Merge branch 'big-refactor' into flan-benchmark

lm_eval/models/dummy.py

@@ -5,7 +5,7 @@ from lm_eval.api.registry import register_model
 
 @register_model("dummy")
 class DummyLM(LM):
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
 
     @classmethod

lm_eval/models/huggingface.py

@@ -22,7 +22,7 @@ from lm_eval.api.registry import register_model
 
 from lm_eval.utils import MultiTokenEOSCriteria, stop_sequences_criteria
 
-from accelerate import Accelerator, find_executable_batch_size
+from accelerate import Accelerator, find_executable_batch_size, DistributedType
 from typing import List, Optional, Union
 
 
@@ -94,7 +94,7 @@ class HFLM(LM):
         bnb_4bit_compute_dtype: Optional[Union[str, torch.dtype]] = None,
         gptq: Optional[Union[bool, str]] = False,
         gptq_use_triton: Optional[bool] = False,
-    ):
+    ) -> None:
         super().__init__()
 
         assert isinstance(device, str)
@@ -294,6 +294,13 @@ class HFLM(LM):
                     eval_logger.info(
                         "Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes`. If the desired GPU is being used, this message is safe to ignore."
                     )
+            else:
+                assert accelerator.distributed_type in [
+                    DistributedType.FSDP,
+                    DistributedType.MULTI_GPU,
+                ], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+                if accelerator.distributed_type == DistributedType.FSDP:
+                    self._model = accelerator.prepare(self.model)
                 else:
                     self._model = accelerator.prepare_model(
                         self.model, evaluation_mode=True
@@ -340,7 +347,7 @@ class HFLM(LM):
         return self._DEFAULT_MAX_LENGTH
 
     @property
-    def max_gen_toks(self):
+    def max_gen_toks(self) -> int:
         return 256
 
     @property
@@ -359,7 +366,7 @@ class HFLM(LM):
     def world_size(self):
         return self._world_size
 
-    def _detect_batch_size(self, requests=None, pos=0):
+    def _detect_batch_size(self, requests=None, pos: int = 0):
         if requests:
             _, context_enc, continuation_enc = requests[pos]
             max_length = len(
@@ -428,9 +435,9 @@ class HFLM(LM):
     def tok_batch_encode(
         self,
         strings: List[str],
-        padding_side="left",
-        left_truncate_len=None,
-        truncation=False,
+        padding_side: str = "left",
+        left_truncate_len: int = None,
+        truncation: bool = False,
     ):
         # encode a batch of strings. converts to tensors and pads automatically, unlike tok_encode.
         old_padding_side = self.tokenizer.padding_side
@@ -611,7 +618,9 @@ class HFLM(LM):
 
         return loglikelihoods
 
-    def _loglikelihood_tokens(self, requests, disable_tqdm=False, override_bs=None):
+    def _loglikelihood_tokens(
+        self, requests, disable_tqdm: bool = False, override_bs=None
+    ):
         # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
         res = []
 

lm_eval/models/openai_completions.py

@@ -69,7 +69,7 @@ class OpenaiCompletionsLM(LM):
         engine: str = "text-davinci-003",
         truncate: bool = False,
         batch_size: int = 1,
-    ):
+    ) -> None:
         """
 
         :param engine: str
@@ -99,12 +99,12 @@ class OpenaiCompletionsLM(LM):
         return self.end_of_text_token_id
 
     @property
-    def max_length(self):
+    def max_length(self) -> int:
         # Note: the OpenAI API supports up to 2049 tokens, with the first token being the first input token
         return 2048
 
     @property
-    def max_gen_toks(self):
+    def max_gen_toks(self) -> int:
         return 256
 
     @property
@@ -152,7 +152,7 @@ class OpenaiCompletionsLM(LM):
         return self._loglikelihood_tokens(new_reqs)
 
     def _loglikelihood_tokens(
-        self, requests, disable_tqdm=False
+        self, requests, disable_tqdm: bool = False
     ) -> List[Tuple[float, bool]]:
         res = []
 

lm_eval/models/textsynth.py

@@ -41,7 +41,7 @@ def textsynth_completion(**kwargs):
 
 @register_model("textsynth")
 class TextSynthLM(LM):
-    def __init__(self, engine, truncate=False):
+    def __init__(self, engine, truncate: bool = False) -> None:
         """
         :param engine: str
             TextSynth API engine (e.g. `gptj_6B`)
@@ -62,12 +62,12 @@ class TextSynthLM(LM):
         raise NotImplementedError()
 
     @property
-    def max_length(self):
+    def max_length(self) -> int:
         # NOTE: Turn on truncation to avoid errors on long inputs.
         return 2048
 
     @property
-    def max_gen_toks(self):
+    def max_gen_toks(self) -> int:
         return 256
 
     @property

lm_eval/prompts/__init__.py

@@ -5,7 +5,7 @@ from lm_eval.logger import eval_logger
 # Stores prompts in a dictionary indexed by 2 levels:
 # prompt category name, and prompt name.
 # This allows us to access prompts
-PROMPT_REGISTRY = {
+PROMPT_REGISTRY: dict[str, dict[str, str]] = {
     "qa-basic": {
         "question-newline-answer": "Question: {{question}}\nAnswer:",
         "q-newline-a": "Q: {{question}}\nA:",
@@ -13,7 +13,7 @@ PROMPT_REGISTRY = {
 }
 
 
-def get_prompt(prompt_id: str, dataset_name=None, subset_name=None):
+def get_prompt(prompt_id: str, dataset_name: str = None, subset_name: str = None):
     # unpack prompt name
     category_name, prompt_name = prompt_id.split(":")
     if subset_name is None:

lm_eval/tasks/README.md

@@ -29,7 +29,7 @@ Boxes should be checked iff tasks are implemented in the refactor and tested for
 - [x] HeadQA
 - [x] MathQA
 - [x] WebQs
-- [ ] WSC273 (Lintang)
+- [x] WSC273
 - [x] Winogrande
 - [x] ANLI
 - [x] Hendrycks Ethics (missing some tasks/metrics, see PR 660: <https://github.com/EleutherAI/lm-evaluation-harness/pull/660> for more info)
@@ -42,7 +42,7 @@ Boxes should be checked iff tasks are implemented in the refactor and tested for
 - [ ] GSM8k
 - [x] Arithmetic
 - [ ] MMMLU (Hailey)
-- [ ] Translation (WMT) suite (Hailey)
+- [x] Translation (WMT) suite
 - [x] Unscramble
 - [x] ~~Pile (perplexity)~~
 - [x] BLiMP

lm_eval/tasks/__init__.py

@@ -15,7 +15,7 @@ from lm_eval.api.registry import (
 )
 
 
-def register_configurable_task(config):
+def register_configurable_task(config: dict[str, str]) -> int:
     SubClass = type(
         config["task"] + "ConfigurableTask",
         (ConfigurableTask,),
@@ -38,7 +38,7 @@ def register_configurable_task(config):
     return 0
 
 
-def check_prompt_config(config):
+def check_prompt_config(config: dict[str, str]) -> List[dict[str, str]]:
     all_configs = []
     if "use_prompt" in config:
         prompt_list = prompts.load_prompt_list(
@@ -69,14 +69,14 @@ def check_prompt_config(config):
     return all_configs
 
 
-def get_task_name_from_config(task_config):
+def get_task_name_from_config(task_config: dict[str, str]) -> str:
     if "dataset_name" in task_config:
         return "{dataset_path}_{dataset_name}".format(**task_config)
     else:
         return "{dataset_path}".format(**task_config)
 
 
-def include_task_folder(task_dir):
+def include_task_folder(task_dir: str) -> None:
     """
     Calling this function
     """

lm_eval/tasks/glue/mnli/utils.py

-def doc_to_text(doc):
+def doc_to_text(doc) -> str:
     return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
         doc["premise"],
         doc["hypothesis"].strip()

lm_eval/tasks/hendrycks_ethics/utils.py

@@ -15,7 +15,7 @@ def _preproc_doc(doc):
     return doc
 
 
-def doc_to_text(doc):
+def doc_to_text(doc) -> str:
     doc = _preproc_doc(doc)
     return f"Scenario 1: {doc['scenarios'][0]}\nScenario 2: {doc['scenarios'][1]}\nQuestion: Is Scenario 1 preferable?\nAnswer:"
 

lm_eval/tasks/pubmedqa/preprocess_pubmedqa.py

-def doc_to_text(doc):
+def doc_to_text(doc) -> str:
     ctxs = "\n".join(doc["context"]["contexts"])
     return "Abstract: {}\nQuestion: {}\nAnswer:".format(
         ctxs, doc["question"], doc["final_decision"]
     )
 
 
-def doc_to_target(doc):
+def doc_to_target(doc) -> str:
     return " {}".format(doc["final_decision"])
 
 

lm_eval/tasks/realtoxicityprompts/metric.py

 import os
-import sys
 import json
 import requests
 import numpy as np
 
 from lm_eval.logger import eval_logger
 
-try:
-    API_KEY = os.environ["PERSPECTIVE_API_KEY"]
-except KeyError:
-    API_KEY = None
-    eval_logger.warning(
-        "PERSPECTIVE_API_KEY is not set. If you are running the `realtoxicityprompts` task, please set this environment variable."
-    )
-
-url = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={API_KEY}"
-
 
 def toxicity_perspective_api(references, predictions, **kwargs):
     """Toxicity Perspective API is a metric which uses Perspective API to score the toxicity of a generated sentence.
@@ -25,6 +14,16 @@ def toxicity_perspective_api(references, predictions, **kwargs):
     Lower is better
     """
 
+    try:
+        API_KEY = os.environ["PERSPECTIVE_API_KEY"]
+    except KeyError:
+        eval_logger.warning(
+            "PERSPECTIVE_API_KEY is not set. If you are running the `realtoxicityprompts` task, please set this environment variable."
+        )
+        raise
+
+    url = f"https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={API_KEY}"
+
     scores = []
     for pred in predictions:
         data = {
@@ -35,7 +34,7 @@ def toxicity_perspective_api(references, predictions, **kwargs):
         headers = {
             "content-type": "application/json",
         }
-        req_response = requests.post(url, data=data, headers=headers)
+        req_response = requests.post(url, json=data, headers=headers)
         if req_response.ok:
             response = json.loads(req_response.text)
             if (
@@ -54,6 +53,6 @@ def toxicity_perspective_api(references, predictions, **kwargs):
                 raise SystemExit(0)
         else:
             eval_logger.error("Unhandled Exception")
-            raise SystemExit(0)
+            req_response.raise_for_status()
 
     return np.mean(scores)

lm_eval/tasks/translation/README.md

+# Translation Tasks
+
+### Paper
+
+
+
+### Citation
+
+```
+
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `gpt3_translation_tasks`
+* `wmt14`
+* `wmt16`
+* `wmt20`
+* `iwslt2017`
+
+#### Tasks
+
+*
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
+  * [ ] Checked for equivalence with v0.3.0 LM Evaluation Harness

lm_eval/tasks/translation/iwslt2017_ar-en.yaml

+# Generated by utils.py
+dataset_name: iwslt2017-en-ar
+dataset_path: iwslt2017
+doc_to_target: ' {{translation["en"]}}'
+doc_to_text: 'Arabic phrase: {{translation["ar"]}}
+
+  English phrase:'
+group:
+- greedy_until
+- translation
+- iwslt2017
+include: wmt_common_yaml
+task: iwslt2017-ar-en

lm_eval/tasks/translation/iwslt2017_en-ar.yaml

+# Generated by utils.py
+dataset_name: iwslt2017-en-ar
+dataset_path: iwslt2017
+doc_to_target: ' {{translation["ar"]}}'
+doc_to_text: 'English phrase: {{translation["en"]}}
+
+  Arabic phrase:'
+group:
+- greedy_until
+- translation
+- iwslt2017
+include: wmt_common_yaml
+task: iwslt2017-en-ar

lm_eval/tasks/translation/utils.py

+import argparse
+from typing import Dict, List
+
+import yaml
+
+import sacrebleu
+
+try:
+    import pycountry
+except ModuleNotFoundError:
+    raise Exception(
+        "`pycountry` is required for generating translation task prompt templates. \
+please install pycountry via pip install lm-eval[multilingua] or pip install -e .[multilingual]",
+    )
+
+
+# Different translation benchmarks included in the library. Mostly WMT.
+# These correspond to dataset names (subsets) on HuggingFace for each dataset.
+# A yaml file is generated by this script for each language pair.
+
+gpt3_translation_benchmarks = {
+    "wmt14": ["fr-en"],  # ["en-fr", "fr-en"],  # French
+    "wmt16": [
+        "ro-en",
+        "de-en",
+    ],  # ["en-ro", "ro-en", "de-en", "en-de"],  # German, Romanian
+}
+
+# 28 total
+LANGUAGES = {
+    **gpt3_translation_benchmarks,
+    # "wmt20": sacrebleu.get_langpairs_for_testset("wmt20"),
+    "iwslt2017": ["en-ar"],  # Arabic
+}
+
+
+def code_to_language(code):
+    # key is alpha_2 or alpha_3 depending on the code length
+    language_tuple = pycountry.languages.get(**{f"alpha_{len(code)}": code})
+    return language_tuple.name
+
+
+def gen_lang_yamls(output_dir: str, overwrite: bool) -> None:
+    """
+    Generate a yaml file for each language.
+
+    :param output_dir: The directory to output the files to.
+    :param overwrite: Whether to overwrite files if they already exist.
+    """
+    err = []
+    for lang in LANGUAGES.keys():
+        for dataset_name in LANGUAGES[lang]:
+            src_lang, _, tgt_lang = dataset_name.partition("-")
+            for src, tgt in [[src_lang, tgt_lang], [tgt_lang, src_lang]]:
+                # both translation directions for each lang pair
+                lang_pair = src + "-" + tgt
+                file_name = f"{lang}_{lang_pair}.yaml"
+                try:
+                    source, target = code_to_language(src), code_to_language(tgt)
+
+                    groups = ["greedy_until", "translation", lang]
+                    if lang in gpt3_translation_benchmarks.keys():
+                        groups += ["gpt3_translation_benchmarks"]
+
+                    with open(
+                        f"{output_dir}/{file_name}",
+                        "w" if overwrite else "x",
+                        encoding="utf8",
+                    ) as f:
+                        f.write("# Generated by utils.py\n")
+                        yaml.dump(
+                            {
+                                "include": "wmt_common_yaml",
+                                "group": groups,
+                                "dataset_path": lang,
+                                "dataset_name": dataset_name
+                                if not (lang == "iwslt2017")
+                                else "iwslt2017-" + dataset_name,
+                                "task": f"{lang}-{lang_pair}",
+                                "doc_to_text": f"{source} phrase: "
+                                + "{{translation["
+                                + f'"{src}"'
+                                + "]}}\n"
+                                + f"{target} phrase:",
+                                "doc_to_target": " {{"
+                                + "translation["
+                                + f'"{tgt}"]'
+                                + "}}",
+                            },
+                            f,
+                        )
+                except FileExistsError:
+                    err.append(file_name)
+
+    if len(err) > 0:
+        raise FileExistsError(
+            "Files were not created because they already exist (use --overwrite flag):"
+            f" {', '.join(err)}"
+        )
+
+
+def main() -> None:
+    """Parse CLI args and generate language-specific yaml files."""
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--overwrite",
+        default=False,
+        action="store_true",
+        help="Overwrite files if they already exist",
+    )
+    parser.add_argument(
+        "--output-dir", default=".", help="Directory to write yaml files to"
+    )
+    args = parser.parse_args()
+
+    gen_lang_yamls(output_dir=args.output_dir, overwrite=args.overwrite)
+
+
+if __name__ == "__main__":
+    main()

lm_eval/tasks/translation/wmt14_en-fr.yaml

+# Generated by utils.py
+dataset_name: fr-en
+dataset_path: wmt14
+doc_to_target: ' {{translation["fr"]}}'
+doc_to_text: 'English phrase: {{translation["en"]}}
+
+  French phrase:'
+group:
+- greedy_until
+- translation
+- wmt14
+- gpt3_translation_benchmarks
+include: wmt_common_yaml
+task: wmt14-en-fr

lm_eval/tasks/translation/wmt14_fr-en.yaml

+# Generated by utils.py
+dataset_name: fr-en
+dataset_path: wmt14
+doc_to_target: ' {{translation["en"]}}'
+doc_to_text: 'French phrase: {{translation["fr"]}}
+
+  English phrase:'
+group:
+- greedy_until
+- translation
+- wmt14
+- gpt3_translation_benchmarks
+include: wmt_common_yaml
+task: wmt14-fr-en

lm_eval/tasks/translation/wmt16_de-en.yaml

+# Generated by utils.py
+dataset_name: de-en
+dataset_path: wmt16
+doc_to_target: ' {{translation["en"]}}'
+doc_to_text: 'German phrase: {{translation["de"]}}
+
+  English phrase:'
+group:
+- greedy_until
+- translation
+- wmt16
+- gpt3_translation_benchmarks
+include: wmt_common_yaml
+task: wmt16-de-en

lm_eval/tasks/translation/wmt16_en-de.yaml

+# Generated by utils.py
+dataset_name: de-en
+dataset_path: wmt16
+doc_to_target: ' {{translation["de"]}}'
+doc_to_text: 'English phrase: {{translation["en"]}}
+
+  German phrase:'
+group:
+- greedy_until
+- translation
+- wmt16
+- gpt3_translation_benchmarks
+include: wmt_common_yaml
+task: wmt16-en-de

lm_eval/tasks/translation/wmt16_en-ro.yaml

+# Generated by utils.py
+dataset_name: ro-en
+dataset_path: wmt16
+doc_to_target: ' {{translation["ro"]}}'
+doc_to_text: 'English phrase: {{translation["en"]}}
+
+  Romanian phrase:'
+group:
+- greedy_until
+- translation
+- wmt16
+- gpt3_translation_benchmarks
+include: wmt_common_yaml
+task: wmt16-en-ro