chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,119 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+import numpy as np
+import torch
+from fairseq.data import Dictionary, FairseqDataset
+from fairseq.tasks import LegacyFairseqTask, register_task
+
+
+logger = logging.getLogger(__name__)
+
+
+@register_task("dummy_mt")
+class DummyMTTask(LegacyFairseqTask):
+    @staticmethod
+    def add_args(parser):
+        """Add task-specific arguments to the parser."""
+        parser.add_argument("--dict-size", default=49996, type=int)
+        parser.add_argument("--dataset-size", default=100000, type=int)
+        parser.add_argument("--src-len", default=30, type=int)
+        parser.add_argument("--tgt-len", default=30, type=int)
+
+    def __init__(self, args, dictionary):
+        super().__init__(args)
+        self.dictionary = dictionary
+        self.seed = args.seed
+
+        dictionary.pad_to_multiple_(8)  # often faster if divisible by 8
+
+        self.dummy_src = torch.arange(args.src_len + 1) + dictionary.pad() + 1
+        self.dummy_tgt = torch.arange(args.tgt_len + 1) + dictionary.pad() + 1
+
+    @classmethod
+    def setup_task(cls, args, **kwargs):
+        """Setup the task. """
+        dictionary = Dictionary()
+        for i in range(args.dict_size):
+            dictionary.add_symbol("word{}".format(i))
+        logger.info("dictionary: {} types".format(len(dictionary)))
+
+        args.max_source_positions = args.src_len + dictionary.pad() + 2
+        args.max_target_positions = args.tgt_len + dictionary.pad() + 2
+
+        return cls(args, dictionary)
+
+    def load_dataset(self, split, epoch=1, combine=False, **kwargs):
+        """Load a given dataset split.
+        Args:
+            split (str): name of the split (e.g., train, valid, test)
+        """
+        item_size = max(self.args.src_len, self.args.tgt_len)
+        if self.args.batch_size is not None:
+            bsz = self.args.batch_size
+        else:
+            bsz = max(1, self.args.max_tokens // item_size)
+        tgt = torch.stack([self.dummy_tgt for _ in range(bsz)])
+        self.datasets[split] = DummyDataset(
+            {
+                "id": 1,
+                "net_input": {
+                    "src_tokens": torch.stack([self.dummy_src for _ in range(bsz)]),
+                    "src_lengths": torch.full(
+                        (bsz,), self.args.src_len, dtype=torch.long
+                    ),
+                    "prev_output_tokens": tgt.clone(),
+                },
+                "target": tgt,
+                "nsentences": bsz,
+                "ntokens": bsz * self.args.tgt_len,
+            },
+            num_items=self.args.dataset_size,
+            item_size=item_size,
+        )
+
+    @property
+    def source_dictionary(self):
+        return self.dictionary
+
+    @property
+    def target_dictionary(self):
+        return self.dictionary
+
+
+class DummyDataset(FairseqDataset):
+    def __init__(self, batch, num_items, item_size):
+        super().__init__()
+        self.batch = batch
+        self.num_items = num_items
+        self.item_size = item_size
+
+    def __getitem__(self, index):
+        return index
+
+    def __len__(self):
+        return self.num_items
+
+    def collater(self, samples):
+        return self.batch
+
+    @property
+    def sizes(self):
+        return np.array([self.item_size] * self.num_items)
+
+    def num_tokens(self, index):
+        return self.item_size
+
+    def size(self, index):
+        return self.item_size
+
+    def ordered_indices(self):
+        return np.arange(self.num_items)
+
+    @property
+    def supports_prefetch(self):
+        return False