add mlp for gemma2 (#11678)

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

@@ -1513,11 +1513,13 @@ def _optimize_post(model, lightweight_bmm=False):
         from ipex_llm.transformers.models.gemma import gemma_rms_norm_forward
         from ipex_llm.transformers.models.gemma2 import gemma2_attention_forward
         from ipex_llm.transformers.models.gemma2 import gemma2_model_forward
+        from ipex_llm.transformers.models.gemma2 import gemma2_mlp_forward
         from transformers.models.gemma2.modeling_gemma2 import Gemma2RMSNorm, Gemma2Attention
-        from transformers.models.gemma2.modeling_gemma2 import Gemma2Model
+        from transformers.models.gemma2.modeling_gemma2 import Gemma2Model, Gemma2MLP
         convert_forward(model, Gemma2RMSNorm, gemma_rms_norm_forward)
         convert_forward(model, Gemma2Attention, gemma2_attention_forward)
         convert_forward(model, Gemma2Model, gemma2_model_forward)
+        convert_forward(model, Gemma2MLP, gemma2_mlp_forward)
     elif model.config.model_type == "Yi":
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)

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

@@ -41,3 +41,21 @@ def merge_qkv_base(module: torch.nn.Module, attention_class):
         ])
         module.qkv_proj = qkv_proj
         del module.q_proj, module.k_proj, module.v_proj
+
+
+def fuse_mlp_base(module: torch.nn.Module, act: int, x: torch.Tensor):
+    from ipex_llm.transformers.models.utils import mlp_fusion_check
+    x_2d = x.view(-1, x.size(-1))
+    qtype = getattr(module.gate_proj, "qtype", None)
+    if mlp_fusion_check(x_2d, qtype, module.training):
+        import xe_linear
+        x_2d = x_2d.contiguous()
+        return module.down_proj(
+            xe_linear.mlp_forward_xpu(
+                x_2d, module.gate_proj.weight.data, module.up_proj.weight.data,
+                x_2d.size(0), x_2d.size(1), module.gate_proj.out_len,
+                act, qtype
+            )
+        )
+    else:
+        return module.down_proj(module.act_fn(module.gate_proj(x)) * module.up_proj(x))

python/llm/src/ipex_llm/transformers/models/gemma2.py

@@ -34,7 +34,8 @@
 import torch
 
 from typing import Optional, Tuple
-from ipex_llm.transformers.models.common import merge_qkv_base
+from ipex_llm.transformers.models.common import merge_qkv_base, fuse_mlp_base
+from ipex_llm.transformers.models.utils import GELU
 from ipex_llm.transformers.models.utils import should_use_fuse_rope, use_sdp, use_sdp_causal
 from transformers.cache_utils import Cache
 from transformers.models.gemma2.modeling_gemma2 import Gemma2Model, Gemma2Attention
@@ -177,3 +178,7 @@ def gemma2_attention_forward(
         attn_weights = None
 
     return attn_output, attn_weights, past_key_value
+
+
+def gemma2_mlp_forward(self, x: torch.Tensor):
+    return fuse_mlp_base(self, GELU, x)