utils.py 17.8 KB
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import os
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import re
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import sys
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import yaml
import inspect
import pathlib
import functools
import subprocess
import collections
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import importlib.util
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import fnmatch
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from typing import Iterator, List, Literal, Union
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import gc
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import torch
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import transformers
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from omegaconf import OmegaConf
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from jinja2 import BaseLoader, Environment, StrictUndefined
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from itertools import islice
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from lm_eval.logger import eval_logger
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def escaped_split(text, sep_char, maxsplit=-1):
    """Split text into a list on occurrences of the given separation
    character `sep_char`. The separation character may be escaped by a
    backslash to avoid splitting at that location.

    The separation character must be a string of size 1.

    If `maxsplit` is given, at most `maxsplit` splits are done (thus,
    the list will have at most `maxsplit + 1` elements). If `maxsplit`
    is not specified or less than 0, then there is no limit on the
    number of splits (all possible splits are made).
    """
    assert (
        len(sep_char) == 1
    ), "separation string must be a single character for escaped splitting"

    if maxsplit == 0:
        return text
    maxsplit = max(0, maxsplit)

    return re.split(r"(?<!\\)" + sep_char, text, maxsplit)


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def simple_parse_args_string(args_string):
    """
    Parses something like
        args1=val1,arg2=val2
    Into a dictionary
    """
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    args_string = args_string.strip()
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    if not args_string:
        return {}
    arg_list = args_string.split(",")
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    args_dict = OmegaConf.to_object(OmegaConf.from_dotlist(arg_list))
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    return args_dict
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def join_iters(iters):
    for iter in iters:
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        yield from iter
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def chunks(iter, n: int = 0, fn=None):
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    arr = []
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    for i, x in enumerate(iter):
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        arr.append(x)
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        if len(arr) == (fn(i) if fn else n):
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            yield arr
            arr = []
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    if arr:
        yield arr

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def group(arr, fn):
    res = collections.defaultdict(list)

    for ob in arr:
        res[fn(ob)].append(ob)
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    return list(res.values())

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class MultiChoice:
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    def __init__(self, choices) -> None:
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        self.choices = choices

    # Simple wildcard support (linux filename patterns)
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    def __contains__(self, values) -> bool:
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        for value in values.split(","):
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            if len(fnmatch.filter(self.choices, value)) == 0:
                eval_logger.info(f"Available tasks to choose:")
                for choice in self.choices:
                    eval_logger.info(f"  - {choice}")
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                raise ValueError("'{}' is not in task list".format(value))
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        return True

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    def __iter__(self) -> Iterator:
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        for choice in self.choices:
            yield choice


# Returns a list containing all values of the source_list that
# match at least one of the patterns
def pattern_match(patterns, source_list):
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    if type(patterns) == str:
        patterns = [patterns]

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    task_names = set()
    for pattern in patterns:
        for matching in fnmatch.filter(source_list, pattern):
            task_names.add(matching)
    return sorted(list(task_names))


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def general_detokenize(string):
    string = string.replace(" n't", "n't")
    string = string.replace(" )", ")")
    string = string.replace("( ", "(")
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    string = string.replace('" ', '"')
    string = string.replace(' "', '"')
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    string = re.sub(r" (['.,])", r"\1", string)
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    return string


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def get_rolling_token_windows(token_list, prefix_token, max_seq_len, context_len):
    """
    - context_len allows for a rolling window context, allowing each prediction window to potentially
      condition on some context

    :param token_list: list
        List of tokens to be PREDICTED
    :param max_seq_len: int
        max_seq_len of model (or max_seq_len we want to use)
    :param context_len: int
        Amount of desired token context for prediction. Needs to be at least 1.
    :param prefix_token: token
        Dummy token like <eos> so the first token has something to condition on
    :return: generator
        Generator of tuples
            (input_tokens, pred_tokens)
        Note: Score only the last len(pred_tokens) logits of the LM
    """
    assert 1 <= context_len <= max_seq_len
    if not token_list:
        return
    # +1 offset, going from input->preds
    pred_len = max_seq_len - context_len + 1
    predicted = 0

    # Special handling for first window: predict all tokens
    first_seq_len = min(max_seq_len, len(token_list))
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    yield ([prefix_token] + token_list[: first_seq_len - 1], token_list[:first_seq_len])
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    predicted += first_seq_len

    while predicted < len(token_list):
        window_pred_len = min(len(token_list) - predicted, pred_len)
        window_end = predicted + window_pred_len
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        yield (
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            token_list[window_end - max_seq_len - 1 : window_end - 1],
            token_list[window_end - window_pred_len : window_end],
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        )
        predicted += window_pred_len

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def make_disjoint_window(pair):
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    """Takes output from get_rolling_token_windows and makes the context not overlap with the continuation"""
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    a, b = pair
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    return a[: len(a) - (len(b) - 1)], b
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class Reorderer:
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    def __init__(self, arr, fn) -> None:
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        self.size = len(arr)
        arr = list(enumerate(arr))
        arr = group(arr, lambda x: fn(x[1]))
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        # arr = [([y[0] for y in x], x[0][1]) for x in arr]
        # TODO: overhaul reorderer. It currently grouped requests by content but we don't want this
        arr = [([y[0]], x[0][1]) for x in arr for y in x]
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        arr.sort(key=lambda x: fn(x[1]))

        self.arr = arr
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    def get_reordered(self):
        return [x[1] for x in self.arr]
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    def get_original(self, newarr):
        res = [None] * self.size
        cov = [False] * self.size

        for (inds, _), v in zip(self.arr, newarr):
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            for ind in inds:
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                res[ind] = v
                cov[ind] = True
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        assert all(cov)
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        return res

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class Grouper:
    """
    takes an array `arr` and function `fn` and returns a dictionary
    with keys fn(ob) for each ob in `arr` and with values `self.arr[key]` a list of all
    objects in `arr` satisfying `key == fn(ob)`.
    """

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    def __init__(self, arr, fn) -> None:
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        # self.orig_arr = arr
        self.size = len(arr)
        arr = list(enumerate(arr))

        def group_return_dict(arr, fn):
            res = collections.defaultdict(list)

            for ob in arr:
                res[fn(ob)].append(ob)
            return res

        arr = group_return_dict(arr, lambda x: fn(x[1]))

        # self.arr has format Dict[Tuple[int, <entry from orig. arr>]]
        self.arr = arr
        self._grouped = None

    def get_grouped(self):
        # return the contents but not indices for our grouped dict.
        if self._grouped:
            return self._grouped
        grouped = {}
        for key in self.arr.keys():
            # drop the index from each element of self.arr
            grouped[key] = [y[1] for y in self.arr[key]]
        self._grouped = grouped
        return grouped

    def get_original(self, grouped_dict):
        # take in a grouped dictionary with e.g. results for each key listed
        # in the same order as the instances in `self.arr`, and
        # return the results in the same (single list) order as `self.orig_arr`.
        res = [None] * self.size
        cov = [False] * self.size
        # orig = [None] * self.size

        assert grouped_dict.keys() == self.arr.keys()

        for key in grouped_dict.keys():
            for (ind, _), v in zip(self.arr[key], grouped_dict[key]):
                res[ind] = v
                cov[ind] = True
                # orig[ind] = _

        assert all(cov)
        # assert orig == self.orig_arr

        return res


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def make_table(result_dict, column: str = "results"):
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    """Generate table of results."""
    from pytablewriter import MarkdownTableWriter, LatexTableWriter

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    if column == "results":
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        column_name = "Tasks"
    elif column == "groups":
        column_name = "Groups"
    elif column == "benchmarks":
        column_name = "Benchmarks"
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    md_writer = MarkdownTableWriter()
    latex_writer = LatexTableWriter()
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    md_writer.headers = [
        column_name,
        "Version",
        "Filter",
        "Metric",
        "Value",
        "",
        "Stderr",
    ]
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    latex_writer.headers = [
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        column_name,
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        "Version",
        "Filter",
        "Metric",
        "Value",
        "",
        "Stderr",
    ]
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    values = []

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    for k, dic in result_dict[column].items():
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        version = result_dict["versions"][k]
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        for (mf), v in dic.items():
            m, _, f = mf.partition(",")
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            if m.endswith("_stderr"):
                continue

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            if m + "_stderr" + "," + f in dic:
                se = dic[m + "_stderr" + "," + f]
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                values.append([k, version, f, m, "%.4f" % v, "±", "%.4f" % se])
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            else:
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                values.append([k, version, f, m, "%.4f" % v, "", ""])
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            k = ""
            version = ""
    md_writer.value_matrix = values
    latex_writer.value_matrix = values

    # todo: make latex table look good
    # print(latex_writer.dumps())

    return md_writer.dumps()


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def positional_deprecated(fn):
    """
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    A decorator to nudge users into passing only keyword args (`kwargs`) to the
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    wrapped function, `fn`.
    """
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    @functools.wraps(fn)
    def _wrapper(*args, **kwargs):
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        if len(args) != 1 if inspect.ismethod(fn) else 0:
            print(
                f"WARNING: using {fn.__name__} with positional arguments is "
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                "deprecated and will be disallowed in a future version of "
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                "lm-evaluation-harness!"
            )
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        return fn(*args, **kwargs)
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    return _wrapper
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@positional_deprecated
def find_test_root(start_path: pathlib.Path) -> pathlib.Path:
    """
    Search upward in the directory tree to a maximum of three layers
    to find and return the package root (containing the 'tests' folder)
    """
    cur_path = start_path.resolve()
    max_layers = 3
    for _ in range(max_layers):
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        if (cur_path / "tests" / "test_version_stable.py").exists():
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            return cur_path
        else:
            cur_path = cur_path.parent.resolve()
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    raise FileNotFoundError(
        f"Unable to find package root within {max_layers} upwards" + f"of {start_path}"
    )

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@positional_deprecated
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def run_task_tests(task_list: List[str]):
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    """
    Find the package root and run the tests for the given tasks
    """
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    import pytest

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    package_root = find_test_root(start_path=pathlib.Path(__file__))
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    task_string = " or ".join(task_list)
    args = [
        f"{package_root}/tests/test_version_stable.py",
        f"--rootdir={package_root}",
        "-k",
        f"{task_string}",
    ]
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    sys.path.append(str(package_root))
    pytest_return_val = pytest.main(args)
    if pytest_return_val:
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        raise ValueError(
            f"Not all tests for the specified tasks ({task_list}) ran successfully! Error code: {pytest_return_val}"
        )
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def get_git_commit_hash():
    """
    Gets the git commit hash of your current repo (if it exists).
    Source: https://github.com/EleutherAI/gpt-neox/blob/b608043be541602170bfcfb8ec9bf85e8a0799e0/megatron/neox_arguments/neox_args.py#L42
    """
    try:
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        git_hash = subprocess.check_output(["git", "describe", "--always"]).strip()
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        git_hash = git_hash.decode()
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    except subprocess.CalledProcessError or FileNotFoundError:
        # FileNotFoundError occurs when git not installed on system
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        git_hash = None
    return git_hash


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def import_function(loader, node):
    function_name = loader.construct_scalar(node)
    yaml_path = os.path.dirname(loader.name)

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    module_name, function_name = function_name.split(".")
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    module_path = os.path.join(yaml_path, "{}.py".format(module_name))

    spec = importlib.util.spec_from_file_location(module_name, module_path)
    module = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(module)

    function = getattr(module, function_name)
    return function

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# Add the import_function constructor to the YAML loader
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yaml.add_constructor("!function", import_function)
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def load_yaml_config(yaml_path):
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    with open(yaml_path, "rb") as file:
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        yaml_config = yaml.full_load(file)
        yaml_dir = os.path.dirname(yaml_path)
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        if "include" in yaml_config:
            include_path = yaml_config["include"]
            del yaml_config["include"]
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            if type(include_path) == str:
                include_path = [include_path]
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            # Load from the last one first
            include_path.reverse()
            final_yaml_config = {}
            for path in include_path:
                # Assumes that path is a full path.
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                # If not found, assume the included yaml
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                # is in the same dir as the original yaml
                if not os.path.isfile(path):
                    path = os.path.join(yaml_dir, path)

                try:
                    included_yaml_config = load_yaml_config(path)
                    final_yaml_config.update(included_yaml_config)
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                except Exception as ex:
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                    # If failed to load, ignore
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                    raise ex
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            final_yaml_config.update(yaml_config)
            return final_yaml_config
        return yaml_config


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def regex_replace(string, pattern, repl, count: int = 0):
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    """Implements the `re.sub` function as a custom Jinja filter."""
    return re.sub(pattern, repl, string, count=count)
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env = Environment(loader=BaseLoader, undefined=StrictUndefined)
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env.filters["regex_replace"] = regex_replace
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def apply_template(template: str, doc: dict) -> str:
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    rtemplate = env.from_string(template)
    return rtemplate.render(**doc)
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def create_iterator(raw_iterator, rank, world_size, limit=None):
    """
    Method for creating a (potentially) sliced and limited
    iterator from a raw document iterator. Used for splitting data
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    among ranks in multigpu setting or only pulling a sample of documents
    """
    return islice(raw_iterator, rank, limit, world_size)
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def pad_and_concat(
    max_length: int,
    tensors: List[torch.Tensor],
    padding_side: Literal["right", "left"] = "right",
):
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    """
    Method for padding a list of tensors given the maximum tensor
    length in the batch. Used for batching inputs and continuations in
    seq2seq models.
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    """
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    assert (
        padding_side == "left" or padding_side == "right"
    ), f"Unrecognized padding type: '{padding_side}' not 'left' or 'right'"
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    for i, tensor in enumerate(tensors):
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        if len(tensor.shape) == 2:
            tensor = tensor.squeeze(0)  # squeeze, in case passed [1, seq] size
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        tensor_len = tensor.shape[0]
        if tensor_len < max_length:
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            if padding_side == "right":
                # right-pad
                tensors[i] = torch.cat(
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                    [
                        tensor,  # [seq]
                        torch.zeros(
                            max_length - tensor_len,
                            dtype=torch.long,
                            device=tensor.device,
                        ),  # [padding_length - seq]
                    ],
                    dim=0,
                ).unsqueeze(0)
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            else:
                # left-pad
                tensors[i] = torch.cat(
                    [
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                        torch.zeros(
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                            max_length - tensor_len,
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                            dtype=torch.long,
                            device=tensor.device,
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                        ),  # [padding_length - seq]
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                        tensor,  # [seq]
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                    ],
                    dim=0,
                ).unsqueeze(0)
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        else:
            tensors[i] = tensor.unsqueeze(0)

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    return torch.cat(tensors, dim=0)
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def clear_torch_cache() -> None:
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    gc.collect()
    torch.cuda.empty_cache()
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def get_dtype(dtype: Union[str, torch.dtype]) -> torch.dtype:
    """Converts `dtype` from `str` to torch.dtype when possible. Does not use an instantiated HF AutoConfig"""
    if isinstance(dtype, str) and dtype != "auto":
        # Convert `str` args torch dtype: `float16` -> `torch.float16`
        _torch_dtype = getattr(torch, dtype)
    else:
        _torch_dtype = dtype
    return _torch_dtype


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# Multi-token stopping criteria
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class MultiTokenEOSCriteria(transformers.StoppingCriteria):
    """Criteria to stop on the specified multi-token sequence."""

    def __init__(
        self,
        sequence: str,
        tokenizer: transformers.PreTrainedTokenizer,
        initial_decoder_input_length: int,
        batch_size: int,
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    ) -> None:
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        self.initial_decoder_input_length = initial_decoder_input_length
        self.done_tracker = [False] * batch_size
        self.sequence = sequence
        self.sequence_ids = tokenizer.encode(sequence, add_special_tokens=False)
        self.sequence_id_len = len(self.sequence_ids)
        self.tokenizer = tokenizer

    def __call__(self, input_ids, scores, **kwargs) -> bool:
        # For efficiency, we compare the last n tokens where n is the number of tokens in the stop_sequence
        lookback_ids_batch = input_ids[:, self.initial_decoder_input_length :][
            :, -self.sequence_id_len :
        ]

        lookback_tokens_batch = self.tokenizer.batch_decode(lookback_ids_batch)

        for i, done in enumerate(self.done_tracker):
            if not done:
                self.done_tracker[i] = self.sequence in lookback_tokens_batch[i]
        return False not in self.done_tracker


def stop_sequences_criteria(
    tokenizer: transformers.PreTrainedTokenizer,
    stop_sequences: List[str],
    initial_decoder_input_length: int,
    batch_size: int,
) -> transformers.StoppingCriteriaList:
    return transformers.StoppingCriteriaList(
        [
            *[
                MultiTokenEOSCriteria(
                    sequence, tokenizer, initial_decoder_input_length, batch_size
                )
                for sequence in stop_sequences
            ],
        ]
    )