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Model: tommy1235/cvx-coder
Source: Original Platform

ms_wrapper.py

@@ -0,0 +1,143 @@
+import os
+import torch
+from typing import Union, Dict, Any
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.models.builder import MODELS
+from modelscope.utils.constant import Tasks
+from modelscope.pipelines.base import Pipeline
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines.nlp.text_generation_pipeline import TextGenerationPipeline
+from modelscope.models.base import Model, TorchModel
+from modelscope.utils.logger import get_logger
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers.generation.utils import GenerationConfig
+
+import torch
+
+from modelscope.models.base import TorchModel
+from modelscope.preprocessors.base import Preprocessor
+from modelscope.pipelines.base import Model, Pipeline
+from modelscope.utils.config import Config
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.preprocessors.builder import PREPROCESSORS
+from modelscope.models.builder import MODELS
+# m_path="你的路径/cvx-coder"
+# model = AutoModelForCausalLM.from_pretrained(
+#     m_path, 
+#     device_map="cuda", 
+#     torch_dtype="auto", 
+#     trust_remote_code=True, 
+# )
+# tokenizer = AutoTokenizer.from_pretrained(m_path)
+# pipe = pipeline(
+#     "text-generation", 
+#     model=model,
+#     tokenizer=tokenizer,
+# )
+# generation_args = {
+#     "max_new_tokens": 2000,
+#     "return_full_text": False,
+#     "temperature": 0,
+#     "do_sample": False,
+# }
+# content='''my problem is not convex, can i use cvx? if not, what should i do, be specific.'''
+# messages = [
+#     {"role": "user", "content": content},
+# ]
+# output = pipe(messages, **generation_args)
+# print(output[0]['generated_text'])
+
+@PIPELINES.register_module('text-generation', module_name='cvx-coder-pipe')
+class Baichuan7BTextGenerationPipeline(TextGenerationPipeline):
+    def __init__(
+            self,
+            model: Union[Model, str],
+            *args,
+            **kwargs):
+        self.model = Baichuan7BTextGeneration(model) if isinstance(model, str) else model
+        super().__init__(model=model, **kwargs)
+    
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+    
+    def _sanitize_parameters(self, **pipeline_parameters):
+        return {},pipeline_parameters,{}
+    
+    # define the forward pass
+    def forward(self, inputs: str, **forward_params) -> Dict[str, Any]:
+        output = {}
+        content=inputs
+        messages = [
+            {"role": "user", "content": content},
+        ]
+        outputs = self.model.pipeline(messages, **self.model.generation_args)
+       
+        output['text'] = outputs[0]['generated_text']
+        return output
+    
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input
+    
+
+@MODELS.register_module('text-generation', module_name='cvx-coder')
+class Baichuan7BTextGeneration(TorchModel):
+    def __init__(self, model_dir=None, *args, **kwargs):
+        super().__init__(model_dir, *args, **kwargs)
+        self.logger = get_logger()
+        # loading tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+        self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", torch_dtype="auto", trust_remote_code=True)
+        # self.model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto",trust_remote_code=True)
+        self.model.generation_config = GenerationConfig.from_pretrained(model_dir)
+        self.model = self.model.eval()
+        from transformers import pipeline
+        self.pipeline=pipeline("text-generation",model=self.model,tokenizer=self.tokenizer,)
+        self.generation_args= {"max_new_tokens": 2000, "return_full_text": False,"temperature": 0, "do_sample": False,}
+    
+    def forward(self, input: str, *args, **kwargs) -> Dict[str, Any]:
+        content = input
+        messages = [
+            {"role": "user", "content": content},
+        ]
+        outputs = self.model.pipeline(messages, **self.model.generation_args)
+       
+        response = outputs[0]['generated_text']
+        return {OutputKeys.RESPONSE:response, OutputKeys.HISTORY: ""}
+    
+    def quantize(self, bits: int):
+        self.model = self.model.quantize(bits)
+        return self
+    
+    def infer(self, input, **kwargs):
+        content = input
+        messages = [
+            {"role": "user", "content": content},
+        ]
+        outputs = self.pipeline(messages, **self.model.generation_args)
+       
+        response = outputs[0]['generated_text']
+        return response
+
+
+# Tips: usr_config_path is the temporary save configuration location， after upload modelscope hub, it is the model_id
+# usr_config_path = '/mnt/workspace/cvx-coder3'
+usr_config_path = './'
+
+config = Config({
+    "framework": 'pytorch',
+    "task": 'text-generation',
+    "model": {'type': 'cvx-coder'},
+    "pipeline": {"type": "cvx-coder-pipe"},
+    "allow_remote": True
+})
+config.dump('./'+ 'configuration.json')
+
+if __name__ == "__main__":
+    from modelscope.models import Model
+    from modelscope.pipelines import pipeline
+    # model = Model.from_pretrained(usr_config_path)
+    input = "Hello, ModelScope!"
+    inference = pipeline('text-generation', model=usr_config_path)
+    output = inference(input)
+    print(output)