Introduce mBart

src/fairseq2/models/mbart/__init__.py

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+# 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 fairseq2.models.mbart.builder import create_mbart_model as create_mbart_model
+from fairseq2.models.mbart.builder import mbart_arch as mbart_arch
+from fairseq2.models.mbart.builder import mbart_archs as mbart_archs
+from fairseq2.models.mbart.builder import mBartBuilder as mBartBuilder
+from fairseq2.models.mbart.builder import mBartConfig as mBartConfig
+from fairseq2.models.mbart.loader import load_mbart_model as load_mbart_model
+from fairseq2.models.mbart.loader import load_mbart_tokenizer as load_mbart_tokenizer
+from fairseq2.models.mbart.loader import mBartLoader as mBartLoader
+from fairseq2.models.mbart.tokenizer import mBartTokenizer as mBartTokenizer

src/fairseq2/models/mbart/builder.py

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+# 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 dataclasses import dataclass
+from typing import Literal, Optional
+
+from fairseq2.data import VocabularyInfo
+from fairseq2.models.transformer import (
+    TransformerEmbeddingFrontend,
+    TransformerFrontend,
+    TransformerModel,
+)
+from fairseq2.models.utils.arch_registry import ArchitectureRegistry
+from fairseq2.nn.embedding import Embedding
+from fairseq2.nn.position_encoder import (
+    LearnedPositionEncoder,
+    SinusoidalPositionEncoder,
+)
+from fairseq2.nn.projection import TiedProjection
+from fairseq2.nn.transformer import (
+    FeedForwardNetwork,
+    MultiheadAttention,
+    StandardFeedForwardNetwork,
+    StandardMultiheadAttention,
+    StandardTransformerDecoder,
+    StandardTransformerDecoderLayer,
+    StandardTransformerEncoder,
+    StandardTransformerEncoderLayer,
+    TransformerDecoder,
+    TransformerDecoderLayer,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+    TransformerNormOrder,
+    create_default_sdpa,
+)
+from fairseq2.typing import DataType, Device
+
+
+@dataclass
+class mBartConfig:
+    """Holds the configuration of an mBart model."""
+
+    model_dim: int
+    """The dimensionality of the model."""
+
+    max_seq_len: int
+    """The expected maximum sequence length."""
+
+    vocabulary_size: int
+    """The size of the vocabulary."""
+
+    pad_idx: Optional[int]
+    """The index of the pad symbol in the vocabulary."""
+
+    num_encoder_layers: int
+    """The number of Transformer encoder layers."""
+
+    num_decoder_layers: int
+    """The number of Transformer decoder layers."""
+
+    num_encoder_attn_heads: int
+    """The number of attention heads in Transformer encoder layers."""
+
+    num_decoder_attn_heads: int
+    """The number of attention heads in Transformer decoder layers."""
+
+    ffn_inner_dim: int
+    """The inner dimensionality of Transformer feed-forward networks."""
+
+    # Position Encoder
+    pos_encoder_type: Literal["sinusoidal", "learned"]
+    """The type of position encoder."""
+
+    frontend_layernorm: bool
+    """Whether to add the layernorm in the encoder, decoder frontend."""
+
+    dropout_p: float
+    """The dropout probability in Transformer layers."""
+
+    norm_order: TransformerNormOrder
+    """The Layer Normalization order."""
+
+    def update_vocabulary(self, info: VocabularyInfo) -> None:
+        """Update vocabulary configuration from ``info``."""
+        self.vocabulary_size, self.pad_idx = info.size, info.pad_idx
+
+
+mbart_archs = ArchitectureRegistry[mBartConfig]("mbart")
+
+
+mbart_arch = mbart_archs.marker
+
+
+@mbart_arch("base")
+def _base() -> mBartConfig:
+    return mBartConfig(
+        model_dim=1024,
+        max_seq_len=1026,
+        vocabulary_size=65539,
+        pad_idx=0,
+        num_encoder_layers=12,
+        num_decoder_layers=12,
+        num_encoder_attn_heads=16,
+        num_decoder_attn_heads=16,
+        ffn_inner_dim=4096,
+        pos_encoder_type="learned",
+        frontend_layernorm=True,
+        dropout_p=0.1,
+        norm_order=TransformerNormOrder.POST,
+    )
+
+
+class mBartBuilder:
+    """Builds modules of an mBart model as described in
+    :cite:t:`https://arxiv.org/abs/2001.08210`.
+
+    To tweak the architecture, you can derive from this class and override the
+    corresponding methods.
+    """
+
+    config: mBartConfig
+    device: Optional[Device]
+    dtype: Optional[DataType]
+
+    def __init__(
+        self,
+        config: mBartConfig,
+        device: Optional[Device] = None,
+        dtype: Optional[DataType] = None,
+    ) -> None:
+        """
+        :param config:
+            The configuration to use.
+        :param device:
+            The device on which to initialize modules.
+        :param dtype:
+            The data type of module parameters and buffers.
+        """
+        self.config = config
+        self.device = device
+        self.dtype = dtype
+
+    def build_model(self) -> TransformerModel:
+        """Build a model."""
+        embed = self.build_embedding()
+
+        frontend = self.build_frontend(embed)
+
+        encoder = self.build_encoder()
+        decoder = self.build_decoder()
+
+        final_proj = TiedProjection(embed.weight)
+
+        return TransformerModel(
+            frontend, encoder, frontend, decoder, final_proj, self.config.pad_idx
+        )
+
+    def build_embedding(self) -> Embedding:
+        """Build an embedding table."""
+        return Embedding(
+            num_embeddings=self.config.vocabulary_size,
+            embedding_dim=self.config.model_dim,
+            pad_idx=self.config.pad_idx,
+            scaled=True,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_frontend(self, embed: Embedding) -> TransformerFrontend:
+        """Build a Transformer encoder/decoder front-end."""
+        if self.config.pos_encoder_type == "sinusoidal":
+            pos_encoder = SinusoidalPositionEncoder(
+                self.config.model_dim,
+                self.config.max_seq_len,
+                _legacy_pad_idx=self.config.pad_idx,
+                device=self.device,
+                dtype=self.dtype,
+            )
+        else:
+            pos_encoder = LearnedPositionEncoder(
+                self.config.model_dim,
+                self.config.max_seq_len,
+                device=self.device,
+                dtype=self.dtype,
+            )
+
+        return TransformerEmbeddingFrontend(
+            embed,
+            pos_encoder,
+            layer_norm=self.config.frontend_layernorm,
+            dropout_p=self.config.dropout_p,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_encoder(self) -> TransformerEncoder:
+        """Build a Transformer encoder."""
+        num_layers = self.config.num_encoder_layers
+
+        layers = [self.build_encoder_layer() for _ in range(num_layers)]
+
+        return StandardTransformerEncoder(
+            layers,
+            norm_order=self.config.norm_order,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_decoder(self) -> TransformerDecoder:
+        """Build a Transformer decoder."""
+        num_layers = self.config.num_decoder_layers
+
+        layers = [self.build_decoder_layer() for _ in range(num_layers)]
+
+        return StandardTransformerDecoder(
+            layers,
+            norm_order=self.config.norm_order,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_encoder_layer(self) -> TransformerEncoderLayer:
+        """Build a Transformer encoder layer."""
+        self_attn = self.build_attention(self.config.num_encoder_attn_heads)
+
+        ffn = self.build_ffn()
+
+        return StandardTransformerEncoderLayer(
+            self_attn,
+            ffn,
+            dropout_p=self.config.dropout_p,
+            norm_order=self.config.norm_order,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_decoder_layer(self) -> TransformerDecoderLayer:
+        """Build a Transformer decoder layer."""
+        self_attn = self.build_attention(self.config.num_decoder_attn_heads)
+
+        encoder_decoder_attn = self.build_attention(self.config.num_decoder_attn_heads)
+
+        ffn = self.build_ffn()
+
+        return StandardTransformerDecoderLayer(
+            self_attn,
+            encoder_decoder_attn,
+            ffn,
+            dropout_p=self.config.dropout_p,
+            norm_order=self.config.norm_order,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_attention(self, num_heads: int) -> MultiheadAttention:
+        """Build a Transformer multi-head attention layer."""
+        sdpa = create_default_sdpa(attn_dropout_p=self.config.dropout_p)
+
+        return StandardMultiheadAttention(
+            self.config.model_dim,
+            num_heads,
+            sdpa=sdpa,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+    def build_ffn(self) -> FeedForwardNetwork:
+        """Build a Transformer feed-forward network."""
+        return StandardFeedForwardNetwork(
+            self.config.model_dim,
+            self.config.ffn_inner_dim,
+            norm_order=self.config.norm_order,
+            device=self.device,
+            dtype=self.dtype,
+        )
+
+
+def create_mbart_model(
+    config: mBartConfig,
+    device: Optional[Device] = None,
+    dtype: Optional[DataType] = None,
+) -> TransformerModel:
+    """Create an mBart model.
+
+    :param config:
+        The configuration to use.
+    :param device:
+        The device on which to initialize modules.
+    :param dtype:
+        The data type of module parameters and buffers.
+    """
+    return mBartBuilder(config, device, dtype).build_model()