extract metric code for SyntaxGym metric space

prediction.py

@@ -1,235 +0,0 @@
-from typing import Union, Optional as TOptional, List as TList
-
-
-from pyparsing import *
-import numpy as np
-
-METRICS = {
-    'sum': sum,
-    'mean': np.mean,
-    'median': np.median,
-    'range': np.ptp,
-    'max': max,
-    'min': min
-}
-
-
-# Enable parser packrat (caching)
-ParserElement.enablePackrat()
-
-# Relative and absolute tolerance thresholds for surprisal equality
-EQUALITY_RTOL = 1e-5
-EQUALITY_ATOL = 1e-3
-
-
-#######
-# Define a grammar for prediction formulae.
-
-# References a surprisal region
-lpar = Suppress("(")
-rpar = Suppress(")")
-region = lpar + (Word(nums) | "*") + Suppress(";%") + Word(alphanums + "_-") + Suppress("%") + rpar
-literal_float = pyparsing_common.number
-
-class Region(object):
-    def __init__(self, tokens):
-        self.region_number = tokens[0]
-        self.condition_name = tokens[1]
-
-    def __str__(self):
-        return "(%s;%%%s%%)" % (self.region_number, self.condition_name)
-
-    def __repr__(self):
-        return "Region(%s,%s)" % (self.condition_name, self.region_number)
-
-    def __call__(self, surprisal_dict):
-        if self.region_number == "*":
-            return sum(value for (condition, region), value in surprisal_dict.items()
-                       if condition == self.condition_name)
-
-        return surprisal_dict[self.condition_name, int(self.region_number)]
-
-class LiteralFloat(object):
-    def __init__(self, tokens):
-        self.value = float(tokens[0])
-
-    def __str__(self):
-        return "%f" % (self.value,)
-
-    def __repr__(self):
-        return "LiteralFloat(%f)" % (self.value,)
-
-    def __call__(self, surprisal_dict):
-        return self.value
-
-class BinaryOp(object):
-    operators: TOptional[TList[str]]
-
-    def __init__(self, tokens):
-        self.operator = tokens[0][1]
-        if self.operators is not None and self.operator not in self.operators:
-            raise ValueError("Invalid %s operator %s" % (self.__class__.__name__,
-                                                            self.operator))
-        self.operands = [tokens[0][0], tokens[0][2]]
-
-    def __str__(self):
-        return "(%s %s %s)" % (self.operands[0], self.operator, self.operands[1])
-
-    def __repr__(self):
-        return "%s(%s)(%s)" % (self.__class__.__name__, self.operator, ",".join(map(repr, self.operands)))
-
-    def __call__(self, surprisal_dict):
-        op_vals = [op(surprisal_dict) for op in self.operands]
-        return self._evaluate(op_vals, surprisal_dict)
-
-    def _evaluate(self, evaluated_operands, surprisal_dict):
-        raise NotImplementedError()
-
-class BoolOp(BinaryOp):
-    operators = ["&", "|"]
-    def _evaluate(self, op_vals, surprisal_dict):
-        if self.operator == "&":
-            return op_vals[0] and op_vals[1]
-        elif self.operator == "|":
-            return op_vals[0] or op_vals[1]
-
-class FloatOp(BinaryOp):
-    operators = ["-", "+"]
-    def _evaluate(self, op_vals, surprisal_dict):
-        if self.operator == "-":
-            return op_vals[0] - op_vals[1]
-        elif self.operator == "+":
-            return op_vals[0] + op_vals[1]
-
-class ComparatorOp(BinaryOp):
-    operators = ["<", ">", "="]
-    def _evaluate(self, op_vals, surprisal_dict):
-        if self.operator == "<":
-            return op_vals[0] < op_vals[1]
-        elif self.operator == ">":
-            return op_vals[0] > op_vals[1]
-        elif self.operator == "=":
-            return np.isclose(op_vals[0], op_vals[1],
-                                rtol=EQUALITY_RTOL,
-                                atol=EQUALITY_ATOL)
-
-def Chain(op_cls, left_assoc=True):
-    def chainer(tokens):
-        """
-        Create a binary tree of BinaryOps from the given repeated application
-        of the op.
-        """
-        operators = tokens[0][1::2]
-        args = tokens[0][0::2]
-        if not left_assoc:
-            raise NotImplementedError
-
-        arg1 = args.pop(0)
-        while len(args) > 0:
-            operator = operators.pop(0)
-            arg2 = args.pop(0)
-            arg1 = op_cls([[arg1, operator, arg2]])
-
-        return arg1
-
-    return chainer
-
-atom = region.setParseAction(Region) | literal_float.setParseAction(LiteralFloat)
-
-prediction_expr = infixNotation(
-    atom,
-    [
-        (oneOf("- +"), 2, opAssoc.LEFT, Chain(FloatOp)),
-        (oneOf("< > ="), 2, opAssoc.LEFT, ComparatorOp),
-        (oneOf("& |"), 2, opAssoc.LEFT, Chain(BoolOp)),
-    ],
-    lpar=lpar, rpar=rpar
-)
-
-
-class Prediction(object):
-    """
-    Predictions state expected relations between language model surprisal
-    measures in different regions and conditions of a test suite. For more
-    information, see :ref:`architecture`.
-    """
-
-    def __init__(self, idx: int, formula: Union[str, BinaryOp], metric: str):
-        """
-        Args:
-            idx: A unique prediction ID. This is only relevant for
-                serialization.
-            formula: A string representation of the prediction formula, or an
-                already parsed formula. For more information, see
-                :ref:`architecture`.
-            metric: Metric for aggregating surprisals within regions.
-        """
-        if isinstance(formula, str):
-            try:
-                formula = prediction_expr.parseString(formula, parseAll=True)[0]
-            except ParseException as e:
-                raise ValueError("Invalid formula expression %r" % (formula,)) from e
-
-        self.idx = idx
-        self.formula = formula
-
-        if metric not in METRICS.keys():
-            raise ValueError("Unknown metric %s. Supported metrics: %s" %
-                             (metric, " ".join(METRICS.keys())))
-        self.metric = metric
-
-    def __call__(self, item):
-        """
-        Evaluate the prediction on the given item dict representation. For more
-        information on item representations, see :ref:`suite_json`.
-        """
-        # Prepare relevant surprisal dict
-        surps = {(c["condition_name"], r["region_number"]): r["metric_value"][self.metric]
-                 for c in item["conditions"]
-                 for r in c["regions"]}
-        return self.formula(surps)
-
-    @classmethod
-    def from_dict(cls, pred_dict, idx: int, metric: str):
-        """
-        Parse from a prediction dictionary representation (see
-        :ref:`suite_json`).
-        """
-        if not pred_dict["type"] == "formula":
-            raise ValueError("Unknown prediction type %s" % (pred_dict["type"],))
-
-        return cls(formula=pred_dict["formula"], idx=idx, metric=metric)
-
-    @property
-    def referenced_regions(self):
-        """
-        Get a set of the regions referenced by this formula.
-        Each item is a tuple of the form ``(condition_name, region_number)``.
-        """
-        def traverse(x, acc):
-            if isinstance(x, BinaryOp):
-                for val in x.operands:
-                    traverse(val, acc)
-            elif isinstance(x, Region):
-                acc.add((x.condition_name, int(x.region_number)))
-
-            return acc
-
-        return traverse(self.formula, set())
-
-    def as_dict(self):
-        """
-        Serialize as a prediction dictionary representation (see
-        :ref:`suite_json`).
-        """
-        return dict(type="formula", formula=str(self.formula))
-
-    def __str__(self):
-        return "Prediction(%s)" % (self.formula,)
-    __repr__ = __str__
-
-    def __hash__(self):
-        return hash(self.formula)
-
-    def __eq__(self, other):
-        return isinstance(other, Prediction) and hash(self) == hash(other)

syntaxgym.py

@@ -19,8 +19,6 @@ import numpy as np
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-from .prediction import Prediction
-
 
 _CITATION = """
 @inproceedings{Hu:et-al:2020,
@@ -126,185 +124,3 @@ class SyntaxGym(datasets.GeneratorBasedBuilder):
 
             yield item["item_number"], item
 
-
-class SyntaxGymMetricResult(TypedDict):
-    prediction_results: List[List[bool]]
-    region_totals: List[Dict[Tuple[str, int], float]]
-
-
-class SyntaxGymMetric(datasets.Metric):
-    """
-    SyntaxGym prediction evaluation metric.
-    """
-
-    def _info(self):
-        seq = datasets.Sequence
-        features = datasets.Features({
-            "suite": SUITE_DATASET_SPEC
-        })
-        return datasets.MetricInfo(
-            description="TODO",
-            citation=_CITATION,
-            inputs_description="TODO",
-            features=features,
-        )
-
-    def _compute(self, suite, model_id, batch_size: int = 16,
-                 add_start_token=True, device=None
-                 ) -> SyntaxGymMetricResult:
-        if device is not None:
-            assert device in ["gpu", "cpu", "cuda"]
-            if device == "gpu":
-                device = "cuda"
-        else:
-            device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        model = AutoModelForCausalLM.from_pretrained(model_id)
-        model = model.to(device)
-        model.eval()
-
-        tokenizer = AutoTokenizer.from_pretrained(model_id)
-        # TODO copy from perplexity metric
-        tokenizer.pad_token = tokenizer.eos_token
-
-        results = {"prediction_results": [], "region_totals": []}
-        # TODO batch all items together
-        for item in logging.tqdm(suite):
-            result_single = self._compute_single(item, tokenizer, model, device)
-
-            for k in ["prediction_results", "region_totals"]:
-                results[k].append(result_single[k])
-
-        return results
-
-
-    def _compute_single(self, item, tokenizer, model, device):
-        tokenized = tokenizer(item["conditions"]["content"],
-                              padding=True,
-                              return_tensors="pt",
-                              return_offsets_mapping=True).to(device)
-
-        # input_ids: B * T
-        input_ids = tokenized["input_ids"]
-        assert input_ids.ndim == 2
-
-        # Compute sentence level surprisals.
-        with torch.no_grad():
-            # Pre-softmax predictive distribution B * T * V
-            logits = model(input_ids).logits
-            surprisals = -logits.log_softmax(dim=2) / np.log(2)
-
-        # surprisals: B * T * V
-        assert surprisals.ndim == 3
-
-        # Get surprisals of expected words.
-        surps_shifted = surprisals[:, :-1, :]
-        expected_ids = input_ids[:, 1:]
-
-        # TODO: check this logic
-        tt = expected_ids.unsqueeze(2)
-        # reindexed surprisals: B * (T - 1)
-        surprisals = torch.gather(surps_shifted, 2, expected_ids.unsqueeze(2)) \
-            .squeeze(2)
-        # This is the original, which works but not with multiple axes in expected_ids
-        # surprisals = surps_shifted[range(surps_shifted.shape[0]), expected_ids]
-
-        # surprisals is now B * (T - 1)
-
-        #### aggregate
-        condition_names = item["conditions"]["condition_name"]
-        region_totals = {condition_name: defaultdict(float)
-                         for condition_name in condition_names}
-        region2tokens = self.compute_region_token_mapping(
-            item, input_ids, tokenized["offset_mapping"])
-
-        for i, (i_cond, i_inputs) in enumerate(zip(condition_names, input_ids)):
-            for region_number, region_tokens in region2tokens[i_cond].items():
-                for token in region_tokens:
-                    if token == 0:
-                        # surprisal not defined. pass.
-                        continue
-                    elif token <= surprisals.shape[1]:
-                        region_totals[i_cond][region_number] += surprisals[i, token - 1]
-                    else:
-                        # TODO don't think this is an issue, just should clean
-                        # up the aggregation output
-                        assert token == surprisals.shape[1], \
-                            "%s %s" % (token, surprisals.shape[1])
-
-        region_totals = {(condition_name, region_number): float(total)
-                         for condition_name, totals in region_totals.items()
-                         for region_number, total in totals.items()}
-
-        results = {
-            "prediction_results": [
-                Prediction(i, formula, "sum").formula(region_totals)
-                for i, formula in enumerate(item["predictions"])
-            ],
-
-            "region_totals": region_totals
-        }
-        return results
-
-    def get_region_edges(self, item, condition_idx):
-        """
-        Get left edge of each region as a character index.
-        """
-        # NB this is coupled with `condition_to_string` logic of course
-
-        regions = item["conditions"]["regions"][condition_idx]
-
-        idx = 0
-        ret = []
-        for r_idx, region_content in enumerate(regions["content"]):
-            ret.append(idx)
-
-            region_size = len(region_content)
-            if region_content.strip() != "" and r_idx != 0 and not region_content.startswith(","):
-                # Add joining space
-                region_size += 1
-
-            idx += region_size
-
-        return ret
-
-    def compute_region_token_mapping(self, item, input_ids: torch.LongTensor,
-                                     offset_mapping: List[Tuple[int, int]]
-                                     ) -> Dict[str, Dict[int, List[int]]]:
-        # input_ids: B * T
-        # offset_mapping: B * T * 2
-        # assumes batch is sorted according to item's condition_name order
-
-        condition_names = item["conditions"]["condition_name"]
-        region2tokens = {cond: defaultdict(list) for cond in condition_names}
-
-        max_long = torch.iinfo(torch.int64).max
-
-        input_ids = input_ids.detach()
-        for i_cond, (i_tokens, i_offsets) in enumerate(zip(input_ids, offset_mapping)):
-            region_edges = self.get_region_edges(item, i_cond)
-
-            t_cursor, r_cursor = 0, 0
-            while t_cursor < i_tokens.shape[0]:
-                # token = i_tokens[t_cursor]
-                token_char_start, token_char_end = i_offsets[t_cursor]
-
-                if token_char_start == token_char_end == 0:
-                    # This is a padding token. Skip.
-                    # TODO what about BOS/EOS? some models incorporate them
-                    t_cursor += 1
-                    continue
-
-                region_start = region_edges[r_cursor]
-                region_end = region_edges[r_cursor + 1] \
-                    if r_cursor + 1 < len(region_edges) else max_long
-
-                # NB region boundaries are left edges, hence the >= here.
-                if token_char_start >= region_end:
-                    r_cursor += 1
-                    continue
-
-                region2tokens[condition_names[i_cond]][r_cursor + 1].append(t_cursor)
-                t_cursor += 1
-
-        return region2tokens

test.py

@@ -1,515 +0,0 @@
-from typing import List
-
-import datasets
-import numpy as np
-
-import pytest
-
-
-@pytest.fixture(scope="session")
-def syntaxgym_dataset():
-    return datasets.load_dataset("syntaxgym", "subordination_src-src")
-
-
-@pytest.fixture(scope="session")
-def syntaxgym_metric():
-    return datasets.load_metric("syntaxgym")
-
-
-@pytest.fixture(scope="session")
-def model_ref():
-    # return "hf-internal-testing/tiny-random-gpt_neo"
-    return "gpt2"
-
-
-# Reference region surprisals computed with syntaxgym-core.
-# See notebook in https://colab.research.google.com/drive/1qziyPcu65jffizSPi-ZGHKR0x7BaHFMS#scrollTo=RgtnScy6LLKi .
-GPT2_SUBORDINATION_SRC_REFERENCE = \
-[{('no-sub_matrix', 1): 13.151199615123803,
-  ('no-sub_matrix', 2): 38.503222716703526,
-  ('no-sub_matrix', 3): 27.623861034812286,
-  ('no-sub_matrix', 4): 48.831672846038224,
-  ('no-sub_matrix', 5): 38.08533699286694,
-  ('no-sub_no-matrix', 1): 13.151199615123803,
-  ('no-sub_no-matrix', 2): 38.503222716703526,
-  ('no-sub_no-matrix', 3): 27.623861034812286,
-  ('no-sub_no-matrix', 4): 48.831687980511504,
-  ('no-sub_no-matrix', 5): 1.8096143510772873,
-  ('sub_matrix', 1): 14.905592916748805,
-  ('sub_matrix', 2): 39.06304309956175,
-  ('sub_matrix', 3): 26.862648365854433,
-  ('sub_matrix', 4): 50.56554401687938,
-  ('sub_matrix', 5): 26.532245572980194,
-  ('sub_no-matrix', 1): 14.905592916748805,
-  ('sub_no-matrix', 2): 39.06304309956175,
-  ('sub_no-matrix', 3): 26.862648365854433,
-  ('sub_no-matrix', 4): 50.56553438585093,
-  ('sub_no-matrix', 5): 7.470089829866611},
- {('no-sub_matrix', 1): 10.116093820255577,
-  ('no-sub_matrix', 2): 20.96513246705127,
-  ('no-sub_matrix', 3): 20.02959138986416,
-  ('no-sub_matrix', 4): 23.779661397107446,
-  ('no-sub_matrix', 5): 33.2560281692696,
-  ('no-sub_no-matrix', 1): 10.116093820255577,
-  ('no-sub_no-matrix', 2): 20.96513246705127,
-  ('no-sub_no-matrix', 3): 20.02959138986416,
-  ('no-sub_no-matrix', 4): 23.779661397107446,
-  ('no-sub_no-matrix', 5): 1.9449125865631063,
-  ('sub_matrix', 1): 13.545157521732826,
-  ('sub_matrix', 2): 24.96048395897244,
-  ('sub_matrix', 3): 18.609464944317324,
-  ('sub_matrix', 4): 23.057566440062317,
-  ('sub_matrix', 5): 26.424454285669032,
-  ('sub_no-matrix', 1): 13.545157521732826,
-  ('sub_no-matrix', 2): 24.96048395897244,
-  ('sub_no-matrix', 3): 18.609464944317324,
-  ('sub_no-matrix', 4): 23.057566440062317,
-  ('sub_no-matrix', 5): 2.807467838359704},
- {('no-sub_matrix', 1): 11.992867568477442,
-  ('no-sub_matrix', 2): 45.813114232935774,
-  ('no-sub_matrix', 3): 24.57554828372551,
-  ('no-sub_matrix', 4): 45.334025774062916,
-  ('no-sub_matrix', 5): 26.208189541862073,
-  ('no-sub_no-matrix', 1): 11.992867568477442,
-  ('no-sub_no-matrix', 2): 45.813114232935774,
-  ('no-sub_no-matrix', 3): 24.57554828372551,
-  ('no-sub_no-matrix', 4): 45.33402766587207,
-  ('no-sub_no-matrix', 5): 1.8284485151385752,
-  ('sub_matrix', 1): 14.219887768799735,
-  ('sub_matrix', 2): 46.25055434117979,
-  ('sub_matrix', 3): 23.054221678472672,
-  ('sub_matrix', 4): 47.08503858470256,
-  ('sub_matrix', 5): 22.154772321452022,
-  ('sub_no-matrix', 1): 14.219887768799735,
-  ('sub_no-matrix', 2): 46.25055434117979,
-  ('sub_no-matrix', 3): 23.054221678472672,
-  ('sub_no-matrix', 4): 47.08503858470256,
-  ('sub_no-matrix', 5): 3.0655133594366757},
- {('no-sub_matrix', 1): 10.55002943802296,
-  ('no-sub_matrix', 2): 52.419810137608856,
-  ('no-sub_matrix', 3): 23.30710475332303,
-  ('no-sub_matrix', 4): 37.957905964008944,
-  ('no-sub_matrix', 5): 29.259648135104936,
-  ('no-sub_no-matrix', 1): 10.55002943802296,
-  ('no-sub_no-matrix', 2): 52.419810137608856,
-  ('no-sub_no-matrix', 3): 23.30710475332303,
-  ('no-sub_no-matrix', 4): 37.957905964008944,
-  ('no-sub_no-matrix', 5): 1.9632913405649093,
-  ('sub_matrix', 1): 15.289384584900025,
-  ('sub_matrix', 2): 53.93652737134243,
-  ('sub_matrix', 3): 19.43915835312633,
-  ('sub_matrix', 4): 36.459591551099386,
-  ('sub_matrix', 5): 22.185742699245417,
-  ('sub_no-matrix', 1): 15.289384584900025,
-  ('sub_no-matrix', 2): 53.93652737134243,
-  ('sub_no-matrix', 3): 19.43915835312633,
-  ('sub_no-matrix', 4): 36.4595598203003,
-  ('sub_no-matrix', 5): 5.707732355645454},
- {('no-sub_matrix', 1): 23.543723213902986,
-  ('no-sub_matrix', 2): 31.967972102825854,
-  ('no-sub_matrix', 3): 29.159572978411727,
-  ('no-sub_matrix', 4): 36.61365345925747,
-  ('no-sub_matrix', 5): 44.576591305970545,
-  ('no-sub_no-matrix', 1): 23.543723213902986,
-  ('no-sub_no-matrix', 2): 31.967972102825854,
-  ('no-sub_no-matrix', 3): 29.159572978411727,
-  ('no-sub_no-matrix', 4): 36.61365345925747,
-  ('no-sub_no-matrix', 5): 3.2813457388593714,
-  ('sub_matrix', 1): 27.118410129310597,
-  ('sub_matrix', 2): 33.909617362987866,
-  ('sub_matrix', 3): 28.791166362258743,
-  ('sub_matrix', 4): 37.24960609010374,
-  ('sub_matrix', 5): 31.660933798006262,
-  ('sub_no-matrix', 1): 27.118410129310597,
-  ('sub_no-matrix', 2): 33.909617362987866,
-  ('sub_no-matrix', 3): 28.791166362258743,
-  ('sub_no-matrix', 4): 37.24960609010374,
-  ('sub_no-matrix', 5): 7.3613541428239015},
- {('no-sub_matrix', 1): 14.22171869610082,
-  ('no-sub_matrix', 2): 30.270423022911977,
-  ('no-sub_matrix', 3): 25.973276891204705,
-  ('no-sub_matrix', 4): 28.43856735947716,
-  ('no-sub_matrix', 5): 57.39887418731055,
-  ('no-sub_no-matrix', 1): 14.22171869610082,
-  ('no-sub_no-matrix', 2): 30.270423022911977,
-  ('no-sub_no-matrix', 3): 25.973276891204705,
-  ('no-sub_no-matrix', 4): 28.43856735947716,
-  ('no-sub_no-matrix', 5): 1.7127059109344136,
-  ('sub_matrix', 1): 16.39289784951447,
-  ('sub_matrix', 2): 31.5671111565765,
-  ('sub_matrix', 3): 24.54307828171008,
-  ('sub_matrix', 4): 29.249645624130757,
-  ('sub_matrix', 5): 53.59155769093577,
-  ('sub_no-matrix', 1): 16.39289784951447,
-  ('sub_no-matrix', 2): 31.5671111565765,
-  ('sub_no-matrix', 3): 24.54307828171008,
-  ('sub_no-matrix', 4): 29.249645624130757,
-  ('sub_no-matrix', 5): 7.225276653947023},
- {('no-sub_matrix', 1): 13.729688714733188,
-  ('no-sub_matrix', 2): 36.018118127225165,
-  ('no-sub_matrix', 3): 28.232055923783275,
-  ('no-sub_matrix', 4): 44.44634394296659,
-  ('no-sub_matrix', 5): 38.277975147059344,
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-
-
-def test_gpt_subordination_region_totals():
-    """
-    Check region-level surprisals against the original syntaxgym-core
-    implementation, using the same underlying `gpt2` model.
-    """
-    reference = ...  # TODO
-
-    # TODO work out references
-    dataset = datasets.load_dataset("./syntaxgym.py", "subordination_src-src")
-    metric = datasets.load_metric("./syntaxgym.py")
-    result = metric.compute(suite=dataset["test"], model_id="gpt2")
-
-    from pprint import pprint
-    pprint(result["region_totals"][0])
-    pprint(GPT2_SUBORDINATION_SRC_REFERENCE[0])
-
-    keys = result["region_totals"][0].keys()
-    assert set(keys) == set(GPT2_SUBORDINATION_SRC_REFERENCE[0].keys())
-
-    result_ndarray = np.concatenate([np.array([region_totals[key] for key in keys])
-                                     for region_totals in result["region_totals"]])
-    reference_ndarray = np.concatenate([np.array([region_totals[key] for key in keys])
-                                        for region_totals in GPT2_SUBORDINATION_SRC_REFERENCE])
-    pprint(sorted(zip(keys, np.abs(result_ndarray - reference_ndarray)),
-                  key=lambda x: -x[1]))
-    np.testing.assert_allclose(result_ndarray, reference_ndarray, atol=1e-3)