normalize the input model, to ensure that a model str is a valid local path: in other words,
     for model represented by a model id, the model shall be downloaded locally
     """
@@ -31,7 +34,10 @@ def normalize_model_input(model, model_revision, third_party=None):
             if third_party is not None:
                 user_agent[ThirdParty.KEY] = third_party
             model = snapshot_download(
-                model, revision=model_revision, user_agent=user_agent)
+                model,
+                revision=model_revision,
+                user_agent=user_agent,
+                ignore_file_pattern=ignore_file_pattern)
     elif isinstance(model, list) and isinstance(model[0], str):
         for idx in range(len(model)):
             if is_official_hub_path(
@@ -68,6 +74,7 @@ def pipeline(task: str = None,
              framework: str = None,
              device: str = 'gpu',
              model_revision: Optional[str] = DEFAULT_MODEL_REVISION,
+             ignore_file_pattern: List[str] = None,
              **kwargs) -> Pipeline:
     """ Factory method to build an obj:`Pipeline`.
 
@@ -82,6 +89,8 @@ def pipeline(task: str = None,
         model_revision: revision of model(s) if getting from model hub, for multiple models, expecting
         all models to have the same revision
         device (str, optional): whether to use gpu or cpu is used to do inference.
+        ignore_file_pattern(`str` or `List`, *optional*, default to `None`):
+            Any file pattern to be ignored in downloading, like exact file names or file extensions.
 
     Return:
         pipeline (obj:`Pipeline`): pipeline object for certain task.
@@ -104,7 +113,10 @@ def pipeline(task: str = None,
     if third_party is not None:
         kwargs.pop(ThirdParty.KEY)
     model = normalize_model_input(
-        model, model_revision, third_party=third_party)
+        model,
+        model_revision,
+        third_party=third_party,
+        ignore_file_pattern=ignore_file_pattern)
     pipeline_props = {'type': pipeline_name}
     if pipeline_name is None:
         # get default pipeline for this task

modelscope/pipelines/nlp/text_generation_pipeline.py

@@ -2,7 +2,7 @@
 
 # Copyright (c) 2022 Zhipu.AI
 import os
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 from transformers import GenerationConfig
@@ -450,7 +450,7 @@ class SeqGPTPipeline(Pipeline):
         # gen & decode
         # prompt += '[GEN]'
         input_ids = self.tokenizer(
-            prompt + '[GEN]',
+            prompt + forward_params.get('gen_token', ''),
             return_tensors='pt',
             padding=True,
             truncation=True,
@@ -519,15 +519,15 @@ class Llama2TaskPipeline(TextGenerationPipeline):
         return {}, pipeline_parameters, {}
 
     def forward(self,
-                inputs,
-                max_length=2048,
-                do_sample=True,
-                top_p=0.85,
-                temperature=1.0,
-                repetition_penalty=1.,
-                eos_token_id=2,
-                bos_token_id=1,
-                pad_token_id=0,
+                inputs: str,
+                max_length: int = 2048,
+                do_sample: bool = False,
+                top_p: float = 0.9,
+                temperature: float = 0.6,
+                repetition_penalty: float = 1.,
+                eos_token_id: int = 2,
+                bos_token_id: int = 1,
+                pad_token_id: int = 0,
                 **forward_params) -> Dict[str, Any]:
         output = {}
         inputs = self.tokenizer(
@@ -553,3 +553,96 @@ class Llama2TaskPipeline(TextGenerationPipeline):
     # format the outputs from pipeline
     def postprocess(self, input, **kwargs) -> Dict[str, Any]:
         return input
+
+
+@PIPELINES.register_module(
+    Tasks.chat, module_name=Pipelines.llama2_text_generation_chat_pipeline)
+class Llama2chatTaskPipeline(Pipeline):
+    """Use `model` and `preprocessor` to create a generation pipeline for prediction.
+
+        Args:
+            model (str or Model): Supply either a local model dir which supported the text generation task,
+            or a model id from the model hub, or a torch model instance.
+            preprocessor (Preprocessor): An optional preprocessor instance, please make sure the preprocessor fits for
+            the model if supplied.
+            kwargs (dict, `optional`):
+                Extra kwargs passed into the preprocessor's constructor.
+        Examples:
+            >>> from modelscope.utils.constant import Tasks
+            >>> import torch
+            >>> from modelscope.pipelines import pipeline
+            >>> from modelscope import Model
+            >>> pipe = pipeline(task=Tasks.chat, model="modelscope/Llama-2-7b-chat-ms", device_map='auto',
+            >>> torch_dtype=torch.float16, ignore_file_pattern = [r'.+\\.bin$'], model_revision='v1.0.5')
+            >>> inputs = 'Where is the capital of Zhejiang?'
+            >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9,
+            >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1, pad_token_id=0)
+            >>> print(result['response'])
+            >>> inputs = 'What are the interesting places there?'
+            >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9,
+            >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1,
+            >>> pad_token_id=0, history=result['history'])
+            >>> print(result['response'])
+            >>> inputs = 'What are the company there?'
+            >>> history_demo = [('Where is the capital of Zhejiang?',
+            >>> 'Thank you for asking! The capital of Zhejiang Province is Hangzhou.')]
+            >>> result = pipe(inputs,max_length=512, do_sample=False, top_p=0.9,
+            >>> temperature=0.6, repetition_penalty=1., eos_token_id=2, bos_token_id=1,
+            >>> pad_token_id=0, history=history_demo)
+            >>> print(result['response'])
+
+            To view other examples plese check tests/pipelines/test_llama2_text_generation_pipeline.py.
+        """
+
+    def __init__(self,
+                 model: Union[Model, str],
+                 preprocessor: Preprocessor = None,
+                 config_file: str = None,
+                 device: str = 'gpu',
+                 auto_collate: bool = True,
+                 **kwargs) -> Dict[str, Any]:
+        device_map = kwargs.get('device_map', None)
+        torch_dtype = kwargs.get('torch_dtype', None)
+        self.model = Model.from_pretrained(
+            model, device_map=device_map, torch_dtype=torch_dtype)
+        from modelscope.models.nlp.llama2 import Llama2Tokenizer
+        self.tokenizer = Llama2Tokenizer.from_pretrained(model)
+        super().__init__(model=self.model, **kwargs)
+
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+
+    def _sanitize_parameters(self, **pipeline_parameters):
+        return {}, pipeline_parameters, {}
+
+    def forward(self,
+                inputs: str,
+                max_length: int = 2048,
+                do_sample: bool = False,
+                top_p: float = 0.9,
+                temperature: float = 0.6,
+                repetition_penalty: float = 1.,
+                eos_token_id: int = 2,
+                bos_token_id: int = 1,
+                pad_token_id: int = 0,
+                system: str = 'you are a helpful assistant!',
+                history: List = [],
+                **forward_params) -> Dict[str, Any]:
+        inputs_dict = forward_params
+        inputs_dict['text'] = inputs
+        inputs_dict['max_length'] = max_length
+        inputs_dict['do_sample'] = do_sample
+        inputs_dict['top_p'] = top_p
+        inputs_dict['temperature'] = temperature
+        inputs_dict['repetition_penalty'] = repetition_penalty
+        inputs_dict['eos_token_id'] = eos_token_id
+        inputs_dict['bos_token_id'] = bos_token_id
+        inputs_dict['pad_token_id'] = pad_token_id
+        inputs_dict['system'] = system
+        inputs_dict['history'] = history
+        output = self.model.chat(inputs_dict, self.tokenizer)
+        return output
+
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input

tests/pipelines/test_llama2_text_generation_pipeline.py

@@ -13,18 +13,21 @@ class Llama2TextGenerationPipelineTest(unittest.TestCase):
 
     def setUp(self) -> None:
         self.llama2_model_id_7B_chat_ms = 'modelscope/Llama-2-7b-chat-ms'
-        self.llama2_input_chat_ch = '天空为什么是蓝色的？'
+        self.llama2_input_chat_ch = 'What are the company there?'
+        self.history_demo = [(
+            'Where is the capital of Zhejiang?',
+            'Thank you for asking! The capital of Zhejiang Province is Hangzhou.'
+        )]
 
     def run_pipeline_with_model_id(self,
                                    model_id,
                                    input,
                                    init_kwargs={},
                                    run_kwargs={}):
-        pipeline_ins = pipeline(
-            task=Tasks.text_generation, model=model_id, **init_kwargs)
+        pipeline_ins = pipeline(task=Tasks.chat, model=model_id, **init_kwargs)
         pipeline_ins._model_prepare = True
         result = pipeline_ins(input, **run_kwargs)
-        print(result['text'])
+        print(result['response'])
 
     # 7B_ms_chat
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
@@ -34,12 +37,15 @@ class Llama2TextGenerationPipelineTest(unittest.TestCase):
             self.llama2_input_chat_ch,
             init_kwargs={
                 'device_map': 'auto',
-                'torch_dtype': torch.float16
+                'torch_dtype': torch.float16,
+                'model_revision': 'v1.0.5',
+                'ignore_file_pattern': [r'.+\.bin$']
             },
             run_kwargs={
-                'max_length': 200,
+                'max_length': 512,
                 'do_sample': True,
-                'top_p': 0.85
+                'top_p': 0.9,
+                'history': self.history_demo
             })
 
 

tests/pipelines/test_text_generation.py

@@ -46,7 +46,7 @@ class TextGenerationTest(unittest.TestCase):
         self.llama_model_id = 'skyline2006/llama-7b'
         self.llama_input = 'My name is Merve and my favorite'
         self.seqgpt_model_id = 'damo/nlp_seqgpt-560m'
-        self.ecomgpt_model_id = 'damo/nlp_seqgpt-560m'
+        self.ecomgpt_model_id = 'damo/nlp_ecomgpt_multilingual-7B-ecom'
 
     def run_pipeline_with_model_instance(self, model_id, input):
         model = Model.from_pretrained(model_id)
@@ -327,7 +327,8 @@ class TextGenerationTest(unittest.TestCase):
         inputs = {'task': '抽取', 'text': '杭州欢迎你。', 'labels': '地名'}
         PROMPT_TEMPLATE = '输入: {text}\n{task}: {labels}\n输出: '
         prompt = PROMPT_TEMPLATE.format(**inputs)
-        self.run_pipeline_with_model_id(self.seqgpt_model_id, prompt)
+        self.run_pipeline_with_model_id(
+            self.seqgpt_model_id, prompt, run_kwargs={'gen_token': '[GEN]'})
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_ecomgpt_with_model_name(self):
@@ -336,7 +337,7 @@ class TextGenerationTest(unittest.TestCase):
                           '### Instruction:\n{text}\n{instruction}\n\n### Response:'
         inputs = {
             'instruction':
-            'Classify the sentence, candidate labels: product, brand',
+            'Classify the sentence, select from the candidate labels: product, brand',
             'text': '照相机'
         }
         prompt = PROMPT_TEMPLATE.format(**inputs)