support function calling

Former-commit-id: 66533b3f65babf2429c92c0f8fafe4eff5e0ff63

src/llmtuner/data/aligner.py

@@ -0,0 +1,106 @@
+from functools import partial
+from typing import TYPE_CHECKING, Any, Dict, List, Union
+
+from .utils import Role
+
+
+if TYPE_CHECKING:
+    from datasets import Dataset, IterableDataset
+
+    from ..hparams import DataArguments
+    from .parser import DatasetAttr
+
+
+def convert_alpaca(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
+    outputs = {"prompt": [], "response": [], "system": [], "tool": []}
+    for i in range(len(examples[dataset_attr.prompt])):
+        prompt = []
+        if dataset_attr.history:
+            for old_prompt, old_response in examples[dataset_attr.history][i]:
+                prompt.append({"role": Role.USER, "content": old_prompt})
+                prompt.append({"role": Role.ASSISTANT, "content": old_response})
+
+        instruction = examples[dataset_attr.prompt][i]
+        if dataset_attr.query and examples[dataset_attr.query][i]:
+            instruction += "\n" + examples[dataset_attr.query][i]
+        prompt.append({"role": Role.USER, "content": instruction})
+
+        if isinstance(examples[dataset_attr.response][i], list):
+            response = [{"role": Role.ASSISTANT, "content": content} for content in examples[dataset_attr.response][i]]
+        else:
+            response = [{"role": Role.ASSISTANT, "content": examples[dataset_attr.response][i]}]
+        
+        outputs["prompt"].append(prompt)
+        outputs["response"].append(response)
+        outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
+        outputs["tool"].append("")
+
+    return outputs
+
+
+def convert_sharegpt(examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr") -> Dict[str, List[Any]]:
+    outputs = {"prompt": [], "response": [], "system": [], "tool": []}
+    tag_mapping = {
+        dataset_attr.user_tag: Role.USER,
+        dataset_attr.assistant_tag: Role.ASSISTANT,
+        dataset_attr.observation_tag: Role.OBSERVATION,
+        dataset_attr.function_tag: Role.FUNCTION
+    }
+    for i, messages in enumerate(examples[dataset_attr.messages]):
+        messages = messages[:len(messages) // 2 * 2] # should be multiples of 2
+        if len(messages) == 0:
+            continue
+
+        prompt = []
+        response = []
+        for turn_idx, message in enumerate(messages):
+            if turn_idx % 2 == 0:
+                accept_tags = [dataset_attr.user_tag, dataset_attr.observation_tag]
+            else:
+                accept_tags = [dataset_attr.assistant_tag, dataset_attr.function_tag]
+
+            if message[dataset_attr.role_tag] not in accept_tags:
+                raise ValueError("Invalid role tag.")
+
+            prompt.append({"role": tag_mapping[message[dataset_attr.role_tag]], "content": message[dataset_attr.content_tag]})
+
+        last_message = prompt.pop(-1)
+        response.append(last_message)
+        outputs["prompt"].append(prompt)
+        outputs["response"].append(response)
+        outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
+        outputs["tool"].append(examples[dataset_attr.tool][i] if dataset_attr.tool else "")
+
+    return outputs
+
+
+def align_dataset(
+    dataset: Union["Dataset", "IterableDataset"], dataset_attr: "DatasetAttr", data_args: "DataArguments"
+) -> Union["Dataset", "IterableDataset"]:
+    r"""
+    Aligned dataset:
+        prompt: [{"role": "user", "content": "..."}]
+        response: [{"role": "assistant", "content": "..."}]
+        system: "..."
+        tool: "..."
+    """
+    if dataset_attr.formatting == "alpaca":
+        convert_func = partial(convert_alpaca, dataset_attr=dataset_attr)
+    else:
+        convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr)
+
+    column_names = list(next(iter(dataset)).keys())
+    kwargs = {}
+    if not data_args.streaming:
+        kwargs = dict(
+            num_proc=data_args.preprocessing_num_workers,
+            load_from_cache_file=(not data_args.overwrite_cache),
+            desc="Converting format of dataset"
+        )
+
+    return dataset.map(
+        convert_func,
+        batched=True,
+        remove_columns=column_names,
+        **kwargs
+    )