Merge branch 'main' into llama

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

.github/workflows/unit_tests.yml

@@ -22,10 +22,10 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v5
       with:
-        python-version: 3.8
+        python-version: 3.9
         cache: pip
         cache-dependency-path: pyproject.toml
     - name: Pre-Commit
@@ -42,7 +42,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [ "3.8", "3.9", "3.10", "3.11" ]
+        python-version: ["3.9", "3.10", "3.11", "3.12" ]
     timeout-minutes: 30
     steps:
     - name: Checkout Code
@@ -75,15 +75,16 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Set up Python 3.8
+    - name: Set up Python 3.9
       uses: actions/setup-python@v5
       with:
-        python-version: 3.8
+        python-version: 3.9
         cache: pip
         cache-dependency-path: pyproject.toml
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install -e '.[dev,optimum,deepsparse,sparseml,api]' --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -U transformers peft
     - name: Test with pytest
       run: python -m pytest tests/models --showlocals -s -vv

.pre-commit-config.yaml

@@ -29,7 +29,7 @@ repos:
       - id: mixed-line-ending
         args: [--fix=lf]
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.7.4
+    rev: v0.9.2
     hooks:
       # Run the linter.
       - id: ruff

README.md

@@ -270,6 +270,7 @@ Note that for externally hosted models, configs such as `--device` which relate
 | vLLM                                                                                                                      | :heavy_check_mark:       | `vllm`                                              | [Most HF Causal Language Models](https://docs.vllm.ai/en/latest/models/supported_models.html)                                                                                                                                                                                                                                                              | `generate_until`, `loglikelihood`, `loglikelihood_rolling` |
 | Mamba                       | :heavy_check_mark:       | `mamba_ssm`                                         | [Mamba architecture Language Models via the `mamba_ssm` package](https://huggingface.co/state-spaces)                                                                                                                                                                                                                                                      | `generate_until`, `loglikelihood`, `loglikelihood_rolling`                             |
 | Huggingface Optimum (Causal LMs)    | ✔️         | `openvino`                                          | Any decoder-only AutoModelForCausalLM converted with Huggingface Optimum into OpenVINO™ Intermediate Representation (IR) format                                                                                                                                                                                                                            |  `generate_until`, `loglikelihood`, `loglikelihood_rolling`                         | ...                                                      |
+| Huggingface Optimum-intel IPEX (Causal LMs)    | ✔️         | `ipex`                                          | Any decoder-only AutoModelForCausalLM                                                                                                                                                                                                                      |  `generate_until`, `loglikelihood`, `loglikelihood_rolling`                         | ...                                                      |
 | Neuron via AWS Inf2 (Causal LMs)    | ✔️         | `neuronx`                                           | Any decoder-only AutoModelForCausalLM supported to run on [huggingface-ami image for inferentia2](https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2)                                                                                                                                                                                            |  `generate_until`, `loglikelihood`, `loglikelihood_rolling`                         | ...                                                      |
 | [Neural Magic DeepSparse](https://github.com/neuralmagic/deepsparse)    | ✔️         | `deepsparse`                                        | Any LM from [SparseZoo](https://sparsezoo.neuralmagic.com/) or on [HF Hub with the "deepsparse" tag](https://huggingface.co/models?other=deepsparse)                                                                                                                                                                                                       |  `generate_until`, `loglikelihood`                         | ...                                                      |
 | [Neural Magic SparseML](https://github.com/neuralmagic/sparseml)    | ✔️         | `sparseml`                                          | Any decoder-only AutoModelForCausalLM from [SparseZoo](https://sparsezoo.neuralmagic.com/) or on [HF Hub](https://huggingface.co/neuralmagic). Especially useful for models with quantization like [`zoo:llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized`](https://sparsezoo.neuralmagic.com/models/llama2-7b-gsm8k_llama2_pretrain-pruned60_quantized) |  `generate_until`, `loglikelihood`, `loglikelihood_rolling`                         | ...                                                      |
@@ -492,6 +493,7 @@ Extras dependencies can be installed via `pip install -e ".[NAME]"`
 | hf_transfer     | For speeding up HF Hub file downloads        |
 | ifeval          | For running the IFEval task                  |
 | ibm_watsonx_ai  | For using IBM watsonx.ai model apis          |
+| ipex            | For running on optimum-intel ipex backend    |
 | neuronx         | For running on AWS inf2 instances            |
 | mamba           | For loading Mamba SSM models                 |
 | math            | For running math task answer checking        |

docs/interface.md

@@ -58,7 +58,7 @@ This mode supports a number of command-line arguments, the details of which can
 
 * `--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.
 
-* `--wandb_args`:  Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list [here](https://docs.wandb.ai/ref/python/init). e.g., ```--wandb_args project=test-project,name=test-run```
+* `--wandb_args`:  Tracks logging to Weights and Biases for evaluation runs and includes args passed to `wandb.init`, such as `project` and `job_type`. Full list [here](https://docs.wandb.ai/ref/python/init). e.g., ```--wandb_args project=test-project,name=test-run```. Also allows for the passing of the step to log things at (passed to `wandb.run.log`), e.g., `--wandb_args step=123`.
 
 * `--hf_hub_log_args` : Logs evaluation results to Hugging Face Hub. Accepts a string with the arguments separated by commas. Available arguments:
     * `hub_results_org` - organization name on Hugging Face Hub, e.g., `EleutherAI`. If not provided, the results will be pushed to the owner of the Hugging Face token,

docs/new_task_guide.md

@@ -190,7 +190,8 @@ doc_to_target: "{{answer}}"
 ```
 
 
-**Important**: we now add `target_delimiter` between input and target which defaults to " ", such that the full input-output string is `doc_to_target(doc) + target_delimiter + doc_to_text(doc)`. `doc_to_text` and `doc_to_target` should not contain trailing right or left whitespace, respectively.
+> [!WARNING]
+> We add `target_delimiter` between input and target which defaults to " ", such that the full input-output string is `doc_to_text(doc) + target_delimiter + doc_to_target(doc)`. `doc_to_text` and `doc_to_target` should not contain trailing right or left whitespace, respectively. For multiple choice the target will be each choice index concatenated with the delimiter.
 
 
 #### Multiple choice format
@@ -206,7 +207,7 @@ doc_to_choice: "{{[distractor1, distractor2, distractor3, correct_answer]}}"
 ```
 Task implementers are thus able to decide what the answer choices should be for a document, and what prompt format to use.
 
-The label index can also be sourced from a feature directly. For example in `superglue/boolq`, the label index if defined in the feature `label`. We can set `doc_to_target` as simply `label`. The options or verbalizers can be written in a the form of a list `["no", "yes"]` that will correspond to the label index.
+The label index can also be sourced from a feature directly. For example in `superglue/boolq`, the label index if defined in the feature `label`. We can set `doc_to_target` as simply `label`. The options or verbalizers can be written in the form of a list `["no", "yes"]` that will correspond to the label index.
 
 ```yaml
 doc_to_text: "{{passage}}\nQuestion: {{question}}?\nAnswer:"

docs/task_guide.md

@@ -37,6 +37,7 @@ Prompting / in-context formatting options:
 - **doc_to_choice** (`Union[Callable, str]`, *optional*) — Jinja2 template, string, or function to process a sample into a list of possible string choices for `multiple_choice` tasks. Left undefined for `generate_until` tasks.
 - **fewshot_delimiter** (`str`, *optional*, defaults to "\n\n") — String to insert between few-shot examples.
 - **target_delimiter** (`str`, *optional*, defaults to `" "`) — String to insert between input and target output for the datapoint being tested.
+- **assistant_prefill** (`str`, *optional*) — String to append after the <|assistant|> token. For example, if the task is to generate a question, the assistant_prefill could be "The answer is: " to prompt the model to generate an answer to the question. If not using a chat template then this string will be appended to the end of the prompt.
 
 Runtime configuration options:
 - **num_fewshot** (`int`, *optional*, defaults to 0) — Number of few-shot examples before the input.

lm_eval/__main__.py

@@ -257,6 +257,11 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="Sets trust_remote_code to True to execute code to create HF Datasets from the Hub",
     )
+    parser.add_argument(
+        "--confirm_run_unsafe_code",
+        action="store_true",
+        help="Confirm that you understand the risks of running unsafe code for tasks that require it",
+    )
     return parser
 
 
@@ -404,6 +409,7 @@ def cli_evaluate(args: Union[argparse.Namespace, None] = None) -> None:
         numpy_random_seed=args.seed[1],
         torch_random_seed=args.seed[2],
         fewshot_random_seed=args.seed[3],
+        confirm_run_unsafe_code=args.confirm_run_unsafe_code,
         **request_caching_args,
     )
 

lm_eval/api/group.py

@@ -112,6 +112,4 @@ def group_name(self) -> Any:
         return self._config.group
 
     def __repr__(self):
-        return (
-            f"ConfigurableGroup(group={self.group}," f"group_alias={self.group_alias})"
-        )
+        return f"ConfigurableGroup(group={self.group},group_alias={self.group_alias})"

lm_eval/api/metrics.py

@@ -527,9 +527,9 @@ def pooled_sample_stderr(stderrs: List[float], sizes: List[int]):
 
 
 def combined_sample_stderr(stderrs: List[float], sizes: List[int], metrics=None):
-    assert (
-        metrics is not None
-    ), "Need to pass a list of each subtask's metric for this stderr aggregation"
+    assert metrics is not None, (
+        "Need to pass a list of each subtask's metric for this stderr aggregation"
+    )
     assert len(stderrs) == len(sizes) and len(sizes) == len(metrics)
 
     # See https://github.com/EleutherAI/lm-evaluation-harness/pull/1390 for more documentation.

lm_eval/api/model.py

@@ -113,13 +113,17 @@ def generate_until(self, requests) -> List[str]:
         """
         pass
 
-    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:
         """
         Defines how to transform few-shot examples provided as chat history into a format that can be used as input to the LM.
 
         :param chat_history: list[dict[str, str]]
             A list of dictionaries with keys 'role' and 'content'.
             Values are strings representing the role name and the content of the message, respectively.
+        :param add_generation_prompt: bool
+            Whether to append an assistant gen prefix (for e.g. <|assistant|>) to the assistant messages in the chat history. False if prefilling an assistant message.
         :return: str
             A string representing the chat history in a format that can be used as input to the LM.
         """

lm_eval/api/registry.py

@@ -17,13 +17,13 @@ def register_model(*names):
 
     def decorate(cls):
         for name in names:
-            assert issubclass(
-                cls, LM
-            ), f"Model '{name}' ({cls.__name__}) must extend LM class"
+            assert issubclass(cls, LM), (
+                f"Model '{name}' ({cls.__name__}) must extend LM class"
+            )
 
-            assert (
-                name not in MODEL_REGISTRY
-            ), f"Model named '{name}' conflicts with existing model! Please register with a non-conflicting alias instead."
+            assert name not in MODEL_REGISTRY, (
+                f"Model named '{name}' conflicts with existing model! Please register with a non-conflicting alias instead."
+            )
 
             MODEL_REGISTRY[name] = cls
         return cls
@@ -48,9 +48,9 @@ def get_model(model_name):
 
 def register_task(name):
     def decorate(fn):
-        assert (
-            name not in TASK_REGISTRY
-        ), f"task named '{name}' conflicts with existing registered task!"
+        assert name not in TASK_REGISTRY, (
+            f"task named '{name}' conflicts with existing registered task!"
+        )
 
         TASK_REGISTRY[name] = fn
         ALL_TASKS.add(name)
@@ -104,9 +104,9 @@ def decorate(fn):
         ]:
             if key in args:
                 value = args[key]
-                assert (
-                    value not in registry
-                ), f"{key} named '{value}' conflicts with existing registered {key}!"
+                assert value not in registry, (
+                    f"{key} named '{value}' conflicts with existing registered {key}!"
+                )
 
                 if key == "metric":
                     registry[name] = fn
@@ -140,9 +140,9 @@ def get_metric(name: str, hf_evaluate_metric=False) -> Callable:
 
 def register_aggregation(name: str):
     def decorate(fn):
-        assert (
-            name not in AGGREGATION_REGISTRY
-        ), f"aggregation named '{name}' conflicts with existing registered aggregation!"
+        assert name not in AGGREGATION_REGISTRY, (
+            f"aggregation named '{name}' conflicts with existing registered aggregation!"
+        )
 
         AGGREGATION_REGISTRY[name] = fn
         return fn