support custom field for completion from yml (#580)

* support custom field for completion from yml

* remove legacy completion check and add doc

* update README docs

README.md

@@ -322,6 +322,7 @@ See [examples](examples) for quick start. It is recommended to duplicate and mod
     - path: EleutherAI/pile
       name: enron_emails
       type: completion # format from earlier
+      field: text # Optional[str] default: text, field to use for completion data
 
   # huggingface repo with multiple named configurations/subsets
   datasets:
@@ -444,6 +445,9 @@ datasets:
       # 'no_input_format' cannot include {input}
       no_input_format: "{instruction} "
 
+      # for completions datsets, uses the provided field if not `text`
+      field:
+
 # axolotl attempts to save the dataset as an arrow after packing the data together so
 # subsequent training attempts load faster, relative path
 dataset_prepared_path: data/last_run_prepared

src/axolotl/prompt_strategies/__init__.py

@@ -1,6 +1,7 @@
 """Module to load prompt strategies."""
 
 import importlib
+import inspect
 
 from axolotl.prompt_strategies.user_defined import UserDefinedDatasetConfig
 
@@ -16,6 +17,10 @@ def load(strategy, tokenizer, cfg, ds_cfg):
         load_kwargs = {}
         if strategy == "user_defined":
             load_kwargs["ds_cfg"] = UserDefinedDatasetConfig(**ds_cfg)
+        else:
+            sig = inspect.signature(func)
+            if "ds_cfg" in sig.parameters:
+                load_kwargs["ds_cfg"] = ds_cfg
         return func(tokenizer, cfg, **load_kwargs)
     except Exception:  # pylint: disable=broad-exception-caught
         return None

src/axolotl/prompt_strategies/completion.py

@@ -0,0 +1,20 @@
+"""
+Basic completion text
+"""
+from typing import Any, Dict, Optional
+
+from axolotl.prompt_tokenizers import CompletionPromptTokenizingStrategy
+from axolotl.prompters import CompletionPrompter
+
+
+def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
+    strat = CompletionPromptTokenizingStrategy(
+        CompletionPrompter(),
+        tokenizer,
+        cfg.train_on_inputs,
+        cfg.sequence_len,
+    )
+    if ds_cfg and "field" in ds_cfg:
+        strat.field = ds_cfg["field"]
+
+    return strat

src/axolotl/prompt_tokenizers.py

@@ -245,8 +245,31 @@ class CompletionPromptTokenizingStrategy(InstructionPromptTokenizingStrategy):
     Tokenizing strategy for Completion prompts.
     """
 
+    _field: str = "text"
+
+    @property
+    def field(self) -> str:
+        return self._field
+
+    @field.setter
+    def field(self, new_field: str):
+        self._field = new_field
+
+    def parse_instruction_fields(self, prompt) -> Tuple[str, str, str]:
+        return (
+            prompt[self.field],
+            "",
+            "",
+        )
+
     def tokenize_prompt(self, prompt):
-        full_prompt = self._build_full_prompt(prompt["text"], None, None)
+        (
+            instruction,
+            _,
+            _,
+        ) = self.parse_instruction_fields(prompt)
+
+        full_prompt = self._build_full_prompt(instruction, None, None)
         tokenized_full_prompt = self._tokenize(full_prompt)
 
         return tokenized_full_prompt

src/axolotl/utils/data.py

@@ -22,7 +22,6 @@
     AlpacaMultipleChoicePromptTokenizingStrategy,
     AlpacaPromptTokenizingStrategy,
     AlpacaReflectionPTStrategy,
-    CompletionPromptTokenizingStrategy,
     GPTeacherPromptTokenizingStrategy,
     JeopardyPromptTokenizingStrategy,
     OpenAssistantPromptTokenizingStrategy,
@@ -31,7 +30,6 @@
 )
 from axolotl.prompters import (
     AlpacaPrompter,
-    CompletionPrompter,
     GPTeacherPrompter,
     JeopardyPrompter,
     MultipleChoiceConcisePrompter,
@@ -327,15 +325,6 @@ def for_d_in_datasets(dataset_configs):
                 )
                 ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
                 datasets.append(ds_wrapper)
-            elif d_base_type == "completion":
-                ds_strategy = CompletionPromptTokenizingStrategy(
-                    CompletionPrompter(),
-                    tokenizer,
-                    cfg.train_on_inputs,
-                    cfg.sequence_len,
-                )
-                ds_wrapper = TokenizedPromptDataset(ds_strategy, ds)
-                datasets.append(ds_wrapper)
             else:
                 suffix = ""
                 if ":load_" in d.type: