add vocab padding for LLama(Support WizardLM) (#411)

vllm/model_executor/models/llama.py

@@ -187,10 +187,9 @@ class LlamaModel(nn.Module):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.embed_tokens = VocabParallelEmbedding(
-            config.vocab_size,
-            config.hidden_size,
-            perform_initialization=False)
+            vocab_size, config.hidden_size, perform_initialization=False)
         self.layers = nn.ModuleList([
             LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)
         ])
@@ -228,8 +227,9 @@ class LlamaForCausalLM(nn.Module):
         super().__init__()
         self.config = config
         self.model = LlamaModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.lm_head = ColumnParallelLinear(config.hidden_size,
-                                            config.vocab_size,
+                                            vocab_size,
                                             bias=False,
                                             gather_output=False,
                                             perform_initialization=False)
@@ -259,6 +259,8 @@ class LlamaForCausalLM(nn.Module):
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
                      use_np_cache: bool = False):
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
@@ -267,6 +269,17 @@ class LlamaForCausalLM(nn.Module):
             if "rotary_emb.inv_freq" in name:
                 continue
 
+            if "embed_tokens" in name or "lm_head" in name:
+                param = state_dict[name]
+                # Consider padding in the vocab size.
+                padded_vocab_size = (param.shape[0] *
+                                     tensor_model_parallel_world_size)
+                num_extra_rows = padded_vocab_size - self.config.vocab_size
+                extra_rows = torch.empty(num_extra_rows,
+                                         loaded_weight.shape[1])
+                extra_rows = extra_rows.to(loaded_weight)
+                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+
             is_attention_weight = False
             for stride_id, att_weight_name in enumerate(
                 ["q_proj", "k_proj", "v_proj"]):