Create pipeline.py

pipeline.py

@@ -0,0 +1,81 @@
+from torch import cuda
+import transformers
+from accelerate import dispatch_model, infer_auto_device_map
+from accelerate.utils import get_balanced_memory
+from transformers import BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
+from typing import Dict, List, Any
+
+# define custom stopping criteria object
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_ids in stop_token_ids:
+            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
+                return True
+        return False
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        path = "oleksandrfluxon/mpt-7b-instruct-evaluate"
+        print("===> path", path)
+
+        device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+        print("===> device", device)
+
+        model = transformers.AutoModelForCausalLM.from_pretrained(
+            'oleksandrfluxon/mpt-7b-instruct-evaluate',
+            trust_remote_code=True,
+            load_in_8bit=True,  # this requires the `bitsandbytes` library
+            max_seq_len=8192,
+            init_device=device
+        )
+        model.eval()
+        #model.to(device)
+        print(f"===> Model loaded on {device}")
+
+        tokenizer = transformers.AutoTokenizer.from_pretrained("mosaicml/mpt-7b")
+
+        # we create a list of stopping criteria
+        stop_token_ids = [
+            tokenizer.convert_tokens_to_ids(x) for x in [
+                ['Human', ':'], ['AI', ':']
+            ]
+        ]
+        stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
+        print("===> stop_token_ids", stop_token_ids)
+
+        stopping_criteria = StoppingCriteriaList([StopOnTokens()])
+
+        self.pipeline = transformers.pipeline(
+            model=model, tokenizer=tokenizer,
+            return_full_text=True,  # langchain expects the full text
+            task='text-generation',
+            # we pass model parameters here too
+            stopping_criteria=stopping_criteria,  # without this model rambles during chat
+            temperature=0.1,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
+            top_p=0.15,  # select from top tokens whose probability add up to 15%
+            top_k=0,  # select from top 0 tokens (because zero, relies on top_p)
+            max_new_tokens=128,  # mex number of tokens to generate in the output
+            repetition_penalty=1.1  # without this output begins repeating
+        )
+        
+        print("===> init finished")
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str`)
+            parameters (:obj: `str`)
+      Return:
+            A :obj:`str`: todo
+        """
+        # get inputs
+        inputs = data.pop("inputs",data)
+        parameters = data.pop("parameters", {})
+        date = data.pop("date", None)
+        print("===> inputs", inputs)
+        print("===> parameters", parameters)
+
+        result = self.pipeline(inputs, **parameters)
+        print("===> result", result)
+
+        return result