Add SQuAD Metric.

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 - Added NLP metrics:
   - `MatchErrorRate` ([#619](https://github.com/PyTorchLightning/metrics/pull/619))
+  - `SQuAD` ([#623](https://github.com/PyTorchLightning/metrics/pull/623))
 
 
 - Added `MinMaxMetric` to wrappers ([#556](https://github.com/PyTorchLightning/metrics/pull/556))

docs/source/links.rst

@@ -72,3 +72,4 @@
 .. _Python ROUGE Implementation: https://pypi.org/project/rouge-score/
 .. _Scikit_Learn-Ranking.py: https: //github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/_ranking.py
 .. _Verified Uncertainty Calibration: https://arxiv.org/abs/1909.10155
+.. _SQuAD Metric: https://arxiv.org/pdf/1606.05250.pdf

docs/source/references/functional.rst

@@ -458,6 +458,13 @@ sacre_bleu_score [func]
 .. autofunction:: torchmetrics.functional.sacre_bleu_score
     :noindex:
 
+
+squad [func]
+~~~~~~~~~~~~
+
+.. autofunction:: torchmetrics.functional.squad
+    :noindex:
+
 wer [func]
 ~~~~~~~~~~
 

docs/source/references/modules.rst

@@ -640,6 +640,13 @@ SacreBLEUScore
 .. autoclass:: torchmetrics.SacreBLEUScore
     :noindex:
 
+
+SQuAD
+~~~~~
+
+.. autoclass:: torchmetrics.SQuAD
+    :noindex:
+
 WER
 ~~~
 

tests/text/test_squad.py

@@ -0,0 +1,101 @@
+import os
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+from tests.helpers.testers import _assert_allclose, _assert_tensor
+from torchmetrics.functional.text import squad
+from torchmetrics.text.squad import SQuAD
+
+SAMPLE_1 = {
+    "exact_match": 100.0,
+    "f1": 100.0,
+    "predictions": {"prediction_text": "1976", "id": "id1"},
+    "references": {"answers": {"answer_start": [97], "text": ["1976"]}, "id": "id1"},
+}
+
+SAMPLE_2 = {
+    "exact_match": 0.0,
+    "f1": 0.0,
+    "predictions": {"prediction_text": "Hello", "id": "id2"},
+    "references": {"answers": {"answer_start": [97], "text": ["World"]}, "id": "id2"},
+}
+
+BATCH = {
+    "exact_match": [100.0, 0.0],
+    "f1": [100.0, 0.0],
+    "predictions": [
+        {"prediction_text": "1976", "id": "id1"},
+        {"prediction_text": "Hello", "id": "id2"},
+    ],
+    "references": [
+        {"answers": {"answer_start": [97], "text": ["1976"]}, "id": "id1"},
+        {"answers": {"answer_start": [97], "text": ["World"]}, "id": "id2"},
+    ],
+}
+
+
+@pytest.mark.parametrize(
+    "preds,targets,exact_match,f1",
+    [
+        (SAMPLE_1["predictions"], SAMPLE_1["references"], SAMPLE_1["exact_match"], SAMPLE_1["exact_match"]),
+        (SAMPLE_2["predictions"], SAMPLE_2["references"], SAMPLE_2["exact_match"], SAMPLE_2["exact_match"]),
+    ],
+)
+def test_score_fn(preds, targets, exact_match, f1):
+    """Tests for functional."""
+    metrics_score = squad(preds, targets)
+    _assert_tensor(metrics_score["exact_match"])
+    _assert_tensor(metrics_score["f1"])
+    _assert_allclose(metrics_score["exact_match"], exact_match)
+    _assert_allclose(metrics_score["f1"], f1)
+
+
+@pytest.mark.parametrize(
+    "preds,targets,exact_match,f1",
+    [(BATCH["predictions"], BATCH["references"], BATCH["exact_match"], BATCH["f1"])],
+)
+def test_accumulation(preds, targets, exact_match, f1):
+    """Tests for metric works with accumulation."""
+    squad_metric = SQuAD()
+    for pred, target in zip(preds, targets):
+        squad_metric.update(preds=[pred], targets=[target])
+    metrics_score = squad_metric.compute()
+
+    _assert_tensor(metrics_score["exact_match"])
+    _assert_tensor(metrics_score["f1"])
+    _assert_allclose(metrics_score["exact_match"], torch.mean(torch.tensor(exact_match)))
+    _assert_allclose(metrics_score["f1"], torch.mean(torch.tensor(f1)))
+
+
+def _squad_score_ddp(rank, world_size, pred, target, exact_match, f1):
+    """Define a DDP process for SQuAD metric."""
+    os.environ["MASTER_ADDR"] = "localhost"
+    os.environ["MASTER_PORT"] = "12355"
+    dist.init_process_group("gloo", rank=rank, world_size=world_size)
+    squad_metric = SQuAD()
+    squad_metric.update(pred, target)
+    metrics_score = squad_metric.compute()
+    _assert_tensor(metrics_score["exact_match"])
+    _assert_tensor(metrics_score["f1"])
+    _assert_allclose(metrics_score["exact_match"], exact_match)
+    _assert_allclose(metrics_score["f1"], f1)
+    dist.destroy_process_group()
+
+
+def _test_score_ddp_fn(rank, world_size, preds, targets, exact_match, f1):
+    """Core functionality for the `test_score_ddp` test."""
+    _squad_score_ddp(rank, world_size, preds[rank], targets[rank], exact_match[rank], f1[rank])
+
+
+@pytest.mark.parametrize(
+    "preds,targets,exact_match,f1",
+    [(BATCH["predictions"], BATCH["references"], BATCH["exact_match"], BATCH["f1"])],
+)
+@pytest.mark.skipif(not dist.is_available(), reason="test requires torch distributed")
+def test_score_ddp(preds, targets, exact_match, f1):
+    """Tests for metric using DDP."""
+    world_size = 2
+    mp.spawn(_test_score_ddp_fn, args=(world_size, preds, targets, exact_match, f1), nprocs=world_size, join=False)

torchmetrics/__init__.py

@@ -74,6 +74,7 @@
     MatchErrorRate,
     ROUGEScore,
     SacreBLEUScore,
+    SQuAD,
 )
 from torchmetrics.wrappers import BootStrapper, MetricTracker, MinMaxMetric, MultioutputWrapper  # noqa: E402
 
@@ -144,6 +145,7 @@
     "SNR",
     "SpearmanCorrcoef",
     "Specificity",
+    "SQuAD",
     "SSIM",
     "StatScores",
     "STOI",

torchmetrics/functional/__init__.py

@@ -71,6 +71,7 @@
 from torchmetrics.functional.text.mer import match_error_rate
 from torchmetrics.functional.text.rouge import rouge_score
 from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score
+from torchmetrics.functional.text.squad import squad
 from torchmetrics.functional.text.wer import wer
 
 __all__ = [
@@ -132,6 +133,7 @@
     "snr",
     "spearman_corrcoef",
     "specificity",
+    "squad",
     "ssim",
     "stat_scores",
     "stoi",

torchmetrics/functional/text/__init__.py

@@ -16,4 +16,5 @@
 from torchmetrics.functional.text.cer import char_error_rate  # noqa: F401
 from torchmetrics.functional.text.mer import match_error_rate  # noqa: F401
 from torchmetrics.functional.text.sacre_bleu import sacre_bleu_score  # noqa: F401
+from torchmetrics.functional.text.squad import squad  # noqa: F401
 from torchmetrics.functional.text.wer import wer  # noqa: F401