refactor to simplify following upgrade

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

@@ -1325,7 +1325,6 @@ def _optimize_post(model):
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
             from ipex_llm.transformers.models.chatglm2 import chatglm2_attention_forward
-            from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
             from ipex_llm.transformers.models.chatglm2 import chatglm2_encoder_forward
             from ipex_llm.transformers.models.chatglm2 import chatglm2_model_forward
             from ipex_llm.transformers.models.chatglm2 import mlp_forward
@@ -1338,9 +1337,7 @@ def _optimize_post(model):
             convert_forward(model,
                             module.ChatGLMModel,
                             chatglm2_model_forward)
-            convert_forward(model,
-                            module.RMSNorm,
-                            chatglm_rms_norm_forward)
+            convert_forward(model, module.RMSNorm, rms_norm_forward)
             convert_forward(model, module.MLP, mlp_forward)
             # for codegeex-nano
             if hasattr(model.config, "rope_ratio"):
@@ -1358,8 +1355,7 @@ def _optimize_post(model):
             # glm4 family
             modeling_module_name = model.__class__.__module__
             module = importlib.import_module(modeling_module_name)
-            from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
-            convert_forward(model, module.RMSNorm, chatglm_rms_norm_forward)
+            convert_forward(model, module.RMSNorm, rms_norm_forward)
 
             if hasattr(model.transformer, "vision"):
                 # glm4 vision family
@@ -1448,8 +1444,8 @@ def _optimize_post(model):
     elif model.config.model_type == "baichuan":
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
-        from ipex_llm.transformers.models.baichuan import baichuan_mlp_forward
-        convert_forward(model, module.MLP, baichuan_mlp_forward)
+        convert_forward(model, module.RMSNorm, rms_norm_forward)
+        convert_forward(model, module.MLP, mlp_silu_forward)
 
         if model.config.hidden_size in [4096, 2048]:
             # baichuan-7B and baichuan2-7B
@@ -1458,7 +1454,6 @@ def _optimize_post(model):
             for i in range(len(model.model.layers)):
                 setattr(model.model.layers[i].self_attn, "layer_idx", i)
             convert_forward(model, module.Attention, baichuan_attention_forward_7b)
-            convert_forward(model, module.RMSNorm, rms_norm_forward)
             if model.config.vocab_size == 125696:
                 # baichuan2-7B
                 convert_forward(model, module.BaichuanModel, baichuan_model_7b_forward)
@@ -1468,9 +1463,7 @@ def _optimize_post(model):
         elif model.config.hidden_size == 5120:
             # baichuan-13B and baichuan2-13B
             from ipex_llm.transformers.models.baichuan import baichuan_attention_forward_13b
-            from ipex_llm.transformers.models.baichuan import baichuan_13b_rms_norm_forward
             convert_forward(model, module.BaichuanAttention, baichuan_attention_forward_13b)
-            convert_forward(model, module.RMSNorm, baichuan_13b_rms_norm_forward)
 
             if model.config.vocab_size == 125696:
                 # baichaun2-13B
@@ -1565,7 +1558,6 @@ def _optimize_post(model):
             from ipex_llm.transformers.models.qwen import qwen_attention_forward
             from ipex_llm.transformers.models.qwen import qwen_attention_forward_registered
             from ipex_llm.transformers.models.qwen import qwen_mlp_forward
-            from ipex_llm.transformers.models.chatglm2 import chatglm_rms_norm_forward
             from ipex_llm.transformers.models.qwen import qwen_model_forward
             if model.config.max_position_embeddings == 8192 \
                and model.config.hidden_size == 4096:
@@ -1580,7 +1572,7 @@ def _optimize_post(model):
                                 )
             convert_forward(model,
                             module.RMSNorm,
-                            chatglm_rms_norm_forward)
+                            rms_norm_forward)
             convert_forward(model,
                             module.QWenMLP,
                             qwen_mlp_forward)

python/llm/src/ipex_llm/transformers/models/baichuan.py

@@ -47,38 +47,6 @@ def pre_compute_inv_freq(module: torch.nn.Module):
         module.register_buffer("inv_freq", inv_freq, persistent=False)
 
 
-def baichuan_13b_rms_norm_forward(self, hidden_states):
-    if hidden_states.device.type == "xpu" and not (self.training or hidden_states.requires_grad):
-        import xe_addons
-        x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
-        output = xe_addons.rms_norm(self.weight, x_2d, self.epsilon)
-        return output.reshape(hidden_states.shape)
-
-    input_dtype = hidden_states.dtype
-    hidden_states = hidden_states.to(torch.float32)
-    variance = hidden_states.pow(2).mean(-1, keepdim=True)
-    hidden_states = hidden_states * torch.rsqrt(variance + self.epsilon)
-    return self.weight * hidden_states.to(input_dtype)
-
-
-def baichuan_mlp_forward(
-    self,
-    x: torch.Tensor,
-) -> torch.Tensor:
-    x_2d = x.view(-1, x.shape[-1])
-    qtype = getattr(self.gate_proj, "qtype", None)
-    if mlp_fusion_check(x_2d, qtype, self.training):
-        import xe_linear
-        if not x_2d.is_contiguous():
-            x_2d = x_2d.contiguous()
-        return self.down_proj(xe_linear.mlp_forward_xpu(
-            x_2d, self.gate_proj.weight.data, self.up_proj.weight.data,
-            x_2d.shape[0], x_2d.shape[1], self.gate_proj.out_len,
-            SILU, qtype
-        ))
-    return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
 def baichuan_model_7b_forward(
         self,
         input_ids: torch.LongTensor = None,

python/llm/src/ipex_llm/transformers/models/bert.py

@@ -36,24 +36,13 @@
 import torch
 from typing import Optional, Tuple
 from transformers.models.bert.modeling_bert import BertSelfAttention, BertEncoder
+from ipex_llm.transformers.models.common import merge_linear
 from ipex_llm.utils.common import invalidInputError
 
 
 def merge_qkv(module: torch.nn.Module):
     if isinstance(module, BertSelfAttention):
-        q_w = module.query.weight.data
-        k_w = module.key.weight.data
-        v_w = module.value.weight.data
-        q_b = module.query.bias.data
-        k_b = module.key.bias.data
-        v_b = module.value.bias.data
-        new_w = torch.cat([q_w, k_w, v_w], dim=0)
-        new_b = torch.cat([q_b, k_b, v_b], dim=-1)
-        qkv = torch.nn.Linear(0, 0, bias=True)
-        qkv.weight = torch.nn.Parameter(new_w, requires_grad=False)
-        qkv.bias = torch.nn.Parameter(new_b, requires_grad=False)
-        qkv.in_features = module.query.in_features
-        qkv.out_features = module.query.out_features * 3
+        qkv = merge_linear([module.query, module.key, module.value])
         module.qkv = qkv
         del module.query
         del module.key

python/llm/src/ipex_llm/transformers/models/chatglm2.py

@@ -33,34 +33,6 @@
 KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
 
 
-def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
-    """
-    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states
-    go from (batch, num_key_value_heads, seqlen, head_dim) to
-    (batch, num_attention_heads, seqlen, head_dim)
-    """
-    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
-    if n_rep == 1:
-        return hidden_states
-    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads,
-                                                           n_rep, slen, head_dim)
-    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
-
-
-def chatglm_rms_norm_forward(self, hidden_states):
-    if hidden_states.device.type == "xpu" and not (self.training and hidden_states.requires_grad):
-        import xe_addons
-        x_2d = hidden_states.reshape(-1, hidden_states.size(-1)).contiguous()
-        output = xe_addons.rms_norm(self.weight, x_2d, self.eps)
-        return output.reshape(hidden_states.shape)
-
-    input_dtype = hidden_states.dtype
-    hidden_states = hidden_states.to(torch.float32)
-    variance = hidden_states.pow(2).mean(-1, keepdim=True)
-    hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
-    return self.weight * hidden_states.to(input_dtype)
-
-
 def chatglm2_model_forward(
     self,
     input_ids,

python/llm/src/ipex_llm/transformers/models/common.py

@@ -157,8 +157,10 @@ def rms_norm_forward(self, hidden_states: torch.Tensor):
     weight = self.weight
     if hasattr(self, "variance_epsilon"):
         eps = self.variance_epsilon
-    else:
+    elif hasattr(self, "epsilon"):
         eps = self.epsilon
+    else:
+        eps = self.eps
 
     if hidden_states.device.type == 'xpu' and hidden_states.dtype in [torch.float, torch.half]:
         import xe_addons