new awq kernels paths (#2572)

* new awq kernels paths

Author: vince62s

onmt/modules/moe.py

@@ -60,7 +60,9 @@ def forward(self, x):
         y = torch.empty_like(x)
         for i, expert in enumerate(self.experts):
             if torch.any(flat_expert_indices == i):
-                y[flat_expert_indices == i] = expert(x[flat_expert_indices == i])
+                y[flat_expert_indices == i] = expert(
+                    x[flat_expert_indices == i].unsqueeze(0)
+                )
         y = (y.view(*expert_weights.shape, -1) * expert_weights.unsqueeze(-1)).sum(
             dim=1
         )

onmt/modules/rmsnorm.py

@@ -4,11 +4,11 @@
 import torch.nn as nn
 
 try:
-    import awq_inference_engine
+    import awq_ext
 
-    AWQ_INFERENCE_ENGINE = True
+    AWQ_EXT = True
 except ImportError:
-    AWQ_INFERENCE_ENGINE = False
+    AWQ_EXT = False
 
 
 class RMSNorm(torch.nn.Module):
@@ -24,12 +24,12 @@ def __init__(self, hidden_size: int, eps: float = 1e-6):
         self.weight = nn.Parameter(torch.ones(hidden_size))
 
     def forward(self, hidden_states):
-        if AWQ_INFERENCE_ENGINE and not self.training:
+        if AWQ_EXT and not self.training:
             inp_type = hidden_states.dtype
             output = torch.empty_like(hidden_states).to(inp_type)
             if hidden_states.dim() == 2:  # patch for multi experts
                 hidden_states = hidden_states.unsqueeze(0)
-            awq_inference_engine.layernorm_forward_cuda(
+            awq_ext.layernorm_forward_cuda(
                 hidden_states.half(), self.weight.half(), output.half(), self.eps
             )
             if hidden_states.dim() == 2:  # patch for multi experts