adding logging + some bad words

handler.py

@@ -1,9 +1,13 @@
+import logging
 from typing import Dict, List, Any
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList
 
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
 class EndpointHandler():
     def __init__(self, path=""):
+        logging.info("Initializing EndpointHandler with model path: %s", path)
         tokenizer = AutoTokenizer.from_pretrained(path)
         tokenizer.pad_token = tokenizer.eos_token
         self.model = AutoModelForCausalLM.from_pretrained(path)
@@ -11,55 +15,124 @@ class EndpointHandler():
         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
-        """
+        logging.info("Starting inference")
         inputs = data.pop("inputs", data)
         additional_bad_words_ids = data.pop("additional_bad_words_ids", [])
+        
+        # Log the input size
+        logging.info("Encoding inputs")
+        input_ids = self.tokenizer.encode(inputs, return_tensors="pt")
+        logging.info("Input IDs shape: %s", input_ids.shape)
 
+        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
 
-        # 3070, 10456, [313, 334] corresponds to "(*", and we do not want to output a comment
+        # 3070, 10456, [313, 334], [29898, 1068] 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 = [[3070], [313, 334], [10456], [13], [1976, 441, 29889], [2087, 29885, 4430, 29889], [4920, 441], [4920, 441, 29889], [4920, 18054, 29889], [29898, 1068]]
         bad_words_ids.extend(additional_bad_words_ids)
 
-        input_ids = self.tokenizer.encode(inputs, return_tensors="pt")
-        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
+        # Truncation and generation logging
         if input_ids.shape[1] > max_input_length:
+            logging.info("Truncating input IDs to fit within max input length")
             input_ids = input_ids[:, -max_input_length:]
 
         max_length = input_ids.shape[1] + max_generation_length
         
+        logging.info("Generating output")
         generated_ids = self.model.generate(
             input_ids,
-            max_length=max_length,  # 50 new tokens
+            max_length=max_length,
             bad_words_ids=bad_words_ids,
-            temperature=0.7,
+            temperature=0.5,
             top_k=40,
             do_sample=True,
             stopping_criteria=self.stopping_criteria,
         )
+        logging.info("Finished generating output")
 
         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()}]
+        logging.info("Inference complete")
         return prediction
 
-
 class StopAtPeriodCriteria(StoppingCriteria):
     def __init__(self, tokenizer):
         self.tokenizer = tokenizer
 
     def __call__(self, input_ids, scores, **kwargs):
-        # Decode the last generated token to text
         last_token_text = self.tokenizer.decode(input_ids[:, -1], skip_special_tokens=True)
-        # Check if the decoded text ends with a period
-        return '.' in last_token_text
+        logging.info("StopAtPeriodCriteria called. Last token text: '%s'", last_token_text)
+        return '.' in last_token_text
+
+
+
+
+# from typing import Dict, List, Any
+# from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, StoppingCriteria, StoppingCriteriaList
+
+
+# class EndpointHandler():
+#     def __init__(self, path=""):
+#         tokenizer = AutoTokenizer.from_pretrained(path)
+#         tokenizer.pad_token = tokenizer.eos_token
+#         self.model = AutoModelForCausalLM.from_pretrained(path)
+#         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], [29898, 1068] 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], [29898, 1068]]
+#         bad_words_ids.extend(additional_bad_words_ids)
+
+#         input_ids = self.tokenizer.encode(inputs, return_tensors="pt")
+#         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=0.5,
+#             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
+
+
+# class StopAtPeriodCriteria(StoppingCriteria):
+#     def __init__(self, tokenizer):
+#         self.tokenizer = tokenizer
+
+#     def __call__(self, input_ids, scores, **kwargs):
+#         # Decode the last generated token to text
+#         last_token_text = self.tokenizer.decode(input_ids[:, -1], skip_special_tokens=True)
+#         logging.info("StopAtPeriodCriteria called. Last token text: '%s'", last_token_text)
+#         # Check if the decoded text ends with a period
+#         return '.' in last_token_text