Optimized MoE on Gaudi

vllm_hpu_extension/ops.py

@@ -12,6 +12,7 @@
 import math
 import habana_frameworks.torch.core as htcore
 from vllm_hpu_extension.flags import enabled_flags
+
 import habana_frameworks.torch.utils.experimental as htexp
 
 is_hpu_gaudi2 = htexp._get_device_type(
@@ -22,6 +23,9 @@
     FP8_MAX = torch.finfo(torch.float8_e4m3fnuz).max
 
 import os
+
+logger = init_logger(__name__)
+
 # MAX_EXPERTS_PER_SLICE is needed for 1.20, up to 64 experts per slice
 MAX_EXPERTS_PER_SLICE = int(os.environ.get("MAX_EXPERTS_PER_SLICE", -1))
 
@@ -424,6 +428,23 @@ def __init__(self, num_total_experts, experts_min: int = 0, experts_max: int = 8
         self.moe_n_slice = 1 if self.num_experts <= max_expert_per_slice \
                 else self.num_experts // max_expert_per_slice
         self.num_expert_per_group = self.num_experts // self.moe_n_slice
+
+        # if num_tokens exceed the VLLM_DYNAMIC_MOE_MIN_TOKENS,
+        # dynamic MoE is used since its performance is better than
+        # static MoE in this case.
+        self.dynamic_moe_min_tokens = int(
+        os.environ.get("VLLM_DYNAMIC_MOE_MIN_TOKENS", -1))
+        # if the number of expert on a single card is smaller than
+        # VLLM_DYNAMIC_MOE_MIN_EXPERTS_SINGLEHPU, dynamic MoE
+        # is used since its performance is better than
+        # static MoE in this case.
+        self.dynamic_moe_max_num_expert_singleHpu = int(
+        os.environ.get("VLLM_DYNAMIC_MOE_MIN_EXPERTS_SINGLEHPU", 32))
+
+        #self.w13_weight is a tensor of combined w13_list
+        self.w13_weight = None
+        #self.w2_weight is a tensor of combined w2_list
+        self.w2_weight = None
 
     def forward(self,
                 hidden_states,
@@ -432,44 +453,81 @@ def forward(self,
                 permuted_weights=True,
                 activation="silu"):
         # pre-processing for custom op inputs
+        bt, hidden_dim = hidden_states.shape
         experts_range = range(self.num_experts)
         w1_list = [self.w13_list[i].weight.squeeze() for i in experts_range]
         w2_list = [self.w2_list[i].weight.squeeze() for i in experts_range]
 
-        if self.moe_n_slice == 1:
-            return torch.ops.hpu.mixture_of_experts(
-                hidden_states=hidden_states,
-                expert_routing_table=expert_routing_table,
-                router_weights=router_weights,
-                w12=w1_list,
-                w3=w2_list,
-                permuted_weights=permuted_weights,
-                activation=activation,
-                experts_min=self.experts_min,
-                experts_max=self.experts_max)
-        for i in range(self.moe_n_slice):
-            w1_list_slice = w1_list[i * self.num_expert_per_group:(i + 1) * self.num_expert_per_group]
-            w2_list_slice = w2_list[i * self.num_expert_per_group:(i + 1) * self.num_expert_per_group]
-            min_expert = self.experts_min + i * self.num_expert_per_group
-            max_expert = min_expert + self.num_expert_per_group - 1
-            slice_final_hidden_states = torch.ops.hpu.mixture_of_experts(
-                hidden_states=hidden_states,
-                expert_routing_table=expert_routing_table,
-                router_weights=router_weights,
-                w12=w1_list_slice,
-                w3=w2_list_slice,
-                permuted_weights=permuted_weights,
-                activation=activation,
-                experts_min=min_expert,
-                experts_max=max_expert)
-            if i == 0:
-                final_hidden_states = slice_final_hidden_states
-            else:
-                final_hidden_states += slice_final_hidden_states
-            htorch.core.mark_step()
-        return final_hidden_states
+        # When the number of input tokens (batch_size*seqence_length) exceeds
+        # dynamic_moe_min_tokens (default -1) or the number of the experts
+        # on the single card is smaller than dynamic_moe_max_num_expert_singleHpu
+        # (default 32), dynamic MoE is used since it delivers better performance
+        # than static MoE. Otherwise static MoE is used.
+        if bt > self.dynamic_moe_min_tokens or \
+            (self.num_experts <= self.dynamic_moe_max_num_expert_singleHpu):
+            if self.moe_n_slice == 1:
+                return torch.ops.hpu.mixture_of_experts(
+                    hidden_states=hidden_states,
+                    expert_routing_table=expert_routing_table,
+                    router_weights=router_weights,
+                    w12=w1_list,
+                    w3=w2_list,
+                    permuted_weights=permuted_weights,
+                    activation=activation,
+                    experts_min=self.experts_min,
+                    experts_max=self.experts_max)
+            for i in range(self.moe_n_slice):
+                w1_list_slice = w1_list[i * self.num_expert_per_group:(i + 1) * self.num_expert_per_group]
+                w2_list_slice = w2_list[i * self.num_expert_per_group:(i + 1) * self.num_expert_per_group]
+                min_expert = self.experts_min + i * self.num_expert_per_group
+                max_expert = min_expert + self.num_expert_per_group - 1
+                slice_final_hidden_states = torch.ops.hpu.mixture_of_experts(
+                    hidden_states=hidden_states,
+                    expert_routing_table=expert_routing_table,
+                    router_weights=router_weights,
+                    w12=w1_list_slice,
+                    w3=w2_list_slice,
+                    permuted_weights=permuted_weights,
+                    activation=activation,
+                    experts_min=min_expert,
+                    experts_max=max_expert)
+                if i == 0:
+                    final_hidden_states = slice_final_hidden_states
+                else:
+                    final_hidden_states += slice_final_hidden_states
+        else:
+            if self.w13_weight is None:
+                self.w13_weight = torch.stack(w1_list)
 
+            if self.w2_weight is None:
+                self.w2_weight = torch.stack(w2_list)
 
+            selected_experts = (expert_routing_table - self.experts_min).to(torch.int64)
+            moe_intermediate = self.w2_weight.shape[2]
+            padded_weights = torch.zeros((bt, self.num_experts),
+                                         dtype=hidden_states.dtype,
+                                         device=hidden_states.device)
+            padded_weights.scatter_(-1, selected_experts, router_weights)
+            padded_weights = padded_weights.transpose(0, 1).unsqueeze(-1)
+
+            up_gate_states = torch.matmul(
+                hidden_states,
+                self.w13_weight.view(-1, self.w13_weight.size(-1)).transpose(
+                    0, 1))
+            up_gate_states = up_gate_states.reshape(bt, self.num_experts, 2,
+                                                    moe_intermediate)
+            up_states = up_gate_states[:, :, 0, :]
+            gate_states = up_gate_states[:, :, 1, :]
+            current_state_static = F.silu(up_states) * gate_states
+            current_state_static = current_state_static.transpose(0, 1)
+
+            current_hidden_states_static = torch.matmul(
+                current_state_static, self.w2_weight.transpose(
+                    1, 2)) * padded_weights
+            final_hidden_states = current_hidden_states_static.sum(dim=0)
+
+        return final_hidden_states.view(-1, hidden_states.shape[1])
+
 class DynamicFusedMOE(torch.nn.Module):
 
     def __init__(self, num_total_experts):