Add chat template

docs/interface.md

@@ -44,6 +44,12 @@ This mode supports a number of command-line arguments, the details of which can
 
 - `--include_path` : Accepts a path to a folder. If passed, then all YAML files containing `lm-eval` compatible task configurations will be added to the task registry as available tasks. Used for when one is writing config files for their own task in a folder other than `lm_eval/tasks/`.
 
+- `--system_instruction`: Specifies a system instruction string to prepend to the prompt.
+
+- `--apply_chat_template` : If this flag is on, a chat template will be applied to the prompt. For Hugging Face models, the chat template is taken from the tokenizer, if the tokenizer does not have a chat template, a default one will be applied. For other models, a generic chat template is used.
+
+- `--fewshot_as_multiturn` : If this flag is on, the Fewshot examples are treated as a multi-turn conversation. Questions are provided as user content and answers are provided as assistant responses. Requires `--num_fewshot` to be set to be greater than 0, and `--apply_chat_template` to be on.
+
 - `--predict_only`: Generates the model outputs without computing metrics. Use with `--log_samples` to retrieve decoded results.
 
 * `--seed`: Set seed for python's random, numpy and torch.  Accepts a comma-separated list of 3 values for python's random, numpy, and torch seeds, respectively, or a single integer to set the same seed for all three.  The values are either an integer or 'None' to not set the seed. Default is `0,1234,1234` (for backward compatibility).  E.g. `--seed 0,None,8` sets `random.seed(0)` and `torch.manual_seed(8)`. Here numpy's seed is not set since the second value is `None`.  E.g, `--seed 42` sets all three seeds to 42.

lm_eval/__main__.py

@@ -162,6 +162,24 @@ def setup_parser() -> argparse.ArgumentParser:
         default=False,
         help="If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis. Use with --output_path.",
     )
+    parser.add_argument(
+        "--system_instruction",
+        type=str,
+        default=None,
+        help="System instruction to be used in the prompt",
+    )
+    parser.add_argument(
+        "--apply_chat_template",
+        action="store_true",
+        default=False,
+        help="If True, applies the chat template to the prompt",
+    )
+    parser.add_argument(
+        "--fewshot_as_multiturn",
+        action="store_true",
+        default=False,
+        help="If True, uses the fewshot as a multi-turn conversation",
+    )
     parser.add_argument(
         "--show_config",
         action="store_true",
@@ -270,6 +288,11 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
             "Specify --output_path if providing --log_samples or --predict_only"
         )
 
+    if args.fewshot_as_multiturn and args.apply_chat_template is False:
+        raise ValueError(
+            "If fewshot_as_multiturn is set, apply_chat_template must be set to True."
+        )
+
     if args.include_path is not None:
         eval_logger.info(f"Including path: {args.include_path}")
     task_manager = TaskManager(args.verbosity, include_path=args.include_path)
@@ -357,6 +380,9 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         check_integrity=args.check_integrity,
         write_out=args.write_out,
         log_samples=args.log_samples,
+        system_instruction=args.system_instruction,
+        apply_chat_template=args.apply_chat_template,
+        fewshot_as_multiturn=args.fewshot_as_multiturn,
         gen_kwargs=args.gen_kwargs,
         task_manager=task_manager,
         verbosity=args.verbosity,

lm_eval/api/samplers.py

@@ -35,37 +35,79 @@ def get_context(self, doc, num_fewshot):
         # TODO: should we just stop people from using fewshot from same split as evaluating?
         selected_docs = [x for x in fewshotex if x != doc][:num_fewshot]
 
-        labeled_examples = (
-            self.fewshot_delimiter.join(
-                [
-                    # TODO: is separating doc_to_text and doc_to_target by one space always desired?
-                    (
-                        self.doc_to_text(doc)
-                        if (
-                            self.config.doc_to_choice is None
-                            or isinstance(self.doc_to_text(doc), str)
-                        )
-                        else self.doc_to_choice(doc)[self.doc_to_text(doc)]
-                    )
-                    + self.target_delimiter
-                    + (
-                        str(self.doc_to_target(doc)[0])
-                        if isinstance(self.doc_to_target(doc), list)
-                        else self.doc_to_target(doc)
-                        if (
-                            self.config.doc_to_choice is None
-                            or isinstance(self.doc_to_target(doc), str)
-                        )
-                        else str(self.doc_to_choice(doc)[self.doc_to_target(doc)])
-                    )
-                    for doc in selected_docs
-                ]
+        labeled_examples = ""
+        for doc in selected_docs:
+            doc_content = self.doc_to_text(doc)
+            doc_target = self.doc_to_target(doc)
+            labeled_examples += (
+                doc_content
+                if self.config.doc_to_choice is None or isinstance(doc_content, str)
+                else self.doc_to_choice(doc)[doc_content]
             )
-            + self.fewshot_delimiter
-        )
+            labeled_examples += self.target_delimiter
+            labeled_examples += (
+                str(doc_target[0])
+                if isinstance(doc_target, list)
+                else doc_target
+                if self.config.doc_to_choice is None or isinstance(doc_target, str)
+                else str(self.doc_to_choice(doc)[doc_target])
+            )
+            labeled_examples += self.fewshot_delimiter
 
         return labeled_examples
 
+    def get_chat_context(
+        self,
+        doc,
+        num_fewshot,
+        fewshot_as_multiturn: bool = False,
+    ):
+        chat_history = []
+        # draw an extra fewshot sample if using same split as evaluating on
+        n_samples = (
+            num_fewshot + 1
+            if self.config.fewshot_split == self.config.test_split
+            else num_fewshot
+        )
+        # draw `n_samples` docs from fewshot_docs
+        fewshotex = self.sample(n_samples)
+
+        # get rid of the doc that's the one we're evaluating, if it's in the fewshot
+        # TODO: should we just stop people from using fewshot from same split as evaluating?
+        selected_docs = [x for x in fewshotex if x != doc][:num_fewshot]
+
+        if fewshot_as_multiturn:
+            for doc in selected_docs:
+                doc_content = self.doc_to_text(doc)
+                doc_target = self.doc_to_target(doc)
+                chat_history.append(
+                    {
+                        "role": "user",
+                        "content": doc_content
+                        if self.config.doc_to_choice is None
+                        or isinstance(doc_content, str)
+                        else self.doc_to_choice(doc)[doc_content],
+                    }
+                )
+                chat_history.append(
+                    {
+                        "role": "assistant",
+                        "content": str(doc_target[0])
+                        if isinstance(doc_target, list)
+                        else doc_target
+                        if self.config.doc_to_choice is None
+                        or isinstance(doc_target, str)
+                        else str(self.doc_to_choice(doc)[doc_target]),
+                    }
+                )
+        else:
+            # get fewshot context as one user turn
+            chat_history.append(
+                {"role": "user", "content": self.get_context(doc, num_fewshot)}
+            )
+
+        return chat_history
+
     def sample(self, n):
         """
         Draw `n` samples from our fewshot docs. This method should be overridden by subclasses.

lm_eval/api/task.py

@@ -373,6 +373,10 @@ def build_all_requests(
         world_size=None,
         cache_requests=False,
         rewrite_requests_cache=False,
+        system_instruction=None,
+        apply_chat_template=False,
+        fewshot_as_multiturn=False,
+        tokenizer=None,
     ) -> None:
         """Build a set of Instances for a task, and store them in task.instances"""
 
@@ -421,6 +425,10 @@ def build_all_requests(
             fewshot_ctx = self.fewshot_context(
                 doc,
                 0 if self.config.num_fewshot is None else self.config.num_fewshot,
+                system_instruction,
+                apply_chat_template,
+                fewshot_as_multiturn,
+                tokenizer,
             )
 
             # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
@@ -957,31 +965,123 @@ def fewshot_docs(self):
                 )
             return super().fewshot_docs()
 
+    def convert_chat_history_to_string(self, chat_history: list, tokenizer=None) -> str:
+        """Returns chat history tokenized or concatenated as a string.
+
+        :param chat_history: list
+            The chat history to convert to a string.
+        :param tokenizer:
+            Optional tokenizer to use for applying the chat template, if None, the sampler's fewshot_delimiter is used.
+        """
+        if tokenizer:
+            return tokenizer.apply_chat_template(
+                chat_history, tokenize=False, add_generation_prompt=True
+            )
+        else:
+            return self.sampler.fewshot_delimiter + "".join(
+                f"{s['role']}: {s['content']}" + self.sampler.fewshot_delimiter
+                for s in chat_history
+            )
+
     @utils.positional_deprecated
-    def fewshot_context(self, doc: str, num_fewshot: int) -> str:
+    def fewshot_context(
+        self,
+        doc: str,
+        num_fewshot: int,
+        system_instruction: Optional[str] = None,
+        apply_chat_template: bool = False,
+        fewshot_as_multiturn: bool = False,
+        tokenizer=None,
+    ) -> str:
         """Returns a fewshot context string that is made up of a prepended description
         (if provided), the `num_fewshot` number of examples, and an appended prompt example.
 
         :param doc: str
             The document as returned from training_docs, validation_docs, or test_docs.
         :param num_fewshot: int
             The number of fewshot examples to provide in the returned context string.
+        :param  system_instruction: str
+            System instruction to be applied to the prompt.
+        :param apply_chat_template: bool
+            Whether to apply the chat template to the fewshot context.
+        :param fewshot_as_multiturn: bool
+            Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
+        :param tokenizer:
+            The tokenizer to use for applying the chat template.
         :returns: str
             The fewshot context.
         """
+
+        if apply_chat_template:
+            labeled_examples = []
+        else:
+            labeled_examples = ""
+
+        # get task description
         if description := self.config.description:
             description = utils.apply_template(self.config.description, doc)
 
-        if num_fewshot == 0:
-            # always prepend the (possibly empty) task description
-            labeled_examples = description
+        # create system prompt based on the provided system instruction and description
+        if system_instruction is not None and description:
+            system_prompt = (
+                f"{system_instruction}{self.sampler.fewshot_delimiter}{description}"
+            )
+        elif system_instruction is not None:
+            system_prompt = system_instruction
+        elif description:
+            system_prompt = description
         else:
-            labeled_examples = description + self.sampler.get_context(doc, num_fewshot)
+            system_prompt = ""
+
+        # add system prompt if specified
+        if system_prompt:
+            if apply_chat_template:
+                labeled_examples.append({"role": "system", "content": system_prompt})
+            else:
+                labeled_examples = system_prompt
+
+        # if few-shot - append examples after the system prompt
+        if num_fewshot > 0:
+            if apply_chat_template:
+                labeled_examples.extend(
+                    self.sampler.get_chat_context(
+                        doc, num_fewshot, fewshot_as_multiturn
+                    )
+                )
+            else:
+                labeled_examples += self.sampler.get_context(doc, num_fewshot)
 
         example = self.doc_to_text(doc)
-        if self.multiple_input:
-            return labeled_examples
+        if apply_chat_template:
+            if not self.multiple_input:
+                if isinstance(example, str):
+                    labeled_examples.append({"role": "user", "content": example})
+                # for loglikelihood create a list of questions with appended choices
+                elif isinstance(example, list):
+                    labeled_examples_list = []
+                    # copy chat history for each example and append the answer
+                    for ex in example:
+                        chat = deepcopy(labeled_examples)
+                        chat.append({"role": "user", "content": ex})
+                        labeled_examples_list.append(
+                            self.convert_chat_history_to_string(chat, tokenizer)
+                        )
+                    return labeled_examples_list
+                # if example is an integer, append the choice or convert to string
+                elif isinstance(example, int):
+                    if self.config.doc_to_choice is not None:
+                        choices = self.doc_to_choice(doc)
+                        labeled_examples.append(
+                            {"role": "user", "content": choices[example]}
+                        )
+                    else:
+                        labeled_examples.append(
+                            {"role": "user", "content": str(example)}
+                        )
+            return self.convert_chat_history_to_string(labeled_examples, tokenizer)
         else:
+            if self.multiple_input:
+                return labeled_examples
             if isinstance(example, str):
                 return labeled_examples + example
             elif isinstance(example, list):

lm_eval/evaluator.py

@@ -55,6 +55,9 @@ def simple_evaluate(
     check_integrity: bool = False,
     write_out: bool = False,
     log_samples: bool = True,
+    system_instruction: Optional[str] = None,
+    apply_chat_template: bool = False,
+    fewshot_as_multiturn: bool = False,
     gen_kwargs: Optional[str] = None,
     task_manager: Optional[TaskManager] = None,
     verbosity: str = "INFO",
@@ -99,6 +102,12 @@ def simple_evaluate(
         If True, write out an example document and model input for checking task integrity
     :param log_samples: bool
         If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis
+    :param system_instruction: str
+        System instruction to be applied to the prompt
+    :param apply_chat_template: bool
+        If True, apply chat template to the prompt
+    :param fewshot_as_multiturn: bool
+        Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
     :param gen_kwargs: str
         String arguments for model generation
         Ignored for all tasks with loglikelihood output_type
@@ -262,6 +271,9 @@ def simple_evaluate(
         bootstrap_iters=bootstrap_iters,
         write_out=write_out,
         log_samples=log_samples,
+        system_instruction=system_instruction,
+        apply_chat_template=apply_chat_template,
+        fewshot_as_multiturn=fewshot_as_multiturn,
         verbosity=verbosity,
     )
 
@@ -317,6 +329,9 @@ def evaluate(
     bootstrap_iters: Optional[int] = 100000,
     write_out: bool = False,
     log_samples: bool = True,
+    system_instruction: Optional[str] = None,
+    apply_chat_template: bool = False,
+    fewshot_as_multiturn: bool = False,
     verbosity: str = "INFO",
 ):
     """Instantiate and evaluate a model on a list of tasks.
@@ -333,6 +348,12 @@ def evaluate(
         If True, write out an example document and model input for checking task integrity
     :param log_samples: bool
         If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis
+    :param system_instruction: str
+        System instruction to be applied to the prompt
+    :param apply_chat_template: bool
+        If True, apply chat template to the prompt
+    :param fewshot_as_multiturn: bool
+        Whether to provide the fewshot examples as a multiturn conversation or a single user turn.
     :return
         Dictionary of results
     """
@@ -362,6 +383,10 @@ def evaluate(
             world_size=lm.world_size,
             cache_requests=cache_requests,
             rewrite_requests_cache=rewrite_requests_cache,
+            system_instruction=system_instruction,
+            apply_chat_template=apply_chat_template,
+            fewshot_as_multiturn=fewshot_as_multiturn,
+            tokenizer=lm.tokenizer if hasattr(lm, "tokenizer") else None,
         )
         eval_logger.debug(
             f"Task: {task_output.task_name}; number of requests on this rank: {len(task.instances)}"

lm_eval/models/huggingface.py

@@ -583,7 +583,9 @@ def _create_model(
             if self._model.config.vocab_size != len(self.tokenizer):
                 # resize model for LoRAs with added tokens
                 self._model.resize_token_embeddings(len(self.tokenizer))
-                eval_logger.info(f"Model config indicates vocab_size='{self._model.config.vocab_size}', but found tokenizer with vocab size '{len(self.tokenizer)}'. Resizing model embedding layer...") 
+                eval_logger.info(
+                    f"Model config indicates vocab_size='{self._model.config.vocab_size}', but found tokenizer with vocab size '{len(self.tokenizer)}'. Resizing model embedding layer..."
+                )
             self._model = PeftModel.from_pretrained(
                 self._model, peft, revision=revision
             )