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config.json

@@ -2,9 +2,14 @@
   "activation_dropout": 0.1,
   "activation_function": "relu",
   "architectures": [
-    "M2M100DecoderModel"
+    "SonarDecoderModel"
   ],
   "attention_dropout": 0.1,
+  "auto_map": {
+    "AutoConfig": "sonar_decoder.SonarDecoderConfig",
+    "AutoModel": "sonar_decoder.SonarDecoderModel",
+    "AutoModelForSeq2SeqLM": "sonar_decoder.SonarDecoderModel"
+  },
   "bos_token_id": 0,
   "d_model": 1024,
   "decoder_attention_heads": 16,
@@ -22,7 +27,7 @@
   "init_std": 0.02,
   "is_encoder_decoder": true,
   "max_position_embeddings": 1024,
-  "model_type": "m2m_100",
+  "model_type": "SonarDecoderModel",
   "num_hidden_layers": 24,
   "pad_token_id": 1,
   "scale_embedding": true,

sonar_decoder.py

@@ -0,0 +1,154 @@
+import copy
+
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from transformers.models.m2m_100.modeling_m2m_100 import (
+    M2M100Config,
+    
+    M2M100ScaledWordEmbedding,
+    M2M100Decoder,
+    M2M100PreTrainedModel,
+    GenerationMixin,
+
+    Seq2SeqLMOutput,
+    BaseModelOutput,
+
+    shift_tokens_right,
+    Cache,
+    CrossEntropyLoss,
+)
+
+# override model type to register AutoModels
+class SonarDecoderConfig(M2M100Config):
+    model_type = "SonarDecoderModel"
+
+
+class SonarDecoderModel(M2M100PreTrainedModel, GenerationMixin):
+    # override confing class to register AutoModels
+    config_class = SonarDecoderConfig
+    _tied_weights_keys = {
+        "decoder.embed_tokens.weight": "shared.weight",
+        "lm_head.weight": "shared.weight",
+    }
+    _keys_to_ignore_on_load_unexpected = [r"encoder"]
+
+    def __init__(self, config: M2M100Config):
+        super().__init__(config)
+        self.shared = nn.Embedding(config.vocab_size, config.d_model)
+
+        decoder_config = copy.deepcopy(config)
+        decoder_config.use_cache = False
+        decoder_config.is_encoder_decoder = False
+        self.decoder = M2M100Decoder(decoder_config)
+        self.lm_head = nn.Linear(config.d_model, self.shared.num_embeddings, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, value):
+        self.shared = value
+        self.decoder.embed_tokens = self.shared
+
+    def _tie_weights(self):
+        if self.config.tie_word_embeddings:
+            self._tie_or_clone_weights(self.decoder.embed_tokens, self.shared)
+            # in SONAR models, input and output projections are tied (ideally, this should be configurable)
+            self._tie_or_clone_weights(self.lm_head, self.shared)
+
+    def get_decoder(self):
+        return self.decoder
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        encoder_outputs: tuple[tuple[torch.FloatTensor]] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor] | Seq2SeqLMOutput:
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if labels is not None:
+            if decoder_input_ids is None:
+                decoder_input_ids = shift_tokens_right(
+                    labels, self.config.pad_token_id, self.config.decoder_start_token_id
+                )
+
+        if encoder_outputs is None:
+            raise ValueError("M2M100DecoderModel expects the `encoder_outputs` to be always present.")
+
+        if return_dict and not isinstance(encoder_outputs, BaseModelOutput):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
+
+        # decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=encoder_outputs[0],
+            encoder_attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        lm_logits = self.lm_head(decoder_outputs[0])
+
+        masked_lm_loss = None
+        if labels is not None:
+            # move labels to the correct device to enable PP
+            labels = labels.to(lm_logits.device)
+            loss_fct = CrossEntropyLoss()
+            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + decoder_outputs[1:]
+            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+
+        return Seq2SeqLMOutput(
+            loss=masked_lm_loss,
+            logits=lm_logits,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    @classmethod
+    def _can_set_experts_implementation(cls) -> bool:
+        return False