utils.py 5.43 KB
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import os
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import pathlib
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import re
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import collections
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import functools
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import inspect
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import sys
from typing import List
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from omegaconf import OmegaConf

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class ExitCodeError(Exception):
    pass


def sh(x):
    if os.system(x):
        raise ExitCodeError()


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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):
    arr = []
    for x in iter:
        arr.append(x)
        if len(arr) == n:
            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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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:
    def __init__(self, arr, fn):
        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]
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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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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}"
        )