refactor llama convert to fix minicpm-v 2.5 optimization (#11783)

python/llm/src/ipex_llm/transformers/convert.py

@@ -754,6 +754,10 @@ def _optimize_pre(model, qtype=None):
             model.llm.config.model_type = "qwen2"
             _optimize_pre(model.llm, qtype=qtype)
             model.llm.config.model_type = "minicpmv"
+        elif model.config.hidden_size == 4096 and model.config.vocab_size == 128256:
+            model.llm.config.model_type = "llama"
+            _optimize_pre(model.llm, qtype=qtype)
+            model.llm.config.model_type = "minicpmv"
 
     return model
 
@@ -933,16 +937,6 @@ def _optimize_ipex(model, qtype=ggml_tensor_qtype["bf16"]):
 
 
 def _optimize_post(model, lightweight_bmm=False):
-    from packaging import version
-    from ipex_llm.transformers.models.llama import llama_attention_forward_4_31
-    from ipex_llm.transformers.models.llama import llama_attention_selective_batching_forward_4_31
-    from ipex_llm.transformers.models.llama import llama_model_selective_batching_forward_4_31
-    from ipex_llm.transformers.models.llama import llama_rms_norm_forward
-    from ipex_llm.transformers.models.llama import llama_mlp_forward
-    from ipex_llm.transformers.models.llama import llama_decoder_forward
-    from ipex_llm.transformers.models.llama import llama_model_forward
-    from transformers.modeling_utils import PreTrainedModel
-
     try:
         from sentence_transformers.SentenceTransformer import SentenceTransformer
         if isinstance(model, SentenceTransformer):
@@ -961,110 +955,80 @@ def _optimize_post(model, lightweight_bmm=False):
     except ModuleNotFoundError:
         pass
 
+    from transformers.modeling_utils import PreTrainedModel
     # All huggingface format models are inherited from `PreTrainedModel`
     if not isinstance(model, PreTrainedModel):
         logger.info("Only HuggingFace Transformers models are currently "
                     "supported for further optimizations")
         return model
 
-    vllm_selective_batching = os.getenv("VLLM_ENABLE_SELECTIVE_BATCHING")
+    from packaging import version
-    enable_vllm_se_batching = vllm_selective_batching is not None
-    enable_vllm_se_batching = enable_vllm_se_batching and vllm_selective_batching.lower() == "true"
-
     trans_version = transformers.__version__
-    if version.parse(trans_version) >= version.parse("4.31.0"):
-        convert_forward(
-            model,
-            transformers.models.llama.modeling_llama.LlamaRMSNorm,
-            llama_rms_norm_forward,)
-        convert_forward(model,
-                        transformers.models.llama.modeling_llama.LlamaMLP,
-                        llama_mlp_forward)
-        convert_forward(model,
-                        transformers.models.llama.modeling_llama.LlamaDecoderLayer,
-                        llama_decoder_forward)
-
-        if version.parse(trans_version) >= version.parse("4.36.0"):
-            # transformers version >= 4.36.0
-            from ipex_llm.transformers.models.llama import llama_attention_forward_4_38
-            if version.parse(trans_version) >= version.parse("4.38.0"):
-                if version.parse(trans_version) >= version.parse("4.41.0"):
-                    from ipex_llm.transformers.models.llama import llama_model_forward_4_41
-                    from ipex_llm.transformers.models.llama import llama_attention_forward_4_41
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaModel,
-                        llama_model_forward_4_41)
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaAttention,
-                        llama_attention_forward_4_41)
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaSdpaAttention,
-                        llama_attention_forward_4_41)
-                else:
-                    from ipex_llm.transformers.models.llama import llama_model_forward_4_38
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaModel,
-                        llama_model_forward_4_38)
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaAttention,
-                        llama_attention_forward_4_38)
-                    convert_forward(
-                        model,
-                        transformers.models.llama.modeling_llama.LlamaSdpaAttention,
-                        llama_attention_forward_4_38)
-            else:
-                from ipex_llm.transformers.models.llama import llama_model_forward_4_36
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaModel,
-                    llama_model_forward_4_36)
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaAttention,
-                    llama_attention_forward_4_38)
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaSdpaAttention,
-                    llama_attention_forward_4_38)
-        else:
-            # transformers version between 4.31.0 - 4.35.2
-            convert_forward(
-                model,
-                transformers.models.llama.modeling_llama.LlamaAttention,
-                llama_attention_forward_4_31, )
-            if enable_vllm_se_batching:
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaModel,
-                    llama_model_selective_batching_forward_4_31,
-                )
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaAttention,
-                    llama_attention_selective_batching_forward_4_31,
-                )
-            else:
-                convert_forward(
-                    model,
-                    transformers.models.llama.modeling_llama.LlamaModel,
-                    llama_model_forward)
-    else:
-        # todo implement 4.28.0 ~ 4.30.2
-        pass
 
     # convert all nn.LayerNorm
     from ipex_llm.transformers.models.bloom import bloom_layer_norm_forward
     convert_forward(model,
                     nn.LayerNorm,
                     bloom_layer_norm_forward)
+    from ipex_llm.transformers.models.llama import llama_rms_norm_forward
+    from ipex_llm.transformers.models.llama import llama_mlp_forward
 
-    if model.config.architectures is not None \
+    if model.config.model_type == "llama":
-       and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]:
+        from transformers.models.llama.modeling_llama import LlamaRMSNorm
+        from transformers.models.llama.modeling_llama import LlamaMLP
+        from transformers.models.llama.modeling_llama import LlamaAttention
+        from transformers.models.llama.modeling_llama import LlamaDecoderLayer
+        from transformers.models.llama.modeling_llama import LlamaModel
+        if version.parse(trans_version) >= version.parse("4.36.0"):
+            from transformers.models.llama.modeling_llama import LlamaSdpaAttention
+
+        from ipex_llm.transformers.models.llama import llama_rms_norm_forward
+        from ipex_llm.transformers.models.llama import llama_mlp_forward
+        from ipex_llm.transformers.models.llama import llama_decoder_forward
+
+        convert_forward(model, LlamaRMSNorm, llama_rms_norm_forward)
+        convert_forward(model, LlamaMLP, llama_mlp_forward)
+        convert_forward(model, LlamaDecoderLayer, llama_decoder_forward)
+
+        if version.parse(trans_version) >= version.parse("4.41.0"):
+            from ipex_llm.transformers.models.llama import llama_model_forward_4_41
+            from ipex_llm.transformers.models.llama import llama_attention_forward_4_41
+            convert_forward(model, LlamaModel, llama_model_forward_4_41)
+            convert_forward(model, LlamaAttention, llama_attention_forward_4_41)
+            convert_forward(model, LlamaSdpaAttention, llama_attention_forward_4_41)
+        elif version.parse(trans_version) >= version.parse("4.38.0"):
+            from ipex_llm.transformers.models.llama import llama_model_forward_4_38
+            from ipex_llm.transformers.models.llama import llama_attention_forward_4_38
+            convert_forward(model, LlamaModel, llama_model_forward_4_38)
+            convert_forward(model, LlamaAttention, llama_attention_forward_4_38)
+            convert_forward(model, LlamaSdpaAttention, llama_attention_forward_4_38)
+        elif version.parse(trans_version) >= version.parse("4.36.0"):
+            from ipex_llm.transformers.models.llama import llama_model_forward_4_36
+            from ipex_llm.transformers.models.llama import llama_attention_forward_4_38
+            convert_forward(model, LlamaModel, llama_model_forward_4_36)
+            convert_forward(model, LlamaAttention, llama_attention_forward_4_38)
+            convert_forward(model, LlamaSdpaAttention, llama_attention_forward_4_38)
+        else:
+            vllm_se_batching = os.getenv("VLLM_ENABLE_SELECTIVE_BATCHING", "").lower() == "true"
+            if vllm_se_batching:
+                from ipex_llm.transformers.models.llama import (
+                    llama_model_selective_batching_forward_4_31,
+                    llama_attention_selective_batching_forward_4_31,
+                )
+                convert_forward(model, LlamaModel,
+                                llama_model_selective_batching_forward_4_31)
+                convert_forward(model, LlamaAttention,
+                                llama_attention_selective_batching_forward_4_31)
+            else:
+                from ipex_llm.transformers.models.llama import llama_model_forward
+                from ipex_llm.transformers.models.llama import llama_attention_forward_4_31
+                convert_forward(model, LlamaModel, llama_model_forward)
+                convert_forward(model, LlamaAttention, llama_attention_forward_4_31)
+
+    elif (
+        model.config.architectures is not None
+        and model.config.architectures[0] in ["ChatGLMModel", "ChatGLMForConditionalGeneration"]
+    ):
         if hasattr(model.config, 'padded_vocab_size') and \
                 model.config.padded_vocab_size in [65024, 64896]:
             # chatglm2-6b, chatglm2-6b-32k, chatglm3-6b, chatglm3-6b-32k, chatglm3-6b-128k
@@ -1370,6 +1334,7 @@ def _optimize_post(model, lightweight_bmm=False):
         from ipex_llm.transformers.models.qwen2_moe import qwen2moe_model_forward
         from ipex_llm.transformers.models.qwen2_moe import qwen2_moe_causal_lm_forward
         from ipex_llm.transformers.models.qwen2 import qwen2_attention_forward
+        from ipex_llm.transformers.models.qwen2 import qwen2_mlp_forward
         convert_forward(model,
                         module.Qwen2MoeModel,
                         qwen2moe_model_forward)
@@ -1384,7 +1349,7 @@ def _optimize_post(model, lightweight_bmm=False):
                         qwen2moe_moeblock_forward)
         convert_forward(model,
                         module.Qwen2MoeMLP,
-                        llama_mlp_forward)
+                        qwen2_mlp_forward)
         convert_forward(model,
                         module.Qwen2MoeAttention,
                         qwen2_attention_forward)
@@ -1768,7 +1733,9 @@ def _optimize_post(model, lightweight_bmm=False):
             model.llm.config.model_type = "minicpmv"
         elif model.config.hidden_size == 4096 and model.config.vocab_size == 128256:
             # MiniCPM-V 2.5
-            pass
+            model.llm.config.model_type = "llama"
+            _optimize_post(model.llm, lightweight_bmm=lightweight_bmm)
+            model.llm.config.model_type = "minicpmv"
 
         vpm_modeling_module_name = model.vpm.__class__.__module__
         vpm_module = importlib.import_module(vpm_modeling_module_name)