Add OPT architecture

src/fairseq2/composition/models.py

@@ -67,6 +67,13 @@
     create_nllb_model,
     register_nllb_configs,
 )
+from fairseq2.models.opt import (
+    OPT_FAMILY,
+    OPTConfig,
+    convert_opt_state_dict,
+    create_opt_model,
+    register_opt_configs,
+)
 from fairseq2.models.qwen import (
     QWEN_FAMILY,
     QwenConfig,
@@ -296,6 +303,21 @@ def _register_model_families(container: DependencyContainer) -> None:
 
     register_nllb_configs(container)
 
+    # OPT
+    register_model_family(
+        container,
+        OPT_FAMILY,
+        kls=TransformerLM,
+        config_kls=OPTConfig,
+        factory=create_opt_model,
+        state_dict_converter=convert_opt_state_dict,
+        compiler=compile_transformer_lm,
+        fsdp_applier=apply_fsdp_to_transformer_lm,
+        layerwise_ac_applier=apply_ac_to_transformer_lm,
+    )
+
+    register_opt_configs(container)
+
     # Qwen
     register_model_family(
         container,

src/fairseq2/models/opt/__init__.py

@@ -0,0 +1,15 @@
+# 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.opt.config import OPT_FAMILY as OPT_FAMILY
+from fairseq2.models.opt.config import OPTConfig as OPTConfig
+from fairseq2.models.opt.config import register_opt_configs as register_opt_configs
+from fairseq2.models.opt.factory import OPTFactory as OPTFactory
+from fairseq2.models.opt.factory import create_opt_model as create_opt_model
+from fairseq2.models.opt.hub import get_opt_model_hub as get_opt_model_hub
+from fairseq2.models.opt.interop import convert_opt_state_dict as convert_opt_state_dict

src/fairseq2/models/opt/config.py

@@ -0,0 +1,62 @@
+# 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 dataclasses import dataclass
+from typing import Final
+
+from fairseq2.runtime.config_registry import ConfigRegistrar
+from fairseq2.runtime.dependency import DependencyContainer
+
+OPT_FAMILY: Final = "opt"
+
+
+@dataclass(kw_only=True)
+class OPTConfig:
+    """Holds the configuration of a OPT model.
+
+    The default values correspond to the base architecture as described in
+    :cite:t:`https://arxiv.org/abs/2205.01068`.
+    """
+
+    model_dim: int = 768
+    """The dimensionality of the model."""
+
+    max_seq_len: int = 2048 + 1
+    """The maximum sequence length."""
+
+    vocab_size: int = 50272
+    """The size of the vocabulary."""
+
+    pad_idx: int | None = 1
+    """The index of the PAD symbol in the vocabulary."""
+
+    attn_window_len: int = 2048
+    """The local attention window length."""
+
+    num_layers: int = 12
+    """The number of decoder layers."""
+
+    num_attn_heads: int = 12
+    """The number of attention heads in decoder layers."""
+
+    num_key_value_heads: int = 12
+    """The number of key/value heads for Grouped Query Attention."""
+
+    ffn_inner_dim: int = 3072
+    """The dimensionality of inner projection layers in feed-forward networks."""
+
+    dropout_p: float = 0.1
+    """The dropout probability on outputs of Transformer layers."""
+
+
+def register_opt_configs(container: DependencyContainer) -> None:
+    arch = ConfigRegistrar(container, OPTConfig)
+
+    @arch("125m")
+    def _125m() -> OPTConfig:
+        return OPTConfig()

src/fairseq2/models/opt/factory.py

@@ -0,0 +1,175 @@
+# 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
+
+import torch.nn as nn
+
+from fairseq2.models.opt.config import OPTConfig
+from fairseq2.models.transformer import (
+    CausalAttentionBias,
+    FeedForwardNetwork,
+    LocalAttentionStateFactory,
+    MultiheadAttention,
+    StandardFeedForwardNetwork,
+    StandardMultiheadAttention,
+    TransformerEmbeddingFrontend,
+    TransformerFrontend,
+    TransformerNormOrder,
+    create_default_sdpa,
+)
+from fairseq2.models.transformer_lm import (
+    StandardTransformerLMDecoder,
+    StandardTransformerLMDecoderLayer,
+    TransformerLM,
+    TransformerLMDecoder,
+    TransformerLMDecoderLayer,
+)
+from fairseq2.nn import (
+    Embedding,
+    LayerNorm,
+    LearnedPositionEncoder,
+    Linear,
+    PositionEncoder,
+    Projection,
+    StandardEmbedding,
+    StandardLayerNorm,
+)
+
+
+def create_opt_model(config: OPTConfig) -> TransformerLM:
+    return OPTFactory(config).create_model()
+
+
+class OPTFactory:
+    def __init__(self, config: OPTConfig) -> None:
+        self._config = config
+
+    def create_model(self) -> TransformerLM:
+        config = self._config
+
+        decoder_frontend = self.create_decoder_frontend()
+
+        decoder = self.create_decoder()
+
+        final_proj = self.create_final_projection()
+
+        return TransformerLM(
+            config.model_dim,
+            decoder_frontend,
+            decoder,
+            final_proj,
+            config.pad_idx,
+            config.max_seq_len,
+        )
+
+    def create_decoder_frontend(self) -> TransformerFrontend:
+        config = self._config
+
+        embed = self.create_embedding()
+
+        pos_encoder = self.create_position_encoder()
+
+        return TransformerEmbeddingFrontend(
+            config.model_dim,
+            embed,
+            pos_encoder=pos_encoder,
+            no_scale=True,
+            # dropout_p=config.dropout_p,  # TODO: check if there is dropout here
+        )
+
+    def create_embedding(self) -> Embedding:
+        config = self._config
+
+        return StandardEmbedding(config.vocab_size, config.model_dim, config.pad_idx)
+
+    def create_decoder(self) -> TransformerLMDecoder:
+        config = self._config
+
+        layers = []
+
+        for _ in range(config.num_layers):
+            layer = self.create_decoder_layer()
+
+            layers.append(layer)
+
+        layer_norm = self.create_layer_norm()
+
+        return StandardTransformerLMDecoder(layers, layer_norm)
+
+    def create_position_encoder(self) -> PositionEncoder:
+        config = self._config
+
+        return LearnedPositionEncoder(
+            config.model_dim, config.max_seq_len, _legacy_pad_idx=1
+        )
+
+    def create_decoder_layer(self) -> TransformerLMDecoderLayer:
+        config = self._config
+
+        self_attn = self.create_self_attention()
+
+        self_attn_layer_norm = self.create_layer_norm()
+
+        ffn = self.create_ffn()
+
+        ffn_layer_norm = self.create_layer_norm()
+
+        return StandardTransformerLMDecoderLayer(
+            self_attn,
+            self_attn_layer_norm,
+            ffn,
+            ffn_layer_norm,
+            norm_order=TransformerNormOrder.PRE,
+            dropout_p=config.dropout_p,
+        )
+
+    def create_self_attention(self) -> MultiheadAttention:
+        config = self._config
+
+        attn_bias = CausalAttentionBias(attn_window_len=config.attn_window_len)
+
+        sdpa = create_default_sdpa(attn_bias)
+
+        state_factory = LocalAttentionStateFactory(config.attn_window_len)
+
+        return StandardMultiheadAttention(
+            config.model_dim,
+            config.num_attn_heads,
+            sdpa,
+            num_key_value_heads=config.num_key_value_heads,
+            bias=True,
+            state_factory=state_factory,
+        )
+
+    def create_ffn(self) -> FeedForwardNetwork:
+        config = self._config
+
+        return StandardFeedForwardNetwork(
+            config.model_dim, config.ffn_inner_dim, bias=True
+        )
+
+    def create_layer_norm(self) -> LayerNorm:
+        config = self._config
+
+        return StandardLayerNorm(config.model_dim, bias=True)
+
+    def create_final_projection(self) -> Projection:
+        config = self._config
+
+        return Linear(
+            config.model_dim,
+            config.vocab_size,
+            bias=False,
+            init_fn=_init_final_projection,
+        )
+
+
+def _init_final_projection(proj: Linear) -> None:
+    nn.init.normal_(proj.weight, std=proj.input_dim**-0.5)
+
+    if proj.bias is not None:
+        nn.init.zeros_(proj.bias)

src/fairseq2/models/opt/hub.py

@@ -0,0 +1,15 @@
+# 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 import ModelHubAccessor
+from fairseq2.models.opt.config import OPT_FAMILY, OPTConfig
+from fairseq2.models.transformer_lm import TransformerLM
+
+get_opt_model_hub = ModelHubAccessor(
+    OPT_FAMILY, kls=TransformerLM, config_kls=OPTConfig
+)

src/fairseq2/models/opt/interop.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.opt.config import OPTConfig
+from fairseq2.models.utils.checkpoint import convert_state_dict
+
+_HG_KEY_MAP = {
+    # fmt: off
+    r"^model\.decoder\.embed_tokens\.":                           r"decoder_frontend.embed.",
+    r"^model\.decoder\.embed_positions\.":                        r"decoder_frontend.pos_encoder.",
+    r"^model\.decoder\.layers\.([0-9]+)\.self_attn_layer_norm\.": r"decoder.layers.\1.self_attn_layer_norm.",
+    r"^model\.decoder\.layers\.([0-9]+)\.self_attn\.q_proj\.":    r"decoder.layers.\1.self_attn.q_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.self_attn\.k_proj\.":    r"decoder.layers.\1.self_attn.k_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.self_attn\.v_proj\.":    r"decoder.layers.\1.self_attn.v_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.self_attn\.out_proj\.":  r"decoder.layers.\1.self_attn.output_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.mlp\.gate_proj\.":       r"decoder.layers.\1.ffn.gate_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.fc1\.":                  r"decoder.layers.\1.ffn.inner_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.fc2\.":                  r"decoder.layers.\1.ffn.output_proj.",
+    r"^model\.decoder\.layers\.([0-9]+)\.final_layer_norm\.":     r"decoder.layers.\1.ffn_layer_norm.",
+    r"^model\.decoder\.final_layer_norm\.":                       r"decoder.layer_norm.",
+    r"^lm_head\.":                                                r"final_proj.",
+    # fmt: on
+}
+
+
+def convert_opt_state_dict(
+    state_dict: dict[str, object], config: OPTConfig
+) -> dict[str, object]:
+    if "model.decoder.embed_tokens.weight" in state_dict:  # Hugging Face
+        state_dict = convert_state_dict(state_dict, _HG_KEY_MAP)
+
+    return state_dict

src/fairseq2/nn/position_encoder.py

@@ -225,6 +225,7 @@ def __init__(
         encoding_dim: int,
         max_seq_len: int,
         *,
+        _legacy_pad_idx: int | None = None,
         device: Device | None = None,
         dtype: DataType | None = None,
     ) -> None:
@@ -243,6 +244,10 @@ def __init__(
 
         self.max_seq_len = max_seq_len
 
+        # This is a legacy parameter that should only be set when the encodings
+        # must be compatible with fairseq.
+        self._legacy_pad_idx = 0 if _legacy_pad_idx is None else _legacy_pad_idx
+
         self.reset_parameters()
 
     def reset_parameters(self) -> None:
@@ -271,7 +276,7 @@ def forward(
                 f"The lengths of all sequences in `seqs` must be less than or equal to the maximum sequence length ({self.max_seq_len}), but at least one sequence is of length {max_seq_len} instead."
             )
 
-        indices = seqs_layout.position_indices + 1  # +1 for padding
+        indices = seqs_layout.position_indices + (1 + self._legacy_pad_idx)
 
         if not self.training and state_bag is not None:
             indices = state_bag.step_nr + indices