remove unnecessary ipex kernel usage (#12649)

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

@@ -1984,16 +1984,9 @@ def _optimize_post(model):
         modeling_module_name = model.__class__.__module__
         module = importlib.import_module(modeling_module_name)
         from ipex_llm.transformers.models.yuan import yuan_attention_forward
-        # from ipex_llm.transformers.models.yuan import yuan_mlp_forward
+        convert_forward(model, module.YuanAttention, yuan_attention_forward)
-        convert_forward(model,
+        # from ipex_llm.transformers.models.common import mlp_silu_forward
-                        module.YuanAttention,
+        # convert_forward(model, module.YuanMLP, mlp_silu_forward)
-                        yuan_attention_forward
-                        )
-        # disable able mlp_forward for quantize_kv on mtl.
-        # convert_forward(model,
-        #                 module.YuanMLP,
-        #                 yuan_mlp_forward
-        #                 )
     elif model.config.model_type == 'bert' and (
         not model.config.is_decoder and
         model.config.position_embedding_type == "absolute"

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

@@ -20,17 +20,15 @@
 # https://huggingface.co/IEITYuan/Yuan2-2B-hf/blob/7ab7b3c18eb8e5232ce2a3f720d4e6f4b53a2806/README.md#%E5%A3%B0%E6%98%8E%E4%B8%8E%E5%8D%8F%E8%AE%AEterms-and-conditions
 #
 
-import math
 from typing import Optional, Tuple
 
 import torch
 
 from ipex_llm.utils.common import invalidInputError
 from ipex_llm.transformers.models.common import scaled_dot_product_attention
-from ipex_llm.transformers.models.utils import apply_rotary_pos_emb, \
+from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
-    mlp_fusion_check, fp16_fusion_check
 from ipex_llm.transformers.models.utils import use_quantize_kv_cache
-from ipex_llm.transformers.models.utils import SILU, update_past_key_value
+from ipex_llm.transformers.models.utils import update_past_key_value
 from ipex_llm.transformers.models.utils import should_use_fuse_rope
 
 
@@ -98,52 +96,6 @@ def yuan_localized_filtering_forward(
     return lf_output
 
 
-def yuan_mlp_forward(
-    self,
-    x: torch.Tensor,
-    residual=None
-) -> torch.Tensor:
-    x_2d = x.view(-1, x.shape[-1])
-    bsz, hidden_size = x_2d.shape
-    qtype = getattr(self.up_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()
-        out = self.down_proj(xe_linear.mlp_forward_xpu(
-            x_2d, self.up_proj.weight.data, self.gate_proj.weight.data,
-            x_2d.shape[0], x_2d.shape[1], self.up_proj.out_len,
-            SILU, qtype
-        ))
-        if residual is not None:
-            return out + residual
-        else:
-            return out
-    elif fp16_fusion_check(self.up_proj, x, self.training) and \
-            hidden_size == 4096 and bsz == 1:
-        hidden_states1 = torch.ops.torch_ipex.mm_silu(x, self.up_proj.weight)
-        hidden_states = torch.ops.torch_ipex.mm_resmul(
-            x, self.gate_proj.weight, hidden_states1
-        )
-        if residual is None:
-            hidden_states = torch.matmul(hidden_states, self.down_proj.weight)
-        else:
-            attn_output = torch.addmm(
-                residual.flatten(0, -2),
-                hidden_states.flatten(0, -2),
-                self.down_proj.weight,
-                beta=1,
-            )
-            hidden_states = attn_output.view(x.shape)
-        return hidden_states
-    else:
-        out = self.down_proj(self.act_fn(self.up_proj(x)) * self.gate_proj(x))
-        if residual is not None:
-            return out + residual
-        else:
-            return out
-
-
 def yuan_attention_forward(
     self,
     hidden_states: torch.Tensor,