Merge branch 'main' into prefill

lm_eval/tasks/mmmu/mmmu_marketing.yaml

+task: mmmu_val_marketing
+include: _template_yaml
+task_alias: Marketing
+dataset_name: Marketing

lm_eval/tasks/mmmu/mmmu_materials.yaml

+task: mmmu_val_materials
+include: _template_yaml
+task_alias: Materials
+dataset_name: Materials

lm_eval/tasks/mmmu/mmmu_math.yaml

+task: mmmu_val_math
+include: _template_yaml
+task_alias: Math
+dataset_name: Math

lm_eval/tasks/mmmu/mmmu_mechanical_engineering.yaml

+task: mmmu_val_mechanical_engineering
+include: _template_yaml
+task_alias: Mechanical Engineering
+dataset_name: Mechanical_Engineering

lm_eval/tasks/mmmu/mmmu_music.yaml

+task: mmmu_val_music
+include: _template_yaml
+task_alias: Music
+dataset_name: Music

lm_eval/tasks/mmmu/mmmu_pharmacy.yaml

+task: mmmu_val_pharmacy
+include: _template_yaml
+task_alias: Pharmacy
+dataset_name: Pharmacy

lm_eval/tasks/mmmu/mmmu_physics.yaml

+task: mmmu_val_physics
+include: _template_yaml
+task_alias: Physics
+dataset_name: Physics

lm_eval/tasks/mmmu/mmmu_psychology.yaml

+task: mmmu_val_psychology
+include: _template_yaml
+task_alias: Psychology
+dataset_name: Psychology

lm_eval/tasks/mmmu/mmmu_public_health.yaml

+task: mmmu_val_public_health
+include: _template_yaml
+task_alias: Public Health
+dataset_name: Public_Health

lm_eval/tasks/mmmu/mmmu_sociology.yaml

+task: mmmu_val_sociology
+include: _template_yaml
+task_alias: Sociology
+dataset_name: Sociology

lm_eval/tasks/mmmu/utils.py

+import ast
+import random
+import re
+
+import numpy as np
+
+
+random.seed(42)
+
+
+# source for prompt fstrings: https://github.com/MMMU-Benchmark/MMMU/blob/7787d60648c82a9d40acd656fa541a6c74f58995/eval/configs/llava1.5.yaml#L3
+MULTI_CHOICE_EXAMPLE_FORMAT = """{}
+
+{}
+
+Answer with the option's letter from the given choices directly."""
+
+
+SHORT_ANS_EXAMPLE_FORMAT = """{}
+
+Answer the question using a single word or phrase."""
+
+START_CHR = "A"
+
+
+def doc_to_image(doc):
+    # get formatted prompt (incl. multi-choice options) pre-<image {i}> reformatting
+    input_text = _doc_to_text(doc)
+    # locate <image {i}> instances in input
+    image_placeholders = [
+        img.replace(" ", "_").replace("<", "").replace(">", "")
+        for img in re.findall("<image [1-7]>", input_text)
+    ]
+
+    # collect visuals (can have dupes of a given image or be out of order)
+    # E.g. validation_Math_19 contains <image 1> and <image 2> but seen as [<image 1>, <image 2>, <image 1>, <image 1>, <image 2>]
+    visuals = [doc[img] for img in image_placeholders]
+
+    return visuals
+
+
+def doc_to_text(doc):
+    """Get the prompt for a given document."""
+
+    prompt = _doc_to_text(doc)
+
+    for i in range(1, 8):
+        # replace <image {i}> with <image>. TODO: check this is always the right decision incl. for non-HF models
+        prompt = prompt.replace(f"<image {i}>", "<image>")
+
+    return prompt
+
+
+def _doc_to_text(doc):
+    """Helper--get the prompt for a given document but DO NOT yet replace <image {i}> with <image>."""
+
+    if doc["question_type"] == "multiple-choice":
+        choices_str = ""
+
+        for i, choice in enumerate(ast.literal_eval(doc["options"])):
+            # add (A) {choice1}\n , (B) {choice2}\n , and so on
+            # to create the list of formatted choices in the prompt
+            choices_str += f"\n({chr(ord(START_CHR) + i)}) {choice}"
+
+        choices_str = (
+            choices_str.lstrip()
+        )  # remove the extraneous prepended \n that we added
+
+        prompt = MULTI_CHOICE_EXAMPLE_FORMAT.format(doc["question"], choices_str)
+    else:
+        prompt = SHORT_ANS_EXAMPLE_FORMAT.format(doc["question"])
+
+    return prompt
+
+
+def process_results(doc, results):
+    if doc["question_type"] == "multiple-choice":
+        # multichoice logic
+        option_strs = ast.literal_eval(doc["options"])
+        option_letters = ["A", "B", "C", "D", "E", "F", "G", "H", "I"]
+
+        all_choices = option_letters[: len(option_strs)]
+        index2ans = {index: ans for index, ans in zip(option_letters, option_strs)}
+
+        pred = parse_multi_choice_response(results[0], all_choices, index2ans)
+        # print(pred, all_choices, index2ans)
+        is_correct = eval_multi_choice(doc["answer"], pred)
+    else:
+        # freeform response handling
+        pred = parse_open_response(results[0])
+        is_correct = eval_open(doc["answer"], pred)
+
+    return {"acc": float(is_correct)}
+
+    # TODO: it would be better if we could use a Filter for this logic.
+
+
+### Output parsing and answer selection taken from
+### https://github.com/MMMU-Benchmark/MMMU/blob/main/eval/utils/data_utils.py
+### and
+### https://github.com/MMMU-Benchmark/MMMU/blob/main/eval/utils/eval_utils.py
+
+
+# ----------- Process Multi-choice -------------
+def parse_multi_choice_response(response, all_choices, index2ans):
+    """
+    Parse the prediction from the generated response.
+    Return the predicted index e.g., A, B, C, D.
+    """
+    for char in [",", ".", "!", "?", ";", ":", "'"]:
+        response = response.strip(char)
+    response = " " + response + " "  # add space to avoid partial match
+
+    index_ans = True
+    ans_with_brack = False
+    candidates = []
+    for choice in all_choices:  # e.g., (A) (B) (C) (D)
+        if f"({choice})" in response:
+            candidates.append(choice)
+            ans_with_brack = True
+
+    if len(candidates) == 0:
+        for choice in all_choices:  # e.g., A B C D
+            if f" {choice} " in response:
+                candidates.append(choice)
+
+    # if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
+    if len(candidates) == 0 and len(response.split()) > 5:
+        for index, ans in index2ans.items():
+            if ans.lower() in response.lower():
+                candidates.append(index)
+                index_ans = False  # it's content ans.
+
+    if len(candidates) == 0:  # still not get answer, randomly choose one.
+        pred_index = random.choice(all_choices)
+    elif len(candidates) > 1:
+        start_indexes = []
+        if index_ans:
+            if ans_with_brack:
+                for can in candidates:
+                    index = response.rfind(f"({can})")
+                    start_indexes.append(index)  # -1 will be ignored anyway
+                # start_indexes = [generated_response.index(f'({can})') for can in candidates]
+            else:
+                for can in candidates:
+                    index = response.rfind(f" {can} ")
+                    start_indexes.append(index)
+        else:
+            for can in candidates:
+                index = response.lower().rfind(index2ans[can].lower())
+                start_indexes.append(index)
+        # get the last one
+        pred_index = candidates[np.argmax(start_indexes)]
+    else:  # if only one candidate, use it.
+        pred_index = candidates[0]
+
+    # print(response, all_choices, index2ans, pred_index)
+
+    return pred_index
+
+
+# ----------- Process Open -------------
+def check_is_number(string):
+    """
+    Check if the given string a number.
+    """
+    try:
+        float(string.replace(",", ""))
+        return True
+    except ValueError:
+        # check if there's comma inside
+        return False
+
+
+def normalize_str(string):
+    """
+    Normalize the str to lower case and make them float numbers if possible.
+    """
+    # check if characters in the string
+
+    # if number, numerize it.
+    string = string.strip()
+
+    is_number = check_is_number(string)
+
+    if is_number:
+        string = string.replace(",", "")
+        string = float(string)
+        # leave 2 decimal
+        string = round(string, 2)
+        return [string]
+    else:  # it's likely to be a string
+        # lower it
+        string = string.lower()
+        if len(string) == 1:
+            return [" " + string, string + " "]  # avoid trivial matches
+        return [string]
+
+
+def extract_numbers(string):
+    """
+    Exact all forms of numbers from a string with regex.
+    """
+    # Pattern for numbers with commas
+    pattern_commas = r"-?\b\d{1,3}(?:,\d{3})+\b"
+    # Pattern for scientific notation
+    pattern_scientific = r"-?\d+(?:\.\d+)?[eE][+-]?\d+"
+    # Pattern for simple numbers without commas
+    pattern_simple = r"-?(?:\d+\.\d+|\.\d+|\d+\b)(?![eE][+-]?\d+)(?![,\d])"
+
+    # Extract numbers with commas
+    numbers_with_commas = re.findall(pattern_commas, string)
+    # Extract numbers in scientific notation
+    numbers_scientific = re.findall(pattern_scientific, string)
+    # Extract simple numbers without commas
+    numbers_simple = re.findall(pattern_simple, string)
+
+    # Combine all extracted numbers
+    all_numbers = numbers_with_commas + numbers_scientific + numbers_simple
+    return all_numbers
+
+
+def parse_open_response(response):
+    """
+    Parse the prediction from the generated response.
+    Return a list of predicted strings or numbers.
+    """
+
+    # content = content.strip("\n").strip(".").strip(" ")
+    def get_key_subresponses(response):
+        key_responses = []
+        response = response.strip().strip(".").lower()
+        sub_responses = re.split(r"\.\s(?=[A-Z])|\n", response)
+        indicators_of_keys = [
+            "could be ",
+            "so ",
+            "is ",
+            "thus ",
+            "therefore ",
+            "final ",
+            "answer ",
+            "result ",
+        ]
+        key_responses = []
+        for index, resp in enumerate(sub_responses):
+            # if last one, accept it's an equation (the entire response can be just one sentence with equation)
+            if index == len(sub_responses) - 1:
+                indicators_of_keys.extend(["="])
+            shortest_key_response = None  # the shortest response that may contain the answer (tail part of the response)
+            for indicator in indicators_of_keys:
+                if indicator in resp:
+                    if not shortest_key_response:
+                        shortest_key_response = resp.split(indicator)[-1].strip()
+                    else:
+                        if len(resp.split(indicator)[-1].strip()) < len(
+                            shortest_key_response
+                        ):
+                            shortest_key_response = resp.split(indicator)[-1].strip()
+                    # key_responses.append(resp.split(indicator)[1].strip())
+
+            if shortest_key_response:
+                # and it's not trivial
+                if shortest_key_response.strip() not in [
+                    ":",
+                    ",",
+                    ".",
+                    "!",
+                    "?",
+                    ";",
+                    ":",
+                    "'",
+                ]:
+                    key_responses.append(shortest_key_response)
+        if len(key_responses) == 0:  # did not found any
+            return [response]
+        return key_responses
+
+    # pdb.set_trace()
+    key_responses = get_key_subresponses(response)
+
+    pred_list = key_responses.copy()  # keep the original string response
+    for resp in key_responses:
+        pred_list.extend(extract_numbers(resp))
+
+    tmp_pred_list = []
+    for i in range(len(pred_list)):
+        tmp_pred_list.extend(normalize_str(pred_list[i]))
+    pred_list = tmp_pred_list
+
+    # remove duplicates
+    pred_list = list(set(pred_list))
+
+    return pred_list
+
+
+# ----------- Evaluation -------------
+
+
+def eval_multi_choice(gold_i, pred_i):
+    """
+    Evaluate a multiple choice instance.
+    """
+    correct = False
+    # only they are exactly the same, we consider it as correct
+    if isinstance(gold_i, list):
+        for answer in gold_i:
+            if answer == pred_i:
+                correct = True
+                break
+    else:  # gold_i is a string
+        if gold_i == pred_i:
+            correct = True
+    return correct
+
+
+def eval_open(gold_i, pred_i):
+    """
+    Evaluate an open question instance
+    """
+    correct = False
+    if isinstance(gold_i, list):
+        # use float to avoid trivial matches
+        norm_answers = []
+        for answer in gold_i:
+            norm_answers.extend(normalize_str(answer))
+    else:
+        norm_answers = normalize_str(gold_i)
+    for pred in pred_i:  # pred is already normalized in parse response phase
+        if isinstance(pred, str):  # if it's a string, then find if ans in the pred_i
+            for norm_ans in norm_answers:
+                # only see if the string answer in the string pred
+                if isinstance(norm_ans, str) and norm_ans in pred:
+                    if not correct:
+                        correct = True
+                    break
+        else:  # it's a float number
+            if pred in norm_answers:
+                if not correct:
+                    correct = True
+                break
+    return correct

lm_eval/tasks/tmmluplus/default/tmmluplus.yaml → lm_eval/tasks/tmmluplus/default/_tmmluplus.yaml

@@ -4,3 +4,10 @@ task:
 - tmmluplus_social_sciences
 - tmmluplus_humanities
 - tmmluplus_STEM
+aggregate_metric_list:
+  - metric: acc
+    weight_by_size: True
+  - metric: acc_norm
+    weight_by_size: True
+metadata:
+  version: 2.0

lm_eval/tasks/tmmluplus/default/_tmmluplus_STEM.yaml

+group: tmmluplus_STEM
+task:
+- tmmluplus_STEM_tasks
+aggregate_metric_list:
+  - metric: acc
+    weight_by_size: True
+  - metric: acc_norm
+    weight_by_size: True
+metadata:
+  version: 2.0

lm_eval/tasks/tmmluplus/default/_tmmluplus_humanities.yaml

+group: tmmluplus_humanities
+task:
+- tmmluplus_humanities_tasks
+aggregate_metric_list:
+  - metric: acc
+    weight_by_size: True
+  - metric: acc_norm
+    weight_by_size: True
+metadata:
+  version: 2.0

lm_eval/tasks/tmmluplus/default/_tmmluplus_other.yaml

+group: tmmluplus_other
+task:
+- tmmluplus_other_tasks
+aggregate_metric_list:
+  - metric: acc
+    weight_by_size: True
+  - metric: acc_norm
+    weight_by_size: True
+metadata:
+  version: 2.0

lm_eval/tasks/tmmluplus/default/_tmmluplus_social_sciences.yaml

+group: tmmluplus_social_sciences
+task:
+- tmmluplus_social_sciences_tasks
+aggregate_metric_list:
+  - metric: acc
+    weight_by_size: True
+  - metric: acc_norm
+    weight_by_size: True
+metadata:
+  version: 2.0

lm_eval/tasks/tmmluplus/default/_default_template_yaml → lm_eval/tasks/tmmluplus/default/_tmmluplus_template_yaml

@@ -16,4 +16,4 @@ metric_list:
     aggregation: mean
     higher_is_better: true
 metadata:
-  version: 1.0
+  version: 2.0

lm_eval/tasks/tmmluplus/default/tmmluplus_accounting.yaml

 "dataset_name": "accounting"
 "description": "以下為會計學的單選題，請提供正確答案的選項。\n\n"
-"group": "tmmluplus_other"
-"group_alias": "other"
-"include": "_default_template_yaml"
+"tag": "tmmluplus_other_tasks"
+"include": "_tmmluplus_template_yaml"
 "task": "tmmluplus_accounting"
 "task_alias": "accounting"

lm_eval/tasks/tmmluplus/default/tmmluplus_administrative_law.yaml

 "dataset_name": "administrative_law"
 "description": "以下為行政法的單選題，請提供正確答案的選項。\n\n"
-"group": "tmmluplus_humanities"
-"group_alias": "humanities"
-"include": "_default_template_yaml"
+"tag": "tmmluplus_humanities_tasks"
+"include": "_tmmluplus_template_yaml"
 "task": "tmmluplus_administrative_law"
 "task_alias": "administrative law"

lm_eval/tasks/tmmluplus/default/tmmluplus_advance_chemistry.yaml

 "dataset_name": "advance_chemistry"
 "description": "以下為化學的單選題，請提供正確答案的選項。\n\n"
-"group": "tmmluplus_STEM"
-"group_alias": "STEM"
-"include": "_default_template_yaml"
+"tag": "tmmluplus_STEM_tasks"
+"include": "_tmmluplus_template_yaml"
 "task": "tmmluplus_advance_chemistry"
 "task_alias": "advance chemistry"