Bug fixes and improvements to llmadapter

toolkit/config_modules.py

@@ -219,6 +219,7 @@ class AdapterConfig:
         
         # for llm adapter
         self.num_cloned_blocks: int = kwargs.get('num_cloned_blocks', 0)
+        self.quantize_llm: bool = kwargs.get('quantize_llm', False)
 
 
 class EmbeddingConfig:

toolkit/custom_adapter.py

@@ -44,7 +44,7 @@ from transformers import (
     ConvNextModel,
     ConvNextForImageClassification,
     ConvNextImageProcessor,
-    UMT5EncoderModel, LlamaTokenizerFast, AutoModel, AutoTokenizer
+    UMT5EncoderModel, LlamaTokenizerFast, AutoModel, AutoTokenizer, BitsAndBytesConfig
 )
 from toolkit.models.size_agnostic_feature_encoder import SAFEImageProcessor, SAFEVisionModel
 
@@ -201,10 +201,26 @@ class CustomAdapter(torch.nn.Module):
 
             self.te_adapter = TEAdapter(self, self.sd_ref(), self.te, self.tokenizer)
         elif self.adapter_type == 'llm_adapter':
-            self.te = AutoModel.from_pretrained(self.config.text_encoder_path).to(
-                self.sd_ref().unet.device, 
-                dtype=torch_dtype
-            )
+            kwargs = {}
+            if self.config.quantize_llm:
+                bnb_kwargs = {
+                    'load_in_4bit': True,
+                    'bnb_4bit_quant_type': "nf4",
+                    'bnb_4bit_compute_dtype': torch.bfloat16
+                }
+                quantization_config = BitsAndBytesConfig(**bnb_kwargs)
+                kwargs['quantization_config'] = quantization_config
+                kwargs['torch_dtype'] = torch_dtype
+                self.te = AutoModel.from_pretrained(
+                    self.config.text_encoder_path,
+                    **kwargs
+                )
+            else:
+                self.te = AutoModel.from_pretrained(self.config.text_encoder_path).to(
+                    self.sd_ref().unet.device, 
+                    dtype=torch_dtype,
+                )
+            self.te.to = lambda *args, **kwargs: None
             self.te.eval()
             self.tokenizer = AutoTokenizer.from_pretrained(self.config.text_encoder_path)
             self.llm_adapter = LLMAdapter(
@@ -423,6 +439,9 @@ class CustomAdapter(torch.nn.Module):
 
         if 'te_adapter' in state_dict:
             self.te_adapter.load_state_dict(state_dict['te_adapter'], strict=strict)
+            
+        if 'llm_adapter' in state_dict:
+            self.llm_adapter.load_state_dict(state_dict['llm_adapter'], strict=strict)
 
         if 'te_augmenter' in state_dict:
             self.te_augmenter.load_state_dict(state_dict['te_augmenter'], strict=strict)

toolkit/models/llm_adapter.py

@@ -147,13 +147,14 @@ class LLMAdapter(torch.nn.Module):
         prompt_attention_mask = text_inputs.attention_mask.to(device)
         
         # remove the system prompt from the input and attention mask
-        text_input_ids = text_input_ids[:, self.system_prompt_length:]
-        prompt_attention_mask = prompt_attention_mask[:, self.system_prompt_length:]
         
         prompt_embeds = text_encoder(
             text_input_ids, attention_mask=prompt_attention_mask, output_hidden_states=True
         )
         prompt_embeds = prompt_embeds.hidden_states[-2]
+        
+        prompt_embeds = prompt_embeds[:, self.system_prompt_length:]
+        prompt_attention_mask = prompt_attention_mask[:, self.system_prompt_length:]
 
         dtype = text_encoder.dtype