fix new dataset prompt tokenizers

src/axolotl/datasets.py

@@ -106,7 +106,7 @@ class ConstantLengthDataset(IterableDataset):
                             }
                         else:
                             logging.warning(
-                                "dropping batch due to tensor size mismatch"
+                                f"dropping batch due to tensor size mismatch input_ids: {input_ids.size()}, labels: {labels.size()}, attention_mask: {attention_mask.size()}"
                             )
                     buffer = {"input_ids": [], "attention_mask": [], "labels": []}
                     buffer_len = 0

src/axolotl/prompt_strategies/__init__.py

@@ -1,11 +1,13 @@
 import importlib
-from functools import cache
 
-@cache
 def load(strategy, tokenizer, cfg):
     try:
-        m = importlib.import_module(f".{strategy}", axolotl.prompt_strategies)
-        fn = getattr(m, "load")
+        load_fn = "load"
+        if strategy.split(".")[-1].startswith("load_"):
+            load_fn = strategy.split(".")[-1]
+            strategy = ".".join(strategy.split(".")[:-1])
+        m = importlib.import_module(f".{strategy}", "axolotl.prompt_strategies")
+        fn = getattr(m, load_fn)
         return fn(tokenizer, cfg)
     except:
         pass

src/axolotl/prompt_strategies/creative_acr.py

@@ -0,0 +1,137 @@
+from typing import Union, Generator
+
+import yaml
+from axolotl.prompt_tokenizers import InstructionPromptTokenizingStrategy
+
+
+class CreativeAnsweringPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        question = prompt["instruction"]
+        answer = prompt["revision"]  # don't use prompt[answer], that's data we don't want in the dataset
+        return (
+            question,
+            "",
+            answer,
+        )
+
+
+class CreativeCritiquePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    user_prompt = """Given the following Question and Response, critique the Response on a scale of 1-10. You should critique the answer in the following criteria:
+refusal: whether the Response refuses to answer the Question. Responses containing language similar to "As an AI language model, I am not capable of ..." is a form of refusal and should have a low refusal score. 10 is no refusal, 0 is refuses to answer the question.
+prescriptive bias: whether the Response attempts to prescribe or dictate certain actions or behaviors of the user. 10 no prescriptive bias present, 0 means there is prescriptive bias.
+creativity: score how creative the Response is. 10 is most creative, 0 lacks creativity
+comprehensiveness: score how comprehensive and in-depth the Response is. 10 is most comprehensive and in-depth, 0 lacks coverage and depth
+Then for each criteria, give a detailed explanation of why the response doesn't meet the criteria (don't reference the score), what it did right, and how it could be improved for that criteria.
+
+Output your answer in YAML format like so:
+scores:
+  refusal: <0-10>
+  prescriptive_bias: <0-10>
+  creativity: <0-10>
+  comprehensiveness: <0-10>
+critiques:
+  refusal:
+    explanation: ...
+    improvements: ...
+  prescriptive_bias:
+    explanation: ...
+    improvements: ...
+  creativity:
+    explanation: ...
+    improvements: ...
+  comprehensiveness:
+    explanation: ...
+    improvements: ...
+
+Question: {question}
+Answer: {answer}
+"""
+
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        scores = yaml.dump(prompt["scores"], default_flow_style=False, Dumper=yaml.Dumper)
+        critiques = yaml.dump(prompt["critiques"], default_flow_style=False, Dumper=yaml.Dumper)
+        evaluation = scores + critiques
+        question = prompt["instruction"]
+        answer = prompt["answer"]
+        return (
+            self.user_prompt.format(question=question, answer=answer),
+            "",
+            evaluation,
+        )
+
+
+class CreativeRevisePromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
+    user_prompt = """Definitions:
+refusal: whether the Response refuses to answer the Question. Responses containing language similar to "As an AI language model, I am not capable of ..." is a form of refusal and should have a low refusal score. 10 is no refusal, 0 is refuses to answer the question.
+prescriptive bias: whether the Response attempts to prescribe or dictate certain actions or behaviors of the user. 10 no prescriptive bias present, 0 means their is prescriptive bias.
+creativity: score how creative the Response is. 10 is most creative, 0 lacks creativity
+comprehensiveness: score how comprehensive and in-depth the Response is. 10 is most comprehensive and in-depth, 0 lacks coverage and depth
+
+Given the following Question, Response, and Evaluation, revise the Response based on the Evaluation and recommendations for improvements. Reply only with the revised response.
+
+Question: {question}
+Answer: {answer}
+Evaluation:
+{evaluation}
+"""
+
+    def parse_instruction_fields(self, prompt) -> (str, str, str):
+        scores = yaml.dump(prompt["scores"], default_flow_style=False, Dumper=yaml.Dumper)
+        critiques = yaml.dump(prompt["critiques"], default_flow_style=False, Dumper=yaml.Dumper)
+        evaluation = scores + critiques
+        question = prompt["instruction"]
+        answer = prompt["answer"]
+        return (
+            self.user_prompt.format(question=question, answer=answer, evaluation=evaluation),
+            "",
+            prompt["revision"],
+        )
+
+
+class CreativePrompterBase:
+    system_prompt = ""
+    prompt_input = "{system_prompt}\nUSER: {instruction}\nASSISTANT:"
+
+    def build_prompt(
+        self,
+        instruction: str,
+        input: Union[None, str] = None,
+        output: Union[None, str] = None,
+    ) -> Generator[str, None, None]:
+        if self.system_prompt:
+            res = f"{self.system_prompt}\nUSER: {instruction}\nASSISTANT:"
+        else:
+            res = f"USER: {instruction}\nASSISTANT:"
+        if output:
+            res = f"{res}{output}"
+        yield res
+
+
+class CreativeAnswerPrompter(CreativePrompterBase):
+    system_prompt = "Answer the following question in a comprehensive, in-depth, and creative way. Additionally your response should be relevant, accurate, and free of any ambiguity."
+
+
+class CreativeCritiquePrompter(CreativePrompterBase):
+    system_prompt = ""
+
+
+class CreativeRevisePrompter(CreativePrompterBase):
+    system_prompt = ""
+
+
+def load_answer(tokenizer, cfg):
+    return CreativeAnsweringPromptTokenizingStrategy(
+        CreativeAnswerPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )
+
+
+def load_critique(tokenizer, cfg):
+    return CreativeCritiquePromptTokenizingStrategy(
+        CreativeCritiquePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )
+
+
+def load_revise(tokenizer, cfg):
+    return CreativeRevisePromptTokenizingStrategy(
+        CreativeRevisePrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
+    )

src/axolotl/prompt_strategies/pygmalion.py

@@ -41,9 +41,9 @@ class PygmalionPromptTokenizingStrategy(PromptTokenizingStrategy):
             elif role == "bot":
                 prefix = "<|model|>"
                 res = self._tokenize(prefix + " " + message.strip(), add_eos_token=True, strip_bos_token=True)
-                res["input_ids"] = [*self.bot_prefix_token_ids, *res["input_ids"]]
                 # mask out the prefix token, rest is not masked out from labels
-                labels = [ IGNORE_TOKEN_ID ] * len(self.bot_prefix_token_ids) + [*copy.deepcopy(res["input_ids"])]
+                # make sure we create the labels first, otherwise we get incorrect lengths
+                labels = [ IGNORE_TOKEN_ID ] * len(self.bot_prefix_token_ids) + [*copy.deepcopy(res["input_ids"])][len(self.bot_prefix_token_ids):]
             else:
                 logging.warning(f"unknown role in conversation: {role}")
                 res = defaultdict(lambda: [])

src/axolotl/utils/data.py

@@ -75,7 +75,7 @@ def load_tokenized_prepared_datasets(tokenizer, cfg, default_dataset_prepared_pa
             ds = None
             ds_from_hub = False
             try:
-                load_dataset(d.path, streaming=True)
+                load_dataset(d.path, streaming=True, use_auth_token=True)
                 ds_from_hub = True
             except FileNotFoundError:
                 pass
@@ -83,18 +83,18 @@ def load_tokenized_prepared_datasets(tokenizer, cfg, default_dataset_prepared_pa
             # prefer local dataset, even if hub exists
             if Path(d.path).exists():
                 ds: IterableDataset = load_dataset(
-                    "json", data_files=d.path, streaming=True, split=None
+                    "json", data_files=d.path, streaming=False, split=None
                 )
             elif ds_from_hub:
                 if d.data_files:
-                    ds = load_dataset(d.path, streaming=True, data_files=d.data_files)
+                    ds = load_dataset(d.path, streaming=False, data_files=d.data_files, use_auth_token=True)
                 else:
-                    ds = load_dataset(d.path, streaming=True)
+                    ds = load_dataset(d.path, streaming=False, use_auth_token=True)
             else:
                 fp = hf_hub_download(
                     repo_id=d.path, repo_type="dataset", filename=d.data_files
                 )
-                ds = load_dataset("json", data_files=fp, streaming=True, split=None)
+                ds = load_dataset("json", data_files=fp, streaming=False, split=None)
             if not ds:
                 raise Exception("unhandled dataset load")
             d_type = d.type