add ipex moe implementation to support Mixtral and PhiMoe

Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

Dockerfile_intel

@@ -186,7 +186,7 @@ RUN pip install triton py-libnuma
 
 WORKDIR /usr/src
 
-RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout f86e93e4890dc2c989024d148d415c9aa8a1649f
+RUN git clone https://github.com/intel/intel-extension-for-pytorch && cd intel-extension-for-pytorch && git checkout 2e1c98f74ec1b35ad8dd1ebe7dd4b25470f2fd41
 RUN git clone https://github.com/intel/torch-ccl.git && cd torch-ccl && git checkout v2.4.0+cpu+rc0
 
 RUN cd intel-extension-for-pytorch && git submodule sync && git submodule update --init --recursive && python setup.py install

server/text_generation_server/layers/moe/__init__.py

@@ -29,6 +29,8 @@ if SYSTEM == "rocm":
     from vllm.model_executor.layers.fused_moe import fused_topk
 elif SYSTEM != "ipex":
     from moe_kernels.fused_moe import fused_topk, grouped_topk
+else:
+    from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
 
 
 # NOTE: we are using a protocol here, because multiple inherance is not nice.
@@ -140,6 +142,10 @@ class DenseMoELayer(nn.Module):
             )
             for i in range(self.n_experts)
         ]
+        if SYSTEM == "ipex":
+            self.ipex_fused_moe = GatedMLPMOE(
+                W13=self.gate_proj, W2=self.down_proj, W3=self.up_proj, use_prepack=True
+            )
 
         self.process_group = weights.process_group
 
@@ -152,6 +158,17 @@ class DenseMoELayer(nn.Module):
         input_shape = x.shape
         x = x.view(-1, input_shape[-1])
 
+        if SYSTEM == "ipex":
+            return self.ipex_fused_moe(
+                hidden_states=x,
+                router_logits=gating_output,
+                top_k=self.topk,
+                renormalize=self.renormalize,
+                use_grouped_topk=self.n_expert_group is not None,
+                num_expert_group=self.n_expert_group,
+                topk_group=self.topk_group,
+            )
+
         if self.n_expert_group is not None and self.topk_group is not None:
             topk_weights, topk_ids = grouped_topk(
                 x,

server/text_generation_server/layers/moe/unquantized.py

@@ -10,6 +10,8 @@ if SYSTEM == "rocm":
     from vllm.model_executor.layers.fused_moe import fused_moe
 elif SYSTEM != "ipex":
     from moe_kernels.fused_moe import fused_moe
+else:
+    from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
 
 
 class UnquantizedSparseMoELayer(nn.Module):
@@ -52,6 +54,10 @@ class UnquantizedSparseMoELayer(nn.Module):
             name=down_proj_name,
             weights=weights,
         )
+        if SYSTEM == "ipex":
+            self.ipex_fused_moe = GatedMLPMOE(
+                W13=self.gate_up_proj, W2=self.down_proj, use_prepack=True
+            )
 
     def forward(self, x: torch.Tensor, *, gating_output: torch.Tensor) -> torch.Tensor:
         if SYSTEM == "rocm":
@@ -64,6 +70,16 @@ class UnquantizedSparseMoELayer(nn.Module):
                 renormalize=self.renormalize,
                 inplace=True,
             )
+        elif SYSTEM == "ipex":
+            return self.ipex_fused_moe(
+                hidden_states=x,
+                router_logits=gating_output,
+                top_k=self.topk,
+                renormalize=self.renormalize,
+                use_grouped_topk=self.n_expert_group is not None,
+                num_expert_group=self.n_expert_group,
+                topk_group=self.topk_group,
+            )
 
         return fused_moe(
             x,

server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py

@@ -25,6 +25,8 @@ from text_generation_server.utils.import_utils import SYSTEM
 
 if SYSTEM != "ipex":
     from vllm.model_executor.layers.fused_moe import fused_moe
+else:
+    from intel_extension_for_pytorch.llm.modules import GatedMLPMOE
 
 from text_generation_server.layers.attention import (
     paged_attention,
@@ -488,19 +490,35 @@ class BlockSparseMoE(nn.Module):
         )
 
         self.process_group = weights.process_group
+        if SYSTEM == "ipex":
+            self.ipex_fused_moe = GatedMLPMOE(
+                W13=self.wv1, W2=self.w2, use_prepack=True
+            )
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         # router_logits: (num_tokens, n_experts)
         router_logits = self.gate(x)
-        out = fused_moe(
-            x,
-            self.wv1,
-            self.w2,
-            router_logits,
-            self.top_k,
-            renormalize=self.moe_normalize_expert_weights,
-            inplace=True,
-        )
+
+        if SYSTEM == "ipex":
+            out = self.ipex_fused_moe(
+                hidden_states=x,
+                router_logits=router_logits,
+                top_k=self.top_k,
+                renormalize=self.moe_normalize_expert_weights,
+                use_grouped_topk=False,
+                num_expert_group=None,
+                topk_group=None,
+            )
+        else:
+            out = fused_moe(
+                x,
+                self.wv1,
+                self.w2,
+                router_logits,
+                self.top_k,
+                renormalize=self.moe_normalize_expert_weights,
+                inplace=True,
+            )
 
         # Reduce sum
         if self.process_group.size() > 1: