Create modeling_csumlm.py

modeling_csumlm.py

@@ -0,0 +1,94 @@
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PreTrainedEncoder, PreTrainedDecoder
+from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+class CSUMLMEncoder(PreTrainedEncoder):
+    def __init__(self, config):
+        super().__init__(config)
+        # Define the text encoder, image encoder, and audio encoder architectures
+        # ...
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        # Implement the forward pass for the encoder
+        # ...
+        return encoder_outputs
+
+class CSUMLMDecoder(PreTrainedDecoder):
+    def __init__(self, config):
+        super().__init__(config)
+        # Define the decoder architecture
+        # ...
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        head_mask=None,
+        cross_attn_head_mask=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        # Implement the forward pass for the decoder
+        # ...
+        return decoder_outputs
+
+class CSUMLMModel(PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.encoder = CSUMLMEncoder(config)
+        self.decoder = CSUMLMDecoder(config)
+        self.multimodal_fusion = MultimodalFusion(config)
+        # Initialize other components (e.g., attention mechanism, belief desire intent tree)
+        # ...
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        decoder_input_ids=None,
+        decoder_attention_mask=None,
+        head_mask=None,
+        decoder_head_mask=None,
+        cross_attn_head_mask=None,
+        encoder_outputs=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        decoder_inputs_embeds=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        # Implement the forward pass for the CSUMLM model
+        # ...
+        return output
+
+# Register the custom model with Hugging Face Transformers
+CSUMLMModel.register_for_auto_class()