Add fixed moe from mixtral

Signed-off-by: Daniel Huang <[email protected]>

optimum/habana/transformers/models/snowflake/modeling_arctic.py

@@ -754,48 +754,33 @@ def _moe_foreward(self, hidden_states: torch.Tensor) -> torch.Tensor:
 
         routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
         routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-        if self.top_k > 1:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
         # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
 
         final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+            (batch_size, sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
         )
 
-        # Matching between experts, tokens, and their top-k rank. For every i,
-        # expert_idx[i] is the rank topk_idx[i] expert for token_idx[i].
-        expert_idx, token_idx, topk_idx = torch.where(
-            selected_experts
-            == torch.arange(
-                self.num_experts,
-                device=selected_experts.device,
-            ).view((self.num_experts, 1, 1))
+        padded_weights = torch.zeros(
+            (batch_size * sequence_length, self.num_experts), dtype=hidden_states.dtype, device=hidden_states.device
         )
-
-        # Split into one chunk per expert.
-        bincount = torch.bincount(expert_idx, minlength=self.num_experts).tolist()
-        token_idx = token_idx.split(bincount)
-        topk_idx = topk_idx.split(bincount)
+        padded_weights.scatter_(-1, selected_experts, routing_weights)
+        padded_weights = padded_weights.reshape(-1, sequence_length, self.num_experts)
+        padded_weights = padded_weights.permute(2, 0, 1).unsqueeze(-1)
 
         # Loop over all available experts in the model and perform the computation on each expert
-        for expert_layer, top_x, idx in zip(self.experts, token_idx, topk_idx):
-            if top_x.shape[0] == 0:
-                continue
-
-            # in torch it is faster to index using lists than torch tensors
-            top_x_list = top_x.tolist()
-            idx_list = idx.tolist()
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-            # torch.distributed.barrier()
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            padded_weight = padded_weights[expert_idx]
+            current_state_static = hidden_states.reshape(-1, hidden_dim)
+            current_hidden_states_static = (
+                expert_layer(current_state_static).reshape(-1, sequence_length, hidden_dim) * padded_weight
+            )
+            final_hidden_states += current_hidden_states_static
+            # support long sequences exceeding 8192
+            if not self.training and sequence_length > 8192:
+                htcore.mark_step()
         final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
         return final_hidden_states, load_balancing_loss_func(
             (router_logits,), self.num_experts, self.top_k