Merge branch 'main' into llama

# Conflicts:
#	lm_eval/tasks/llama3/README.md

lm_eval/filters/extraction.py

@@ -8,12 +8,17 @@ from lm_eval.api.registry import register_filter
 
 @register_filter("regex")
 class RegexFilter(Filter):
-    """ """
+    """A filter that extracts values from text using regex pattern matching.
+
+    This filter applies a regex pattern to each model response and extracts matched values.
+    If no match is found, returns a fallback value. Useful for extracting structured data
+    (like numbers) from unstructured model outputs.
+    """
 
     def __init__(
         self,
         regex_pattern: str = r"#### (\-?[0-9\.\,]+)",
-        group_select=0,
+        group_select: int = 0,
         fallback: str = "[invalid]",
     ) -> None:
         """
@@ -25,7 +30,7 @@ class RegexFilter(Filter):
         self.group_select = group_select
         self.fallback = fallback
 
-    def apply(self, resps, docs):
+    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
         # here, we assume we have a list, in which each element is
         # a list of model responses for some particular input/target pair.
         # so we process each of these (same input/target response sets)
@@ -55,12 +60,9 @@ class RegexFilter(Filter):
 
 @register_filter("remove_whitespace")
 class WhitespaceFilter(Filter):
-    """ """
-
-    def __init__(self) -> None:
-        pass
+    """Filters out leading whitespace from responses."""
 
-    def apply(self, resps, docs):
+    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
         def filter_set(inst):
             filtered_resp = []
             for resp in inst:
@@ -105,7 +107,7 @@ class MultiChoiceRegexFilter(RegexFilter):
         self.ignore_punctuation = ignore_punctuation
         self.regexes_to_ignore = regexes_to_ignore
 
-    def apply(self, resps, docs):
+    def apply(self, resps: list[list[str]], docs: list[dict]) -> list[list[str]]:
         # here, we assume we have a list, in which each element is
         # a list of model responses for some particular input/target pair.
         # so we process each of these (same input/target response sets)
@@ -164,7 +166,7 @@ class MultiChoiceRegexFilter(RegexFilter):
             fallback_regex = re.compile("|".join(fallback_regexes))
             without_paren_fallback_regex = "|".join(without_paren_fallback_regexes)
             without_paren_fallback_regex = re.compile(
-                f":[\s]*({without_paren_fallback_regex})"
+                rf":[\s]*({without_paren_fallback_regex})"
             )
 
             filtered = []

lm_eval/filters/selection.py

@@ -34,9 +34,9 @@ class TakeKFilter(Filter):
         # need resp to be subscriptable to check below
         resps = list(resps)
         # check we have at least k responses per doc, else we can't take the first k
-        assert (
-            len(resps[0]) >= self.k
-        ), f"Need at least {self.k} responses per doc to take first {self.k}, but got {len(resps[0])} only! Please increase TaskConfig.repeats ."
+        assert len(resps[0]) >= self.k, (
+            f"Need at least {self.k} responses per doc to take first {self.k}, but got {len(resps[0])} only! Please increase TaskConfig.repeats ."
+        )
         return map(lambda r: r[: self.k], resps)
 
 

lm_eval/filters/transformation.py

@@ -43,9 +43,9 @@ class MapFilter(Filter):
         """
         if mapping_dict is None:
             mapping_dict = {}
-        assert isinstance(
-            mapping_dict, dict
-        ), "Provided mapping_dict is not a dictionary"
+        assert isinstance(mapping_dict, dict), (
+            "Provided mapping_dict is not a dictionary"
+        )
         self.mapping_dict = mapping_dict
         self.default_value = default_value
 

lm_eval/loggers/evaluation_tracker.py

@@ -488,7 +488,7 @@ class EvaluationTracker:
         else:
             dataset_summary += f"{self.general_config_tracker.model_name}\n"
         dataset_summary += (
-            f"The dataset is composed of {len(card_metadata)-1} configuration(s), each one corresponding to one of the evaluated task.\n\n"
+            f"The dataset is composed of {len(card_metadata) - 1} configuration(s), each one corresponding to one of the evaluated task.\n\n"
             f"The dataset has been created from {len(results_files)} run(s). Each run can be found as a specific split in each "
             'configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.\n\n'
             'An additional configuration "results" store all the aggregated results of the run.\n\n'
@@ -501,7 +501,7 @@ class EvaluationTracker:
             )
         dataset_summary += (
             "## Latest results\n\n"
-            f'These are the [latest results from run {latest_datetime}]({last_results_file_path.replace("/resolve/", "/blob/")}) '
+            f"These are the [latest results from run {latest_datetime}]({last_results_file_path.replace('/resolve/', '/blob/')}) "
             "(note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. "
             'You find each in the results and the "latest" split for each eval):\n\n'
             f"```python\n{results_string}\n```"

lm_eval/loggers/wandb_logger.py

@@ -48,6 +48,9 @@ class WandbLogger:
 
         self.wandb_args: Dict[str, Any] = kwargs
 
+        # pop the step key from the args to save for all logging calls
+        self.step = self.wandb_args.pop("step", None)
+
         # initialize a W&B run
         if wandb.run is None:
             self.run = wandb.init(**self.wandb_args)
@@ -152,11 +155,11 @@ class WandbLogger:
 
         # log the complete eval result to W&B Table
         table = make_table(["Tasks"] + columns, "results")
-        self.run.log({"evaluation/eval_results": table})
+        self.run.log({"evaluation/eval_results": table}, step=self.step)
 
         if "groups" in self.results.keys():
             table = make_table(["Groups"] + columns, "groups")
-            self.run.log({"evaluation/group_eval_results": table})
+            self.run.log({"evaluation/group_eval_results": table}, step=self.step)
 
     def _log_results_as_artifact(self) -> None:
         """Log results as JSON artifact to W&B."""
@@ -174,13 +177,13 @@ class WandbLogger:
         """Log evaluation results to W&B."""
         # Log configs to wandb
         configs = self._get_config()
-        self.run.config.update(configs)
+        self.run.config.update(configs, allow_val_change=self.step is not None)
 
         wandb_summary, self.wandb_results = self._sanitize_results_dict()
         # update wandb.run.summary with items that were removed
         self.run.summary.update(wandb_summary)
         # Log the evaluation metrics to wandb
-        self.run.log(self.wandb_results)
+        self.run.log(self.wandb_results, step=self.step)
         # Log the evaluation metrics as W&B Table
         self._log_results_as_table()
         # Log the results dict as json to W&B Artifacts
@@ -222,7 +225,7 @@ class WandbLogger:
             instance = [x["arguments"][0][0] for x in data]
             labels = [x["arguments"][0][1] for x in data]
             resps = [
-                f'log probability of continuation is {x["resps"][0][0][0]} '
+                f"log probability of continuation is {x['resps'][0][0][0]} "
                 + "\n\n"
                 + "continuation will {} generated with greedy sampling".format(
                     "not be" if not x["resps"][0][0][1] else "be"
@@ -230,7 +233,7 @@ class WandbLogger:
                 for x in data
             ]
             filtered_resps = [
-                f'log probability of continuation is {x["filtered_resps"][0][0]} '
+                f"log probability of continuation is {x['filtered_resps'][0][0]} "
                 + "\n\n"
                 + "continuation will {} generated with greedy sampling".format(
                     "not be" if not x["filtered_resps"][0][1] else "be"
@@ -329,7 +332,7 @@ class WandbLogger:
 
             # log the samples as a W&B Table
             df = self._generate_dataset(eval_preds, self.task_configs.get(task_name))
-            self.run.log({f"{task_name}_eval_results": df})
+            self.run.log({f"{task_name}_eval_results": df}, step=self.step)
 
             # log the samples as a json file as W&B Artifact
             self._log_samples_as_artifact(eval_preds, task_name)
@@ -348,4 +351,4 @@ class WandbLogger:
                 # log the samples as a json file as W&B Artifact
                 self._log_samples_as_artifact(eval_preds, task_name)
 
-            self.run.log({f"{group}_eval_results": grouped_df})
+            self.run.log({f"{group}_eval_results": grouped_df}, step=self.step)

lm_eval/models/__init__.py

@@ -11,6 +11,7 @@ from . import (
     neuralmagic,
     neuron_optimum,
     openai_completions,
+    optimum_ipex,
     optimum_lm,
     textsynth,
     vllm_causallms,

lm_eval/models/api_models.py

@@ -195,9 +195,9 @@ class TemplateAPI(TemplateLM):
         """Helper method to transform the prompt into the expected API input format. messages consist of batched requests"""
         if isinstance(messages[0], JsonChatStr):
             # for chat completions we need to decode the json string to list[dict,...]
-            assert (
-                self._batch_size == 1
-            ), "non-tokenized chat requests are only supported with batch_size=1"
+            assert self._batch_size == 1, (
+                "non-tokenized chat requests are only supported with batch_size=1"
+            )
             # list[dict["role":..., "content":...],...]
             return json.loads(messages[0].prompt)
 
@@ -253,12 +253,15 @@ class TemplateAPI(TemplateLM):
         return ""
 
     def apply_chat_template(
-        self, chat_history: List[Dict[str, str]]
+        self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
     ) -> Union[str, JsonChatStr]:
         """Applies a chat template to a list of chat history between user and model."""
         if self.tokenizer_backend == "huggingface" and self.tokenized_requests:
             return self.tokenizer.apply_chat_template(
-                chat_history, tokenize=False, add_generation_prompt=True
+                chat_history,
+                tokenize=False,
+                add_generation_prompt=add_generation_prompt,
+                continue_final_message=not add_generation_prompt,
             )
         else:
             # bit of a hack. We'll load back before sending to the API
@@ -503,9 +506,9 @@ class TemplateAPI(TemplateLM):
             return await tqdm_asyncio.gather(*tasks, desc="Requesting API")
 
     def _loglikelihood_tokens(self, requests, **kwargs) -> List[Tuple[float, bool]]:
-        assert (
-            self.tokenizer is not None
-        ), "Tokenizer is required for loglikelihood tasks to compute context lengths."
+        assert self.tokenizer is not None, (
+            "Tokenizer is required for loglikelihood tasks to compute context lengths."
+        )
         res = []
 
         def _collate(req: LogLikelihoodInputs):

lm_eval/models/hf_vlms.py

@@ -51,9 +51,9 @@ class HFMultimodalLM(HFLM):
         # modify init behavior.
         super().__init__(pretrained, **kwargs)
 
-        assert (
-            self.batch_size != "auto"
-        ), "Batch size 'auto' is not yet supported for hf-multimodal models."
+        assert self.batch_size != "auto", (
+            "Batch size 'auto' is not yet supported for hf-multimodal models."
+        )
         self.chat_applied: bool = False
         # TODO: phi-3.5 "image placeholders" are <image_1>, <image_2>, ... in order. how to handle this case
 
@@ -73,9 +73,9 @@ class HFMultimodalLM(HFLM):
                     or getattr(self.config, "image_token_index", None)
                 )
             )
-            assert (
-                self.image_token_id is not None
-            ), "Must have a non-None image_token_id to evaluate a Hugging Face AutoModelForVision2Seq model. Please pass `image_token_id` in `--model_args` if model's config does not already specify one."
+            assert self.image_token_id is not None, (
+                "Must have a non-None image_token_id to evaluate a Hugging Face AutoModelForVision2Seq model. Please pass `image_token_id` in `--model_args` if model's config does not already specify one."
+            )
             # get the string this token ID corresponds to
             self.image_token = self.tok_decode(
                 [self.image_token_id], skip_special_tokens=False
@@ -200,7 +200,9 @@ class HFMultimodalLM(HFLM):
 
         return context_enc, continuation_enc, image_enc
 
-    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+    def apply_chat_template(
+        self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
+    ) -> str:
         self.chat_applied = True
         if not self.interleave:
             for content in chat_history:
@@ -250,7 +252,9 @@ class HFMultimodalLM(HFLM):
                     )
 
         return self.processor.apply_chat_template(
-            chat_history, add_generation_prompt=True
+            chat_history,
+            add_generation_prompt=add_generation_prompt,
+            continue_final_message=not add_generation_prompt,
         )
 
     def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:

lm_eval/models/huggingface.py

@@ -90,6 +90,7 @@ class HFLM(TemplateLM):
         delta: Optional[str] = None,
         autogptq: Optional[Union[bool, str]] = False,
         gptqmodel: Optional[bool] = False,
+        gguf_file: Optional[str] = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -98,7 +99,9 @@ class HFLM(TemplateLM):
             eval_logger.warning(
                 "`pretrained` model kwarg is not of type `str`. Many other model arguments may be ignored. Please do not launch via accelerate or use `parallelize=True` if passing an existing model this way."
             )
-            assert not parallelize, "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
+            assert not parallelize, (
+                "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
+            )
             self._model = pretrained
             self._device = self._model.device
             self._config = self._model.config
@@ -164,6 +167,7 @@ class HFLM(TemplateLM):
                 pretrained,
                 revision=revision,
                 trust_remote_code=trust_remote_code,
+                gguf_file=gguf_file,
             )
 
             # determine which of 'causal' and 'seq2seq' backends to use for HF models
@@ -178,6 +182,7 @@ class HFLM(TemplateLM):
             revision=revision,
             trust_remote_code=trust_remote_code,
             use_fast_tokenizer=use_fast_tokenizer,
+            gguf_file=gguf_file,
         )
 
         # if we passed `pretrained` as a string, initialize our model now
@@ -196,6 +201,7 @@ class HFLM(TemplateLM):
                 delta=delta,
                 autogptq=autogptq,
                 gptqmodel=gptqmodel,
+                gguf_file=gguf_file,
                 **kwargs,
             )
 
@@ -508,12 +514,14 @@ class HFLM(TemplateLM):
         pretrained: str,
         revision: str = "main",
         trust_remote_code: bool = False,
+        gguf_file: Optional[str] = None,
     ) -> None:
         """Return the model config for HuggingFace models"""
         self._config = transformers.AutoConfig.from_pretrained(
             pretrained,
             revision=revision,
             trust_remote_code=trust_remote_code,
+            gguf_file=gguf_file,
         )
 
     def _create_model(
@@ -535,6 +543,7 @@ class HFLM(TemplateLM):
         delta: Optional[str] = None,
         autogptq: Optional[Union[bool, str]] = False,
         gptqmodel: Optional[bool] = False,
+        gguf_file: Optional[str] = None,
         **kwargs,
     ) -> None:
         """
@@ -564,9 +573,9 @@ class HFLM(TemplateLM):
 
         if not autogptq and not gptqmodel:
             if model_kwargs.get("load_in_4bit", None):
-                assert (
-                    transformers.__version__ >= "4.30.0"
-                ), "load_in_4bit requires transformers >= 4.30.0"
+                assert transformers.__version__ >= "4.30.0", (
+                    "load_in_4bit requires transformers >= 4.30.0"
+                )
             if transformers.__version__ >= "4.30.0":
                 if model_kwargs.get("load_in_4bit", None):
                     if model_kwargs.get("bnb_4bit_compute_dtype", None):
@@ -579,6 +588,7 @@ class HFLM(TemplateLM):
                 revision=revision,
                 torch_dtype=get_dtype(dtype),
                 trust_remote_code=trust_remote_code,
+                gguf_file=gguf_file,
                 **model_kwargs,
             )
         else:
@@ -676,6 +686,7 @@ class HFLM(TemplateLM):
         revision: Optional[str] = "main",
         trust_remote_code: Optional[bool] = False,
         use_fast_tokenizer: Optional[bool] = True,
+        gguf_file: Optional[str] = None,
     ) -> None:
         """
         Helper method during initialization.
@@ -683,14 +694,21 @@ class HFLM(TemplateLM):
         Create a tokenizer object corresponding to the correct
         tokenizer for value of `pretrained`, or use the pre-initialized tokenizer passed.
         """
+        kwargs = {
+            "revision": revision,
+            "trust_remote_code": trust_remote_code,
+        }
+
+        # gguf format embeds tokenizer and is not compatible with hf tokenizer `use_fast` param
+        if gguf_file is not None:
+            kwargs["gguf_file"] = gguf_file
+        else:
+            kwargs["use_fast"] = use_fast_tokenizer
 
         if tokenizer:
             if isinstance(tokenizer, str):
                 self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-                    tokenizer,
-                    revision=revision,
-                    trust_remote_code=trust_remote_code,
-                    use_fast=use_fast_tokenizer,
+                    tokenizer, **kwargs
                 )
             else:
                 assert isinstance(
@@ -705,10 +723,7 @@ class HFLM(TemplateLM):
                 # get the HF hub name via accessor on model
                 model_name = self.model.name_or_path
             self.tokenizer = transformers.AutoTokenizer.from_pretrained(
-                model_name,
-                revision=revision,
-                trust_remote_code=trust_remote_code,
-                use_fast=use_fast_tokenizer,
+                model_name, **kwargs
             )
         return None
 
@@ -818,6 +833,12 @@ class HFLM(TemplateLM):
             **add_special_tokens,
         )
         if left_truncate_len:
+            original_lengths = encoding["input_ids"].size(1)
+            if original_lengths > left_truncate_len:
+                eval_logger.warn(
+                    f"Left truncation applied. Original sequence length was {original_lengths}, "
+                    f"truncating to last {left_truncate_len} tokens. Some content will be lost.",
+                )
             encoding["input_ids"] = encoding["input_ids"][:, -left_truncate_len:]
             encoding["attention_mask"] = encoding["attention_mask"][
                 :, -left_truncate_len:
@@ -886,16 +907,16 @@ class HFLM(TemplateLM):
         self, logits: torch.Tensor, contlen: int = None, inplen: int = None
     ) -> torch.Tensor:
         if self.backend == "causal":
-            assert (
-                contlen and inplen
-            ), "Must pass input len and cont. len to select scored logits for causal LM"
+            assert contlen and inplen, (
+                "Must pass input len and cont. len to select scored logits for causal LM"
+            )
             # discard right-padding.
             # also discard the input/context tokens. we'll only score continuations.
             logits = logits[inplen - contlen : inplen]
         elif self.backend == "seq2seq":
-            assert (
-                contlen and not inplen
-            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
+            assert contlen and not inplen, (
+                "Selecting scored logits for Seq2SeqLM requires only cont. len"
+            )
             # only discard right-padding.
             # the logits input to this fn only contain decoder-side tokens.
             logits = logits[:contlen]
@@ -905,8 +926,6 @@ class HFLM(TemplateLM):
     def loglikelihood_rolling(
         self, requests: List[Instance], disable_tqdm: bool = False
     ) -> List[float]:
-        loglikelihoods = []
-
         adaptive_batch_size = None
         if self.batch_size == "auto":
             # using rolling window with maximum context
@@ -915,10 +934,17 @@ class HFLM(TemplateLM):
             print(f"Determined Largest batch size: {batch_size}")
             adaptive_batch_size = batch_size
 
-        for (string,) in tqdm(
-            [req.args for req in requests], disable=(disable_tqdm or (self.rank != 0))
+        # First, collect all windows from all requests
+        all_windows = []  # List of (request_idx, window) tuples
+        request_window_counts = []  # Track number of windows per request
+
+        for req_idx, (string,) in enumerate(
+            tqdm(
+                [req.args for req in requests],
+                disable=(disable_tqdm or (self.rank != 0)),
+            )
         ):
-            rolling_token_windows = list(
+            rolling_token_windows: List[Tuple[List[int], List[int]]] = list(
                 map(
                     utils.make_disjoint_window,
                     utils.get_rolling_token_windows(
@@ -931,37 +957,55 @@ class HFLM(TemplateLM):
             )
 
             # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
-            rolling_token_windows = [(None,) + x for x in rolling_token_windows]
-
-            pad_amnt = 0
-            if self.world_size > 1:
-                # We pad out the external document-level iterator so the inner iterator doesn't hang
-                mytensor = torch.tensor(len(rolling_token_windows), device=self.device)
-                gathered = (
-                    self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
-                )
+            windows = [(None,) + x for x in rolling_token_windows]
 
-                pad_amnt = max(gathered) - gathered[self.rank]
-                if pad_amnt > 0:
-                    rolling_token_windows += pad_amnt * [rolling_token_windows[0]]
+            # Store windows with their request index
+            all_windows.extend((req_idx, window) for window in windows)
+            request_window_counts.append(len(windows))
 
-            string_nll = self._loglikelihood_tokens(
-                requests=rolling_token_windows,
-                disable_tqdm=True,
-                override_bs=adaptive_batch_size,
+        # Handle distributed case padding
+        pad_amnt = 0
+        if self.world_size > 1:
+            mytensor = torch.tensor(len(all_windows), device=self.device)
+            gathered = self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
+            pad_amnt = max(gathered) - gathered[self.rank]
+            if pad_amnt > 0:
+                all_windows += pad_amnt * [all_windows[0]]
+
+        all_nlls = []
+        batch_size = adaptive_batch_size or self.batch_size
+        for i in range(0, len(all_windows), batch_size):
+            batch = all_windows[i : i + batch_size]
+            # Extract just the windows for processing, keeping track of request indices
+            batch_indices, batch_windows = zip(*batch)
+
+            batch_nlls = self._loglikelihood_tokens(
+                requests=batch_windows,
+                disable_tqdm=False,
+                override_bs=len(batch_windows),
             )
+            # Store results with their request indices
+            all_nlls.extend(zip(batch_indices, batch_nlls))
 
-            if (self.world_size > 1) and (pad_amnt > 0):
-                string_nll = [x[0] for x in string_nll[:-pad_amnt]]
-            else:
-                # discard is_greedy
-                string_nll = [x[0] for x in string_nll]
+        # Remove padding if necessary
+        if (self.world_size > 1) and (pad_amnt > 0):
+            all_nlls = all_nlls[:-pad_amnt]
 
-            string_nll = sum(string_nll)
-            loglikelihoods.append(string_nll)
-
-            # cache this loglikelihood_rolling request
-            self.cache_hook.add_partial("loglikelihood_rolling", (string,), string_nll)
+        # Reconstruct per-request loglikelihoods
+        loglikelihoods = []
+        current_idx = 0
+        for window_count in request_window_counts:
+            # Get all nlls for this request
+            request_nlls = all_nlls[current_idx : current_idx + window_count]
+            # Sum up the nlls for this request (discarding is_greedy)
+            request_total = sum(nll[0] for _, nll in request_nlls)
+            loglikelihoods.append(request_total)
+            current_idx += window_count
+
+            string = requests[len(loglikelihoods) - 1].args[0]
+            self.cache_hook.add_partial(
+                "loglikelihood_rolling", (string,), request_total
+            )
 
         return loglikelihoods
 
@@ -1073,6 +1117,13 @@ class HFLM(TemplateLM):
 
                 # when too long to fit in context, truncate from the left
                 if self.backend == "causal":
+                    total_length = len(context_enc) + len(continuation_enc)
+                    if total_length > self.max_length + 1:
+                        eval_logger.warn(
+                            f"Combined length of context ({len(context_enc)}) and continuation ({len(continuation_enc)}) "
+                            f"exceeds model's maximum length ({self.max_length}). "
+                            f"Truncating {total_length - self.max_length + 1} tokens from the left."
+                        )
                     inp = torch.tensor(
                         (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
                         dtype=torch.long,
@@ -1280,6 +1331,9 @@ class HFLM(TemplateLM):
             if self.backend == "causal":
                 # max len for inputs = max length, minus room to generate the max new tokens
                 max_ctx_len = self.max_length - max_gen_toks
+                assert max_ctx_len > 0, (
+                    f"Invalid configuration: requested max tokens to generate ({max_gen_toks}) must be less than model's maximum sequence length ({self.max_length})."
+                )
             elif self.backend == "seq2seq":
                 # max len for inputs = encoder's whole max_length
                 max_ctx_len = self.max_length
@@ -1330,13 +1384,18 @@ class HFLM(TemplateLM):
 
         return res
 
-    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+    def apply_chat_template(
+        self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
+    ) -> str:
         """
         Method to apply a chat template to a list of chat history between user and model.
         """
         try:
             chat_templated = self.tokenizer.apply_chat_template(
-                chat_history, tokenize=False, add_generation_prompt=True
+                chat_history,
+                tokenize=False,
+                add_generation_prompt=add_generation_prompt,
+                continue_final_message=not add_generation_prompt,
             )
         except jinja2.exceptions.TemplateError:
             eval_logger.warning(
@@ -1344,7 +1403,10 @@ class HFLM(TemplateLM):
             )
             chat_history = [msg for msg in chat_history if msg["role"] != "system"]
             chat_templated = self.tokenizer.apply_chat_template(
-                chat_history, tokenize=False, add_generation_prompt=True
+                chat_history,
+                tokenize=False,
+                add_generation_prompt=add_generation_prompt,
+                continue_final_message=not add_generation_prompt,
             )
 
         return chat_templated

lm_eval/models/neuron_optimum.py

@@ -206,7 +206,7 @@ class NEURON_HF(TemplateLM):
                     "Only float16/bfloat16/float32 are supported."
                 )
 
-            print(f"{'='*20} \n exporting model to neuron")
+            print(f"{'=' * 20} \n exporting model to neuron")
             self.model = CustomNeuronModelForCausalLM.from_pretrained(
                 pretrained,
                 revision=revision,
@@ -220,19 +220,17 @@ class NEURON_HF(TemplateLM):
             )
             neuron_config = self.model.config.neuron
             print(
-                f"SUCCESS: neuron model exported with config {neuron_config}. \n {'='*20}"
+                f"SUCCESS: neuron model exported with config {neuron_config}. \n {'=' * 20}"
             )
         else:
-            print(
-                f"{'='*20} \n loading neuron model with config" f" {neuron_config}..."
-            )
+            print(f"{'=' * 20} \n loading neuron model with config {neuron_config}...")
             self.model = CustomNeuronModelForCausalLM.from_pretrained(
                 pretrained,
                 revision=revision,
                 trust_remote_code=trust_remote_code,
                 low_cpu_mem_usage=low_cpu_mem_usage,
             )
-            print(f"SUCCESS: neuron model loaded. \n {'='*20}")
+            print(f"SUCCESS: neuron model loaded. \n {'=' * 20}")
 
         self.truncation = truncation
 
@@ -353,9 +351,9 @@ class NEURON_HF(TemplateLM):
             )
 
     def _select_cont_toks(self, logits, contlen=None, inplen=None):
-        assert (
-            contlen and inplen
-        ), "Must pass input len and cont. len to select scored logits for causal LM"
+        assert contlen and inplen, (
+            "Must pass input len and cont. len to select scored logits for causal LM"
+        )
         # discard right-padding.
         # also discard the input/context tokens. we'll only score continuations.
         logits = logits[inplen - contlen : inplen]

lm_eval/models/openai_completions.py

 import os
 from functools import cached_property
+from operator import itemgetter
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from lm_eval.api.registry import register_model
@@ -68,7 +69,9 @@ class LocalCompletionsAPI(TemplateAPI):
         if not isinstance(outputs, list):
             outputs = [outputs]
         for out in outputs:
-            for choice, ctxlen in zip(out["choices"], ctxlens):
+            for choice, ctxlen in zip(
+                sorted(out["choices"], key=itemgetter("index")), ctxlens
+            ):
                 assert ctxlen > 0, "Context length must be greater than 0"
                 logprobs = sum(choice["logprobs"]["token_logprobs"][ctxlen:-1])
                 tokens_logprobs = choice["logprobs"]["token_logprobs"][ctxlen:-1]
@@ -87,8 +90,10 @@ class LocalCompletionsAPI(TemplateAPI):
         if not isinstance(outputs, list):
             outputs = [outputs]
         for out in outputs:
+            tmp = [None] * len(out["choices"])
             for choices in out["choices"]:
-                res.append(choices["text"])
+                tmp[choices["index"]] = choices["text"]
+            res = res + tmp
         return res
 
     @property
@@ -129,9 +134,9 @@ class LocalChatCompletion(LocalCompletionsAPI):
         eos=None,
         **kwargs,
     ) -> dict:
-        assert (
-            type(messages) is not str
-        ), "chat-completions require the --apply_chat_template flag."
+        assert type(messages) is not str, (
+            "chat-completions require the --apply_chat_template flag."
+        )
         gen_kwargs.pop("do_sample", False)
         if "max_tokens" in gen_kwargs:
             max_tokens = gen_kwargs.pop("max_tokens")
@@ -157,8 +162,10 @@ class LocalChatCompletion(LocalCompletionsAPI):
         if not isinstance(outputs, list):
             outputs = [outputs]
         for out in outputs:
+            tmp = [None] * len(out["choices"])
             for choices in out["choices"]:
-                res.append(choices["message"]["content"])
+                tmp[choices["index"]] = choices["message"]["content"]
+            res = res + tmp
         return res
 
     def tok_encode(
@@ -201,13 +208,12 @@ class OpenAICompletionsAPI(LocalCompletionsAPI):
         return key
 
     def loglikelihood(self, requests, **kwargs):
-        assert (
-            self.model
-            in [
-                "babbage-002",
-                "davinci-002",
-            ]
-        ), f"Prompt loglikelihoods are only supported by OpenAI's API for {['babbage-002', 'davinci-002']}."
+        assert self.model in [
+            "babbage-002",
+            "davinci-002",
+        ], (
+            f"Prompt loglikelihoods are only supported by OpenAI's API for {['babbage-002', 'davinci-002']}."
+        )
         return super().loglikelihood(requests, **kwargs)
 
     def chat_template(self, chat_template: Union[bool, str] = False) -> Optional[str]:
@@ -258,9 +264,9 @@ class OpenAIChatCompletion(LocalChatCompletion):
         eos="<|endoftext|>",
         **kwargs,
     ) -> dict:
-        assert (
-            type(messages) is not str
-        ), "chat-completions require the --apply_chat_template flag."
+        assert type(messages) is not str, (
+            "chat-completions require the --apply_chat_template flag."
+        )
         gen_kwargs.pop("do_sample", False)
         if "max_tokens" in gen_kwargs:
             max_tokens = gen_kwargs.pop("max_tokens")

lm_eval/models/optimum_ipex.py

+from importlib.util import find_spec
+
+from lm_eval import utils
+from lm_eval.api.registry import register_model
+from lm_eval.models.huggingface import HFLM
+from lm_eval.models.utils import get_dtype
+
+
+eval_logger = utils.eval_logger
+
+
+@register_model("ipex")
+class IPEXLM(HFLM):
+    """
+    using the HuggingFace transformers + optimum-intel ipex backend, can run on intel cpu and intel gpu
+    """
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        if "backend" in kwargs:
+            # currently only supports causal models
+            assert kwargs["backend"] == "causal", (
+                "Currently, only IPEXModelForCausalLM is supported."
+            )
+
+        super().__init__(
+            backend=kwargs.pop("backend", "causal"),
+            **kwargs,
+        )
+
+    def _create_model(
+        self,
+        pretrained: str,
+        revision="main",
+        dtype="auto",
+        trust_remote_code=False,
+        # arguments used for splitting a model across GPUs naively.
+        # only used if `parallelize=True`.
+        # (accelerate naive PP (device_map) options)
+        parallelize=False,
+        gpus=None,
+        max_memory_per_gpu=None,
+        max_cpu_memory=None,
+        offload_folder="./offload",
+        # PEFT, delta weights and quantization options
+        peft=None,
+        delta=None,
+        autogptq=False,
+        gptqmodel=False,
+        **kwargs,
+    ) -> None:
+        if not find_spec("optimum"):
+            raise ModuleNotFoundError(
+                "package `optimum` is not installed. Please install it via `pip install optimum[ipex]`"
+            )
+        else:
+            from optimum.intel import IPEXModelForCausalLM
+
+        model_kwargs = kwargs if kwargs else {}
+        model_kwargs.update(
+            self._get_accelerate_args(
+                parallelize=parallelize,
+                device_map=kwargs.get("device_map", None),
+                max_memory_per_gpu=max_memory_per_gpu,
+                max_cpu_memory=max_cpu_memory,
+                offload_folder=offload_folder,
+                gpus=gpus,
+            )
+        )
+
+        self._model = IPEXModelForCausalLM.from_pretrained(
+            pretrained,
+            revision=revision,
+            torch_dtype=get_dtype(dtype),
+            trust_remote_code=trust_remote_code,
+            **model_kwargs,
+        )

lm_eval/models/optimum_lm.py

@@ -29,9 +29,9 @@ class OptimumLM(HFLM):
     ) -> None:
         if "backend" in kwargs:
             # optimum currently only supports causal models
-            assert (
-                kwargs["backend"] == "causal"
-            ), "Currently, only OVModelForCausalLM is supported."
+            assert kwargs["backend"] == "causal", (
+                "Currently, only OVModelForCausalLM is supported."
+            )
 
         self.openvino_device = device
 

lm_eval/models/utils.py

@@ -155,9 +155,9 @@ def pad_and_concat(
     length in the batch. Used for batching inputs and continuations in
     seq2seq models.
     """
-    assert (
-        padding_side == "left" or padding_side == "right"
-    ), f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
+    assert padding_side == "left" or padding_side == "right", (
+        f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
+    )
 
     for i, tensor in enumerate(tensors):
         if len(tensor.shape) == 2:

lm_eval/models/vllm_causallms.py

@@ -76,9 +76,9 @@ class VLLM(TemplateLM):
             )
 
         assert "cuda" in device or device is None, "vLLM only supports CUDA"
-        assert (
-            max_length is None or max_model_len is None
-        ), "Either max_length or max_model_len may be provided, but not both"
+        assert max_length is None or max_model_len is None, (
+            "Either max_length or max_model_len may be provided, but not both"
+        )
 
         self._max_length = max_model_len if max_model_len is not None else max_length
         self.tensor_parallel_size = int(tensor_parallel_size)
@@ -102,7 +102,7 @@ class VLLM(TemplateLM):
         self.batch_size = (
             "auto"
             if isinstance(batch_size, str) and "auto" in batch_size
-            else batch_size
+            else int(batch_size)
         )
         if self.data_parallel_size <= 1:
             self.model = LLM(**self.model_args)
@@ -142,9 +142,9 @@ class VLLM(TemplateLM):
         self._max_gen_toks = max_gen_toks
 
         if lora_local_path is not None:
-            assert parse_version(version("vllm")) > parse_version(
-                "0.3.0"
-            ), "lora adapters only compatible with vllm > v0.3.0."
+            assert parse_version(version("vllm")) > parse_version("0.3.0"), (
+                "lora adapters only compatible with vllm > v0.3.0."
+            )
             self.lora_request = LoRARequest("finetuned", 1, lora_local_path)
         else:
             self.lora_request = None
@@ -184,14 +184,21 @@ class VLLM(TemplateLM):
     def max_gen_toks(self):
         return self._max_gen_toks
 
-    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+    def apply_chat_template(
+        self, chat_history: List[Dict[str, str]], add_generation_prompt: bool = True
+    ) -> str:
         """
         Method to apply a chat template to a list of chat history between user and model.
         """
-        return self.tokenizer.apply_chat_template(
-            chat_history, tokenize=False, add_generation_prompt=True
+        chat_templated = self.tokenizer.apply_chat_template(
+            chat_history,
+            tokenize=False,
+            add_generation_prompt=add_generation_prompt,
+            continue_final_message=not add_generation_prompt,
         )
 
+        return chat_templated
+
     @property
     def tokenizer_name(self) -> str:
         return self.tokenizer.name_or_path.replace("/", "__")
@@ -281,10 +288,21 @@ class VLLM(TemplateLM):
     def loglikelihood_rolling(
         self, requests: List[Instance], disable_tqdm: bool = False
     ) -> List[float]:
-        loglikelihoods = []
-
-        for (string,) in tqdm([req.args for req in requests], disable=disable_tqdm):
-            rolling_token_windows = list(
+        adaptive_batch_size = None
+        if self.batch_size == "auto":
+            adaptive_batch_size = len(requests)
+
+        # First, collect all windows from all requests
+        all_windows = []  # List of (request_idx, window) tuples
+        request_window_counts = []  # Track number of windows per request
+
+        for req_idx, (string,) in enumerate(
+            tqdm(
+                [req.args for req in requests],
+                disable=(disable_tqdm or (self.rank != 0)),
+            )
+        ):
+            rolling_token_windows: List[Tuple[List[int], List[int]]] = list(
                 map(
                     make_disjoint_window,
                     get_rolling_token_windows(
@@ -297,20 +315,42 @@ class VLLM(TemplateLM):
                 )
             )
 
-            rolling_token_windows = [(None,) + x for x in rolling_token_windows]
+            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
+            windows = [(None,) + x for x in rolling_token_windows]
 
-            string_nll = self._loglikelihood_tokens(
-                rolling_token_windows,
-            )
+            # Store windows with their request index
+            all_windows.extend((req_idx, window) for window in windows)
+            request_window_counts.append(len(windows))
 
-            # discard is_greedy
-            string_nll = [x[0] for x in string_nll]
+        all_nlls = []
+        batch_size = adaptive_batch_size or int(self.batch_size)
+        for i in range(0, len(all_windows), batch_size):
+            batch = all_windows[i : i + batch_size]
+            # Extract just the windows for processing, keeping track of request indices
+            batch_indices, batch_windows = zip(*batch)
 
-            string_nll = sum(string_nll)
-            loglikelihoods.append(string_nll)
+            batch_nlls = self._loglikelihood_tokens(
+                requests=batch_windows,
+                disable_tqdm=False,
+            )
+            # Store results with their request indices
+            all_nlls.extend(zip(batch_indices, batch_nlls))
 
-            # cache this loglikelihood_rolling request
-            self.cache_hook.add_partial("loglikelihood_rolling", (string,), string_nll)
+        # Reconstruct per-request loglikelihoods
+        loglikelihoods = []
+        current_idx = 0
+        for window_count in request_window_counts:
+            # Get all nlls for this request
+            request_nlls = all_nlls[current_idx : current_idx + window_count]
+            # Sum up the nlls for this request (discarding is_greedy)
+            request_total = sum(nll[0] for _, nll in request_nlls)
+            loglikelihoods.append(request_total)
+            current_idx += window_count
+
+            string = requests[len(loglikelihoods) - 1].args[0]
+            self.cache_hook.add_partial(
+                "loglikelihood_rolling", (string,), request_total
+            )
 
         return loglikelihoods
 

lm_eval/models/vllm_vlms.py

@@ -144,7 +144,9 @@ class VLLM_VLM(VLLM):
             )
         return outputs
 
-    def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
+    def apply_chat_template(
+        self, chat_history: List[Dict[str, str]], add_generation_prompt=True
+    ) -> str:
         self.chat_applied = True
         if not self.interleave:
             for content in chat_history:
@@ -194,7 +196,9 @@ class VLLM_VLM(VLLM):
                     )
 
         return self.processor.apply_chat_template(
-            chat_history, add_generation_prompt=True
+            chat_history,
+            add_generation_prompt=add_generation_prompt,
+            continue_final_message=not add_generation_prompt,
         )
 
     def generate_until(

lm_eval/tasks/arabicmmlu/_arabicmmlu.yaml

@@ -9,4 +9,4 @@ aggregate_metric_list:
   - metric: acc
     weight_by_size: True
 metadata:
-  version: 0
+  version: 1

lm_eval/tasks/arabicmmlu/_arabicmmlu_humanities.yaml

@@ -6,4 +6,4 @@ aggregate_metric_list:
   - metric: acc
     weight_by_size: True
 metadata:
-  version: 0
+  version: 1

lm_eval/tasks/arabicmmlu/_arabicmmlu_language.yaml

@@ -6,4 +6,4 @@ aggregate_metric_list:
   - metric: acc
     weight_by_size: True
 metadata:
-  version: 0
+  version: 1