use mlp silu_mul fusion in qwen2 to optimize memory usage (#11574)

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

@@ -1323,6 +1323,7 @@ def _optimize_post(model, lightweight_bmm=False):
         from ipex_llm.transformers.models.qwen2 import qwen2_model_forward
         from ipex_llm.transformers.models.qwen2 import qwen2_attention_forward
         from ipex_llm.transformers.models.qwen2 import qwen2_causal_lm_forward
+        from ipex_llm.transformers.models.qwen2 import qwen2_mlp_forward
         convert_forward(model,
                         module.Qwen2Model,
                         qwen2_model_forward)
@@ -1334,7 +1335,7 @@ def _optimize_post(model, lightweight_bmm=False):
                         llama_rms_norm_forward)
         convert_forward(model,
                         module.Qwen2MLP,
-                        llama_mlp_forward)
+                        qwen2_mlp_forward)
         convert_forward(model,
                         module.Qwen2Attention,
                         qwen2_attention_forward)

python/llm/src/ipex_llm/transformers/models/qwen2.py

@@ -45,6 +45,7 @@ import torch
 from torch.nn import CrossEntropyLoss
 from torch.nn.functional import scaled_dot_product_attention as sdpa
 
+from ipex_llm.transformers.models.utils import SILU, mlp_fusion_check
 from ipex_llm.transformers.models.utils import should_use_fuse_rope
 from ipex_llm.transformers.models.utils import use_quantize_kv_cache, restore_fp8_kv_cache
 from ipex_llm.transformers.models.utils import use_flash_attention, use_sdp, use_sdp_causal
@@ -491,3 +492,26 @@ def qwen2_attention_forward(
     if not output_attentions:
         attn_weights = None
     return attn_output, attn_weights, past_key_value
+
+
+def qwen2_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) and not self.down_proj.enable_xetla:
+        import xe_linear
+        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
+        ))
+    elif not self.training:
+        import xe_addons
+        gate = self.gate_proj(x)
+        up = self.up_proj(x)
+        xe_addons.mlp_silu_mul_inplaced(gate, up)
+        return self.down_proj(gate)
+    else:
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))