fix meta_eval after refactor and add new meta_mmlu_instruct task for 3.2

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/README.md

@@ -22,15 +22,16 @@ Given those differences, the numbers from this recipe can not be compared to the
 
 ## Environment setups
 
-Please install lm-evaluation-harness and our llama-recipe repo by following:
+Please install lm-evaluation-harness and our llama-cookbook repo by following:
 
 ```
-git clone [email protected]:meta-llama/llama-recipes.git
-cd llama-recipes
+git clone [email protected]:meta-llama/llama-cookbook.git
+cd llama-cookbook
 pip install -U pip setuptools
 pip install -e .
+pip install -U antlr4_python3_runtime==4.11
 pip install lm-eval[math,ifeval,sentencepiece,vllm]==0.4.3
-cd tools/benchmarks/llm_eval_harness/meta_eval
+cd end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval
 ```
 
 To access our [3.1 evals Hugging Face collection](https://huggingface.co/collections/meta-llama/llama-31-evals-66a2c5a14c2093e58298ac7f), you must:

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/eval_config.yaml

@@ -1,6 +1,6 @@
-model_name: "meta-llama/Llama-3.1-8B-Instruct" # The name of the model to evaluate. This must be a valid Meta Llama 3 based model name in the HuggingFace model hub."
+model_name: "meta-llama/Llama-3.2-3B-Instruct" # The name of the model to evaluate. This must be a valid Meta Llama 3 based model name in the HuggingFace model hub."
 
-evals_dataset: "meta-llama/Llama-3.1-8B-Instruct-evals" # The name of the 3.1 evals dataset to evaluate, please make sure this eval dataset corresponds to the model loaded. This must be a valid dataset name in the Llama 3.x Evals collection.
+evals_dataset: "meta-llama/Llama-3.2-3B-Instruct-evals" # The name of the 3.1 evals dataset to evaluate, please make sure this eval dataset corresponds to the model loaded. This must be a valid dataset name in the Llama 3.x Evals collection.
 # Must be one of the following ["meta-llama/Llama-3.1-8B-Instruct-evals","meta-llama/Llama-3.1-70B-Instruct-evals","meta-llama/Llama-3.1-405B-Instruct-evals","meta-llama/Llama-3.1-8B-evals","meta-llama/Llama-3.1-70B-evals","meta-llama/Llama-3.1-405B-evals","meta-llama/Llama-3.2-1B-evals","meta-llama/Llama-3.2-3B-evals", "meta-llama/Llama-3.2-1B-Instruct-evals", "meta-llama/Llama-3.2-3B-Instruct-evals"]
 
 tasks: "meta_instruct" # Available tasks for 3.1 instruct model: "meta_math_hard", "meta_gpqa_cot", "meta_mmlu_pro_instruct", "meta_ifeval"; or just use "meta_instruct" to run all of them.

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/meta_template/mmlu/mmlu_instruct.yaml

@@ -0,0 +1,29 @@
+task: meta_mmlu_instruct
+dataset_path: meta-llama/Llama-3.1-8B-Instruct-evals
+dataset_name: Llama-3.1-8B-Instruct-evals__mmlu__details
+test_split: latest
+output_type: generate_until
+process_docs: !function utils.process_docs_instruct
+doc_to_text: !function utils.doc_to_text_instruct
+doc_to_target: gold
+filter_list:
+  - name: "strict-match"
+    filter:
+      - function: "regex"
+        group_select: -1
+        regex_pattern: ' ([A-D])'
+      - function: "take_first"
+generation_kwargs:
+  until: []
+  do_sample: false
+  temperature: 0
+  max_gen_toks: 1024
+num_fewshot: 0
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+metadata:
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/meta_template/mmlu/mmlu.yaml → end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/meta_template/mmlu/mmlu_pretrain.yaml

@@ -3,12 +3,12 @@ dataset_path: meta-llama/Llama-3.1-8B-evals
 dataset_name: Llama-3.1-8B-evals__mmlu__details
 test_split: latest
 output_type: multiple_choice
-process_docs: !function utils.process_docs
-doc_to_text: !function utils.doc_to_text
+process_docs: !function utils.process_docs_pretrain
+doc_to_text: !function utils.doc_to_text_pretrain
 doc_to_target: !function utils.doc_to_target
 doc_to_choice: ["A", "B", "C", "D"]
 # 5-shot prompts are already included in the dataset
 # So no need to generate
 num_fewshot: 0
 metadata:
-  version: 1.0
+  version: 1.0

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/meta_template/mmlu/utils.py

@@ -1,12 +1,22 @@
 import string
+
 import datasets
 
-def doc_to_text(doc: dict) -> str:
+
+def doc_to_text_pretrain(doc: dict) -> str:
     # Strip out the last two characters, which is a space and the answer
     # E.g., "Answer: B" -> "Answer:"
     return doc["input_final_prompts"][0][:-2]
+    return text
+
+
+def doc_to_text_instruct(doc: dict) -> str:
+    # Strip out the last two characters, which is a space and the answer
+    # E.g., "Answer: B" -> "Answer:"
+    return doc["input_final_prompts"][0]
+
 
-def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
+def process_docs_pretrain(dataset: datasets.Dataset) -> datasets.Dataset:
     def _process_doc(doc: dict) -> dict:
         # input_correct_responses is in format of: "Answer: B"
         answer = doc["input_correct_responses"][0]
@@ -21,11 +31,43 @@ def _process_doc(doc: dict) -> dict:
         return out_doc
 
     dataset = dataset.select_columns(
-        ["input_question", "input_correct_responses", "input_final_prompts", "is_correct", "input_question_hash",
-         "input_choice_list"])
+        [
+            "input_question",
+            "input_correct_responses",
+            "input_final_prompts",
+            "is_correct",
+            "input_question_hash",
+            "input_choice_list",
+        ]
+    )
+    dataset = dataset.rename_column("is_correct", "previously_is_correct")
+    dataset = dataset.map(_process_doc)
+    return dataset.map(_process_doc)
+
+
+def process_docs_instruct(dataset: datasets.Dataset) -> datasets.Dataset:
+    def _process_doc(doc: dict) -> dict:
+        out_doc = {
+            "problem": doc["input_question"],
+            "gold": doc["input_correct_responses"][0],
+        }
+        return out_doc
+
+    dataset = dataset.select_columns(
+        [
+            "input_question",
+            "input_correct_responses",
+            "input_final_prompts",
+            "is_correct",
+            "input_question_hash",
+            "input_choice_list",
+            "output_prediction_text",
+        ]
+    )
     dataset = dataset.rename_column("is_correct", "previously_is_correct")
     dataset = dataset.map(_process_doc)
     return dataset.map(_process_doc)
 
+
 def doc_to_target(doc: dict) -> str:
     return doc["gold"]

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/meta_template/mmlu_pro/utils.py

@@ -1,21 +1,31 @@
 import string
 
-
 import datasets
 
 
-
 def doc_to_text(doc: dict) -> str:
     return doc["input_final_prompts"][0]
 
+
 def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:
     def _process_doc(doc: dict) -> dict:
         out_doc = {
             "problem": doc["input_question"],
             "gold": doc["input_correct_responses"][0],
         }
         return out_doc
-    dataset = dataset.select_columns(["input_question", "input_correct_responses", "input_final_prompts", "is_correct","input_question_hash","input_choice_list","output_prediction_text"])
-    dataset = dataset.rename_column("is_correct","previously_is_correct")
+
+    dataset = dataset.select_columns(
+        [
+            "input_question",
+            "input_correct_responses",
+            "input_final_prompts",
+            "is_correct",
+            "input_question_hash",
+            "input_choice_list",
+            "output_prediction_text",
+        ]
+    )
+    dataset = dataset.rename_column("is_correct", "previously_is_correct")
     dataset = dataset.map(_process_doc)
     return dataset.map(_process_doc)

end-to-end-use-cases/benchmarks/llm_eval_harness/meta_eval/prepare_meta_eval.py

@@ -3,43 +3,46 @@
 
 import argparse
 import errno
-import shutil
 import glob
 import os
+import shutil
 from pathlib import Path
+
 import nltk
 import yaml
 from datasets import Dataset, load_dataset
 
-LLAMA_3_1_INSTRUCT_EVALS=[
+LLAMA_3_1_INSTRUCT_EVALS = [
     "meta-llama/Llama-3.1-8B-Instruct-evals",
     "meta-llama/Llama-3.1-70B-Instruct-evals",
     "meta-llama/Llama-3.1-405B-Instruct-evals",
 ]
-LLAMA_3_1_PRETRAIN_EVALS=[
+LLAMA_3_1_PRETRAIN_EVALS = [
     "meta-llama/Llama-3.1-8B-evals",
     "meta-llama/Llama-3.1-70B-evals",
     "meta-llama/Llama-3.1-405B-evals",
 ]
-LLAMA_3_2_INSTRUCT_EVALS=[
+LLAMA_3_2_INSTRUCT_EVALS = [
     "meta-llama/Llama-3.2-1B-Instruct-evals",
     "meta-llama/Llama-3.2-3B-Instruct-evals",
 ]
-LLAMA_3_2_PRETRAIN_EVALS=[
+LLAMA_3_2_PRETRAIN_EVALS = [
     "meta-llama/Llama-3.2-1B-evals",
     "meta-llama/Llama-3.2-3B-evals",
 ]
 
+
 # get the ifeval  from the evals dataset and join it with the original ifeval datasets
 def get_ifeval_data(model_name, output_dir):
     print(f"preparing the ifeval data using {model_name}'s evals dataset")
     if model_name not in [
         "Llama-3.1-8B-Instruct",
         "Llama-3.1-70B-Instruct",
         "Llama-3.1-405B-Instruct",
+        "Llama-3.3-70B-Instruct",
     ]:
         raise ValueError(
-            "Only Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Llama-3.1-405B-Instruct models are supported for IFEval"
+            "Only Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Llama-3.1-405B-Instruct, Llama-3.3-70B-Instruct models are supported for IFEval"
         )
     original_dataset_name = "wis-k/instruction-following-eval"
     meta_dataset_name = f"meta-llama/{model_name}-evals"
@@ -80,11 +83,12 @@ def get_math_hard_data(model_name, output_dir):
         "Llama-3.1-8B-Instruct",
         "Llama-3.1-70B-Instruct",
         "Llama-3.1-405B-Instruct",
+        "Llama-3.3-70B-Instruct",
     ]:
         raise ValueError(
-            "Only Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Llama-3.1-405B-Instruct models are supported for MATH_hard"
+            "Only Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Llama-3.1-405B-Instruct, Llama-3.3-70B-Instruct models are supported for MATH_hard"
         )
-    original_dataset_name = "lighteval/MATH-Hard"
+    original_dataset_name = "DigitalLearningGmbH/MATH-lighteval"
     meta_dataset_name = f"meta-llama/{model_name}-evals"
     meta_data = load_dataset(
         meta_dataset_name,
@@ -95,6 +99,7 @@ def get_math_hard_data(model_name, output_dir):
     joined = join_meta_and_original_math_data(meta_data, math_data)
     joined.to_parquet(output_dir + "/joined_math_hard.parquet")
 
+
 def get_math_data(model_name, output_dir):
     print(f"preparing the math data using {model_name}'s evals dataset")
     if model_name not in [
@@ -104,7 +109,7 @@ def get_math_data(model_name, output_dir):
         raise ValueError(
             "Only Llama-3.2-1B-Instruct and Llama-3.2-3B-Instruct models are supported for MATH"
         )
-    original_dataset_name = "lighteval/MATH"
+    original_dataset_name = "DigitalLearningGmbH/MATH-lighteval"
     meta_dataset_name = f"meta-llama/{model_name}-evals"
     meta_data = load_dataset(
         meta_dataset_name,
@@ -115,6 +120,7 @@ def get_math_data(model_name, output_dir):
     joined = join_meta_and_original_math_data(meta_data, math_data)
     joined.to_parquet(output_dir + "/joined_math.parquet")
 
+
 def join_meta_and_original_math_data(meta_data, math_data):
     meta_df = meta_data.to_pandas()
     math_df = math_data.to_pandas()
@@ -138,6 +144,7 @@ def join_meta_and_original_math_data(meta_data, math_data):
     )
     return joined
 
+
 # get the question from the ifeval dataset
 def get_question(example):
     try:
@@ -181,25 +188,30 @@ def change_yaml(args, base_name):
     if args.evals_dataset in LLAMA_3_1_PRETRAIN_EVALS:
         meta_pretrain["task"] = ["meta_bbh", "meta_mmlu_pro_pretrain"]
     elif args.evals_dataset in LLAMA_3_2_PRETRAIN_EVALS:
-        meta_pretrain["task"] = ["meta_mmlu"]
+        meta_pretrain["task"] = ["meta_mmlu_pretrain"]
     with open(args.work_dir + "/meta_pretrain.yaml", "w") as yaml_file:
         yaml.dump(meta_pretrain, yaml_file)
-    
+
     # Update tasks in meta_instruct.yaml
     with open(args.template_dir + "/meta_instruct.yaml", "r") as yaml_file:
         meta_instruct = yaml.safe_load(yaml_file)
     if args.evals_dataset in LLAMA_3_1_INSTRUCT_EVALS:
-        meta_instruct["task"] = ["meta_ifeval", "meta_math_hard", "meta_gpqa_cot", "meta_mmlu_pro_instruct"]
+        meta_instruct["task"] = [
+            "meta_ifeval",
+            "meta_math_hard",
+            "meta_gpqa_cot",
+            "meta_mmlu_pro_instruct",
+        ]
     elif args.evals_dataset in LLAMA_3_2_INSTRUCT_EVALS:
-        meta_instruct["task"] = ["meta_mmlu", "meta_math", "meta_gpqa"]
+        meta_instruct["task"] = ["meta_mmlu_instruct", "meta_math", "meta_gpqa"]
     with open(args.work_dir + "/meta_instruct.yaml", "w") as yaml_file:
         yaml.dump(meta_instruct, yaml_file)
 
 
 # copy the files and change the yaml file to use the correct model name
 def copy_and_prepare(args):
     # nltk punkt_tab package is needed
-    nltk.download('punkt_tab')
+    nltk.download("punkt_tab")
     copy_dir(args.template_dir, args.work_dir)
     # Use the template yaml to get the correct model name in work_dir yaml
     base_name = (
@@ -227,7 +239,9 @@ def prepare_datasets(args):
     if "meta_instruct" in task_list and args.evals_dataset in LLAMA_3_1_INSTRUCT_EVALS:
         get_ifeval_data(model_name, args.work_dir)
         get_math_hard_data(model_name, args.work_dir)
-    elif "meta_instruct" in task_list and args.evals_dataset in LLAMA_3_2_INSTRUCT_EVALS:
+    elif (
+        "meta_instruct" in task_list and args.evals_dataset in LLAMA_3_2_INSTRUCT_EVALS
+    ):
         get_math_data(model_name, args.work_dir)
     else:
         if "meta_ifeval" in task_list:
@@ -264,10 +278,10 @@ def load_config(config_path: str = "./config.yaml"):
     if not os.path.exists(args.template_dir):
         raise ValueError("The template_dir does not exist, please check the path")
     if args.evals_dataset not in (
-        LLAMA_3_1_INSTRUCT_EVALS +
-        LLAMA_3_1_PRETRAIN_EVALS +
-        LLAMA_3_2_INSTRUCT_EVALS +
-        LLAMA_3_2_PRETRAIN_EVALS
+        LLAMA_3_1_INSTRUCT_EVALS
+        + LLAMA_3_1_PRETRAIN_EVALS
+        + LLAMA_3_2_INSTRUCT_EVALS
+        + LLAMA_3_2_PRETRAIN_EVALS
     ):
         raise ValueError(
             "The evals dataset is not valid, please double check the name, must use the name in the Llama 3.1 or 3.2 Evals collection."