Create modeling_DiffusionLLM.py

modeling_DiffusionLLM.py

@@ -0,0 +1,126 @@
+import torch
+import torch.nn as nn
+from transformers import PretrainedConfig, PreTrainedModel
+
+class DiffusionConfig(PretrainedConfig):
+    """Configuration class for Diffusion-LLM model."""
+    model_type = "diffusionLM"
+    
+    def __init__(
+        self,
+        vocab_size: int = 50257,
+        hidden_size: int = 768,
+        num_hidden_layers: int = 12,
+        num_attention_heads: int = 12,
+        intermediate_size: int = 3072,
+        hidden_dropout_prob: float = 0.1,
+        attention_probs_dropout_prob: float = 0.1,
+        max_position_embeddings: int = 1024,
+        initializer_range: float = 0.02,
+        layer_norm_eps: float = 1e-12,
+        pad_token_id: int = 0,
+        mask_token_id: int = 50256,
+        eos_token_id: int = 50256,
+        num_timesteps: int = 100,
+        time_embed_dim: int = 128,
+        **kwargs
+    ):
+        super().__init__(pad_token_id=pad_token_id, **kwargs)
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads 
+        self.intermediate_size = intermediate_size
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.mask_token_id = mask_token_id
+        self.eos_token_id = eos_token_id
+        self.num_timesteps = num_timesteps
+        self.time_embed_dim = time_embed_dim
+
+class DiffusionLLM(PreTrainedModel):
+    """Main Diffusion-LLM model class"""
+    config_class = DiffusionConfig
+    base_model_prefix = "diffusionLM"
+
+    def __init__(self, config: DiffusionConfig):
+        super().__init__(config)
+        self.model = LLaDAModel(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        timesteps=None,
+        labels=None,
+        return_dict=True,
+    ):
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            timesteps=timesteps,
+            labels=labels,
+        )
+        
+        return outputs
+
+    def generate(
+        self,
+        prompt=None,
+        max_length=100,
+        num_inference_steps=50,
+        temperature=1.0,
+        strategy='random',
+        top_p=0.9,
+        top_k=50,
+        num_beams=5,
+        return_scores=False,
+        use_streaming=False,
+        callback_fn=None
+    ):
+        """Unified generation interface"""
+        if use_streaming:
+            return self.generate_stream(
+                prompt=prompt,
+                max_length=max_length,
+                num_inference_steps=num_inference_steps,
+                temperature=temperature,
+                strategy=strategy,
+                top_p=top_p,
+                top_k=top_k,
+                num_beams=num_beams,
+                callback_fn=callback_fn
+            )
+        else:
+            return self.model.generate(
+                prompt=prompt,
+                max_length=max_length,
+                num_inference_steps=num_inference_steps,
+                temperature=temperature,
+                strategy=strategy,
+                top_p=top_p,
+                top_k=top_k,
+                num_beams=num_beams,
+                return_scores=return_scores
+            )
+
+    def generate_stream(self, **kwargs):
+        """Streaming generation wrapper"""
+        return self.model.generate_stream(**kwargs)
+
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        """Prepare inputs for generation compatibility"""
+        return {
+            "input_ids": input_ids,
+            "attention_mask": kwargs.get("attention_mask", None),
+            "timesteps": kwargs.get("timesteps", None),
+        }
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        """Reorder cache for beam search compatibility"""
+        return past