update qwen2_vl nn lyaout

vllm/model_executor/model_loader/utils.py

@@ -79,7 +79,7 @@ def get_model_architecture(
         model_config: ModelConfig) -> Tuple[Type[nn.Module], str]:
     architectures = getattr(model_config.hf_config, "architectures", [])
     visions = getattr(model_config.hf_config, "visual", []) or getattr(model_config.hf_config, "vision_config", [])
-    support_nn_architectures = ['LlamaForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2VLForConditionalGeneration', 'Qwen2MoeForCausalLM', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 
+    support_nn_architectures = ['LlamaForCausalLM', 'QWenLMHeadModel', 'Qwen2ForCausalLM', 'Qwen2VLForConditionalGeneration', 'Qwen2_5_VLForConditionalGeneration', 'Qwen2MoeForCausalLM', 'ChatGLMModel', 'ChatGLMForConditionalGeneration', 
                                 'BaichuanForCausalLM', 'BloomForCausalLM', 'MedusaModel', 'MixtralForCausalLM', 'MLPSpeculatorPreTrainedModel', 'FalconForCausalLM', 'DeepseekV2ForCausalLM', 'DeepseekV3ForCausalLM']  
     if any(arch in architectures for arch in support_nn_architectures): 
         if os.getenv('LLAMA_NN') != '0': 

vllm/model_executor/models/qwen2_5_vl.py

@@ -69,6 +69,11 @@ from .utils import (AutoWeightsLoader, WeightsMapper,
                     merge_multimodal_embeddings)
 from .vision import get_vit_attn_backend
 
+import os
+import re
+from vllm import _custom_ops as ops
+from vllm.model_executor.utils import pad_weight, gemm_bank_conf
+
 logger = init_logger(__name__)
 
 # === Vision Inputs === #
@@ -528,6 +533,16 @@ class Qwen2_5_VisionTransformer(nn.Module):
             quant_config=quant_config,
             prefix=f"{prefix}.merger",
         )
+        
+        self.quant_method = None
+        if quant_config is not None:
+            self.quant_method=quant_config.get_name()
+            self.quant_config=quant_config
+               
+        self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
+        self.use_gemm_pad = os.environ.get('GEMM_PAD') == '1'
+        self.use_fa_pad = os.environ.get('FA_PAD') == '1'
+        self.use_awq_pad = os.environ.get('AWQ_PAD') == '1'
 
     @property
     def dtype(self) -> torch.dtype:
@@ -675,6 +690,49 @@ class Qwen2_5_VisionTransformer(nn.Module):
                                         default_weight_loader)
                 weight_loader(param, loaded_weight)
             loaded_params.add(name)
+            
+        if self.use_llama_nn and self.quant_method is None:
+            lay_key_words = [
+                "attn.qkv.weight",
+                "attn.proj.weight",
+                "mlp.0.weight",
+                "mlp.2.weight",
+                "self_attn.qkv_proj.weight",
+                "self_attn.o_proj.weight",
+                "mlp.gate_up_proj.weight",
+                "mlp.down_proj.weight",
+                "lm_head.weight",
+            ]
+            combined_words = "|".join(lay_key_words)
+            
+            # lay_qkv_words = ["attn.qkv.weight"]   
+            # qkv_words = "|".join(lay_qkv_words)  
+            
+            # lay_qkv_bias_words = ["attn.qkv.bias"]   
+            # qkv_bias_words = "|".join(lay_qkv_bias_words) 
+            
+            for layername in loaded_params:
+                weight = params_dict[layername]
+                # if self.use_fa_pad and (re.findall(qkv_bias_words, layername)):
+                #     weight.data = pad_weight(weight.data, 32)
+                    
+                matches = re.findall(combined_words, layername)
+                if matches:   
+                    # if self.use_gemm_pad and gemm_bank_conf(weight.data.shape[0]):
+                    #     weight.data = pad_weight(weight.data, 32)  
+                    
+                    # if self.use_fa_pad and (re.findall(qkv_words, layername)):
+                    #     if not gemm_bank_conf(weight.data.shape[0]):
+                    #         weight.data = pad_weight(weight.data, 32)
+                        
+                    _weight = torch.zeros_like(weight.data)
+                    ori_shape =_weight.shape
+                    
+                    ops.trans_w16_gemm(_weight, weight.data, _weight.shape[0], _weight.shape[1])
+                    weight.data.copy_(_weight)
+                    
+                    weight.data=weight.data.reshape(ori_shape[1],-1)
+                    
         return loaded_params