pipeline trial

handler.py

@@ -1,59 +1,97 @@
 from typing import Dict, List, Any
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList
-import torch
 
 
+# class EndpointHandler():
+#     def __init__(self, path=""):
+#         tokenizer = AutoTokenizer.from_pretrained(path)
+#         tokenizer.pad_token = tokenizer.eos_token
+#         self.model = AutoModelForCausalLM.from_pretrained(path).to('cuda')
+#         self.tokenizer = tokenizer
+#         self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])
+
+#     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+#         """
+#        data args:
+#             inputs (:obj: `str`)
+#             kwargs
+#       Return:
+#             A :obj:`list` | `dict`: will be serialized and returned
+#         """
+#         inputs = data.pop("inputs", data)
+#         additional_bad_words_ids = data.pop("additional_bad_words_ids", [])
+
+
+#         # 3070, 10456, [313, 334] corresponds to "(*", and we do not want to output a comment
+#         # 13 is a newline character
+#         # [1976, 441, 29889], [4920, 441, 29889] is "Abort." [4920, 18054, 29889] is "Aborted."
+#         # [2087, 29885, 4430, 29889] is "Admitted."
+#         bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889]]
+#         bad_words_ids.extend(additional_bad_words_ids)
+
+#         input_ids = self.tokenizer.encode(inputs, return_tensors="pt").to('cuda')
+#         max_generation_length = 75  # Desired number of tokens to generate
+#         # max_input_length = 4092 - max_generation_length  # Maximum input length to allow space for generation
+
+#         # # Truncate input_ids to the most recent tokens that fit within the max_input_length
+#         # if input_ids.shape[1] > max_input_length:
+#         #     input_ids = input_ids[:, -max_input_length:]
+
+#         max_length = input_ids.shape[1] + max_generation_length
+        
+#         generated_ids = self.model.generate(
+#             input_ids,
+#             max_length=max_length,  # 50 new tokens
+#             bad_words_ids=bad_words_ids,
+#             temperature=1,
+#             top_k=40,
+#             do_sample=True,
+#             stopping_criteria=self.stopping_criteria,
+#         )
+
+#         generated_text = self.tokenizer.decode(generated_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
+#         prediction = [{"generated_text": generated_text, "generated_ids": generated_ids[0][input_ids.shape[1]:].tolist()}]
+#         return prediction
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
 class EndpointHandler():
     def __init__(self, path=""):
+        self.model_path = path
         tokenizer = AutoTokenizer.from_pretrained(path)
-        tokenizer.pad_token = tokenizer.eos_token
-        self.model = AutoModelForCausalLM.from_pretrained(path).to('cuda')
+        tokenizer.pad_token = self.tokenizer.eos_token
         self.tokenizer = tokenizer
-        self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])
+        # Initialize the pipeline for text generation
+        self.text_generation_pipeline = pipeline("text-generation", model=path, tokenizer=self.tokenizer, device=0)  # device=0 for CUDA
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
-       data args:
-            inputs (:obj: `str`)
-            kwargs
-      Return:
-            A :obj:`list` | `dict`: will be serialized and returned
+        data args:
+             inputs (:obj: `str`)
+             kwargs
+       Return:
+             A :obj:`list` | `dict`: will be serialized and returned
         """
-        torch.cuda.empty_cache()
         inputs = data.pop("inputs", data)
         additional_bad_words_ids = data.pop("additional_bad_words_ids", [])
 
-
-        # 3070, 10456, [313, 334] corresponds to "(*", and we do not want to output a comment
-        # 13 is a newline character
-        # [1976, 441, 29889], [4920, 441, 29889] is "Abort." [4920, 18054, 29889] is "Aborted."
-        # [2087, 29885, 4430, 29889] is "Admitted."
+        # Define bad words to avoid in the output
         bad_words_ids = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889]]
         bad_words_ids.extend(additional_bad_words_ids)
 
-        input_ids = self.tokenizer.encode(inputs, return_tensors="pt").to('cuda')
-        max_generation_length = 75  # Desired number of tokens to generate
-        # max_input_length = 4092 - max_generation_length  # Maximum input length to allow space for generation
-
-        # # Truncate input_ids to the most recent tokens that fit within the max_input_length
-        # if input_ids.shape[1] > max_input_length:
-        #     input_ids = input_ids[:, -max_input_length:]
+        # Generate text using the pipeline
+        generation_kwargs = {
+            "max_length": 75,  # Adjust as needed
+            "temperature": 1,
+            "top_k": 40,
+            "bad_words_ids": bad_words_ids,
+            "pad_token_id": self.tokenizer.eos_token_id  # Ensure padding with EOS token
+        }
+        generated_outputs = self.text_generation_pipeline(inputs, **generation_kwargs)
 
-        max_length = input_ids.shape[1] + max_generation_length
-        
-        generated_ids = self.model.generate(
-            input_ids,
-            max_length=max_length,  # 50 new tokens
-            bad_words_ids=bad_words_ids,
-            temperature=1,
-            top_k=40,
-            do_sample=True,
-            stopping_criteria=self.stopping_criteria,
-        )
-
-        generated_text = self.tokenizer.decode(generated_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
-        prediction = [{"generated_text": generated_text, "generated_ids": generated_ids[0][input_ids.shape[1]:].tolist()}]
-        return prediction
+        # Format the output
+        predictions = [{"generated_text": output["generated_text"]} for output in generated_outputs]
+        return predictions
 
 
 class StopAtPeriodCriteria(StoppingCriteria):