Add BestRQ pretraining

src/fairseq2/datasets/speech.py

@@ -8,19 +8,34 @@
 
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from pathlib import Path
+from pathlib import Pathx
 from typing import Final, final
 
 import torch
 from typing_extensions import override
 
-from fairseq2.datasets.config import Batching, DataReadOptions
+from fairseq2.assets import AssetCard, AssetError
+from fairseq2.data.audio import AudioDecoder, WaveformToFbankConverter
+from fairseq2.data.text import StrSplitter, TextTokenizer, read_text
+from fairseq2.datasets.batching import Batching
 from fairseq2.datasets.data_reader import DataPipelineReader, DataReader
 from fairseq2.datasets.hub import DatasetHubAccessor
 from fairseq2.error import NotSupportedError
 from fairseq2.gang import Gang
 from fairseq2.models.sequence import SequenceBatch
 from fairseq2.typing import DataType
+from fairseq2.nn.padding import get_seqs_and_padding_mask
+from fairseq2.datasets.batching import Batching, LengthBatching, StaticBatching
+from fairseq2.data import (
+    CollateOptionsOverride,
+    Collater,
+    DataPipeline,
+    DataPipelineBuilder,
+    FileMapper,
+    SequenceData,
+    create_bucket_sizes,
+    read_sequence,
+)
 
 
 class SpeechDataset(ABC):
@@ -70,14 +85,41 @@ class SpeechReadOptions(DataReadOptions):
 
 GENERIC_SPEECH_DATASET_FAMILY: Final = "generic_speech"
 
+# TODO: FIX, INFER
+npc = 10
 
 @final
 class GenericSpeechDataset(SpeechDataset):
     """Represents a generic manifest-based Speech dataset."""
 
-    @staticmethod
-    def from_path(path: Path, name: str) -> GenericSpeechDataset:
-        return GenericSpeechDataset()
+    _manifest_dir: Path
+    _splits: set[str]
+
+    def __init__(self, manifest_dir: Path, splits: set[str]) -> None:
+        """
+        :param manifest_dir:
+            The directory under which the manifest files resides.
+        :param splits:
+            The available splits.
+        """
+        self._manifest_dir = manifest_dir
+        self._splits = splits
+
+    @classmethod
+    def from_path(cls, path: Path) -> GenericSpeechDataset:
+        """Load a :class:`GenericSpeechDataset` from ``path``."""
+        path = path.expanduser().resolve()
+        if not path.is_dir():
+            return GenericSpeechDataset(manifest_dir=path.parent, splits={path.stem})
+
+        try:
+            splits = {f.stem for f in path.glob("*.tsv")}
+        except OSError as ex:
+            raise RuntimeError(
+                "The splits cannot be determined. See nested exception for details."
+            ) from ex
+
+        return GenericSpeechDataset(path, splits)
 
     @override
     def create_reader(
@@ -87,13 +129,205 @@ def create_reader(
         min_audio_len: int,
         max_audio_len: int,
         batching: Batching,
-        options: SpeechReadOptions | None = None,
+        *,
+        dtype: DataType = torch.float32,
+        min_audio_len: int = 1,
+        normalize_audio: bool = False,
+        example_shuffle_window: int = 1,
+        batch_shuffle_window: int = 1,
+        drop_remainder: bool = False,
+        sync_batches: bool = True,
+        sync_mode: Literal["until_first", "until_last"] = "until_first",
+        max_num_batches: int | None = None,
+        num_accumulate: int = 1,
+        num_prefetch: int = 1,
+        seed: int = 2,
+        cached_fd_count: int = 1000,
+        use_fbank: bool = False,
+        num_fbank_channels: int = 80,
+        fbank_stride: int = 2,
+        **extras: Any,
     ) -> DataPipelineReader[SequenceBatch]:
-        raise NotSupportedError("not supported yet.")
+        """
+        :param cached_fd_count:
+            The maximum number of file descriptors to keep open while reading
+            audio files.
+        """
+        if split not in self._splits:
+            raise ValueError(
+                f"`split` must be one of the following splits, but is '{split}' instead: {', '.join(sorted(self._splits))}"
+            )
+
+        audio_dir = self._retrieve_data_directory(split)
+
+        builder = self._read_manifest(split)
+
+        # Shuffle examples. Must be consistent across all processes.
+        if example_shuffle_window != 1:
+            builder.shuffle(example_shuffle_window, seed)
+
+        seed += 1
+
+        # Shard.
+        builder.shard(gang.rank, gang.size, allow_uneven=True)
+
+        seed += gang.rank
+
+        if isinstance(batching, LengthBatching):
+            # Bucket by the audio length.
+            bucket_sizes = create_bucket_sizes(
+                max_seq_len=max_audio_len,
+                min_seq_len=min_audio_len,
+                max_num_elements=batching.max_num_elements,
+                num_seqs_multiple_of=8,
+            )
+
+            builder.bucket_by_length(
+                bucket_sizes,
+                selector="audio_size",
+                min_data_len=min_audio_len,
+                skip_below_min_examples=True,
+                skip_above_max_examples=True,
+                drop_remainder=drop_remainder,
+            )
+        elif isinstance(batching, StaticBatching):
+            # Filter out out-of-range audios.
+            def skip(example: dict[str, Any]) -> bool:
+                audio_len = cast(int, example["audio_size"])
+
+                return audio_len >= min_audio_len and audio_len <= max_audio_len
+
+            builder.filter(skip)
+
+            # Bucket `batch_size` examples.
+            builder.bucket(batching.batch_size, drop_remainder=drop_remainder)
+        else:
+            raise RuntimeError(f"`{batching}` is not supported.")
+
+        # Shuffle buckets.
+        if batch_shuffle_window != 1:
+            builder.shuffle(batch_shuffle_window, seed)
+
+        seed += 1
+
+        # Memory map audio files.
+        file_mapper = FileMapper(audio_dir, cached_fd_count=cached_fd_count)
+
+        builder.map(file_mapper, selector="[*].audio")
+
+
+        # Decode audio.
+        audio_decoder = AudioDecoder(dtype=torch.float32 if normalize_audio else dtype)
+
+        builder.map(audio_decoder, selector="[*].audio.data")
+
+        # TODO(balioglu): Check/adjust sample size
+
+        # Normalize audio if requested.
+        def normalize(waveform: Tensor) -> Tensor:
+            with torch.no_grad():
+                waveform = layer_norm(waveform, waveform.shape)
+
+            return waveform.to(dtype)
+
+        if normalize_audio:
+            builder.map(normalize, selector="[*].audio.data.waveform")
+
+        if use_fbank:
+            fbank_converter = WaveformToFbankConverter(
+                num_mel_bins=num_fbank_channels,
+                waveform_scale=1.0,
+                channel_last=True,
+                standardize=False,
+                keep_waveform=True,
+            )
+            builder.map(fbank_converter, selector="[*].audio.data")
+
+        collater = Collater(pad_value=0)
+
+        builder.map(collater, num_parallel_calls=npc)
+
+        # Return only the first `max_num_batches`.
+        if max_num_batches is not None:
+            builder.take(max_num_batches)
+
+        # Prefetch `num_prefetch` batches in background.
+        builder.prefetch(num_prefetch)
+
+        # Wrap examples with `Seq2SeqBatch`.
+        def to_batch(example: dict[str, Any]) -> SequenceBatch:
+            if use_fbank:
+                source_data = cast(SequenceData, example["audio"]["data"]["fbank"])
+            else:
+                source_data = cast(SequenceData, example["audio"]["data"]["waveform"])
+
+            source_seqs, source_padding_mask = get_seqs_and_padding_mask(
+                source_data, gang.device
+            )
+            return SequenceBatch(
+                seqs=source_seqs,
+                padding_mask=source_padding_mask,
+                example=example,
+            )
+
+        pipeline = builder.map(to_batch).and_return()
+
+        return DataPipelineReader[SequenceBatch](
+            pipeline,
+            gang,
+            num_accumulate=num_accumulate,
+            drop_remainder=drop_remainder,
+            sync_batches=sync_batches,
+            sync_mode=sync_mode,
+        )
+
+    def _retrieve_data_directory(self, split: str) -> Path:
+        manifest_file = self._manifest_dir.joinpath(f"{split}.tsv")
+
+        try:
+            with manifest_file.open() as fp:
+                line = fp.readline().rstrip()
+        except OSError as ex:
+            raise DatasetError(
+                f"{manifest_file} cannot be read. See nested exception for details."
+            ) from ex
+
+        try:
+            return Path(line)
+        except ValueError:
+            raise DatasetError(
+                f"The first line of {manifest_file} must point to a data directory."
+            ) from None
+
+    def _read_manifest(self, split: str) -> DataPipelineBuilder:
+        def read_tsv_file() -> DataPipelineBuilder:
+            tsv_file = self._manifest_dir.joinpath(f"{split}.tsv")
+
+            builder = read_text(tsv_file, rtrim=True, memory_map=True)
+
+            builder.skip(1)  # Path to the data directory.
+
+            field_splitter = StrSplitter(names=["audio", "audio_size"])
+
+            builder.map(field_splitter, num_parallel_calls=npc)
+
+            return builder
+
+        tsv_pipeline = read_tsv_file().and_return()
+
+        builder = DataPipeline.zip([tsv_pipeline], flatten=True)
+
+        # Cast audio size to integer.
+        builder.map(int, selector="audio_size")
+
+        # TODO(balioglu): Use `cache()` op.
+        manifest = list(builder.and_return())
+
+        return read_sequence(manifest)
 
     @override
     def splits(self) -> set[str]:
-        return set()
+        return self._splits
 
 
 get_speech_dataset_hub = DatasetHubAccessor(SpeechDataset)

src/fairseq2/models/bestrq/__init__.py

@@ -0,0 +1,37 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+from fairseq2.models.bestrq.factory import BESTRQ_FAMILY as BESTRQ_FAMILY
+from fairseq2.models.bestrq.factory import BestRQBuilder as BestRQBuilder
+from fairseq2.models.bestrq.factory import BestRQConfig as BestRQConfig
+from fairseq2.models.bestrq.factory import (
+    create_bestrq_model as create_bestrq_model,
+)
+from fairseq2.models.bestrq.factory import bestrq_arch as bestrq_arch
+from fairseq2.models.bestrq.factory import bestrq_archs as bestrq_archs
+from fairseq2.models.bestrq.factory import (
+    bestrq_encoder_arch as bestrq_encoder_arch,
+)
+from fairseq2.models.bestrq.factory import (
+    bestrq_encoder_archs as bestrq_encoder_archs,
+)
+from fairseq2.models.bestrq.masker import RandomNoiseMasker as RandomNoiseMasker
+from fairseq2.models.bestrq.model import BestRQFeatures as BestRQFeatures
+from fairseq2.models.bestrq.model import BestRQLoss as BestRQLoss
+from fairseq2.models.bestrq.model import BestRQModel as BestRQModel
+from fairseq2.models.bestrq.model import BestRQOutput as BestRQOutput
+from fairseq2.models.bestrq.quantizer import MultiRandomVectorQuantizerOutput
+
+# isort: split
+
+from fairseq2.dependency import DependencyContainer
+from fairseq2.models.bestrq.archs import register_archs
+
+
+def register_bestrq(container: DependencyContainer) -> None:
+    register_archs()

src/fairseq2/models/bestrq/archs.py

@@ -0,0 +1,29 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+from fairseq2.models.bestrq.factory import (
+    BestRQConfig,
+    bestrq_arch,
+    bestrq_encoder_arch,
+)
+from fairseq2.models.wav2vec2.factory import (
+    Wav2Vec2EncoderConfig,
+)
+from fairseq2.nn.transformer import TransformerNormOrder
+
+
+def register_archs() -> None:
+    @bestrq_arch("base")
+    def _base() -> BestRQConfig:
+        return BestRQConfig()
+
+    @bestrq_encoder_arch("base")
+    def _base_encoder() -> Wav2Vec2EncoderConfig:
+        config = _base()
+
+        return config.encoder_config