Fix `head_dim` not existing in all model configs (Transformers backend) (#14141)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

vllm/model_executor/models/transformers.py

@@ -25,7 +25,6 @@ from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 from vllm.attention import Attention
 from vllm.config import VllmConfig
 from vllm.distributed import get_tensor_model_parallel_world_size
-from vllm.distributed.utils import divide
 from vllm.logger import init_logger
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                ReplicatedLinear,
@@ -128,10 +127,12 @@ class TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
 
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
+        model_config = vllm_config.model_config
+        parallel_config = vllm_config.parallel_config
 
         self.config = config
-        self.vocab_size = config.vocab_size
-        self.unpadded_vocab_size = config.vocab_size
+        self.vocab_size = model_config.get_vocab_size()
+        self.unpadded_vocab_size = model_config.get_vocab_size()
 
         self.model: PreTrainedModel = AutoModel.from_config(
             self.config,
@@ -145,15 +146,17 @@ class TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
         self.apply_base_model_tp_plan(self.model)
 
         # Attention modifications (assumes 1 attention op per hidden layer)
-        tp_size = get_tensor_model_parallel_world_size()
+        num_heads = model_config.get_num_attention_heads(parallel_config)
+        head_size = model_config.get_head_size()
+        num_kv_heads = model_config.get_num_kv_heads(parallel_config)
         self.attention_instances = [
             Attention(
-                num_heads=divide(config.num_attention_heads, tp_size),
-                head_size=config.head_dim,
+                num_heads=num_heads,
+                head_size=head_size,
                 # NOTE: We use Llama scale as default, if it's set by
                 # Transformers, it's updated in vllm_flash_attention_forward
-                scale=config.head_dim**-0.5,
-                num_kv_heads=divide(config.num_key_value_heads, tp_size),
+                scale=head_size**-0.5,
+                num_kv_heads=num_kv_heads,
                 cache_config=cache_config,
                 quant_config=self.quant_config,
                 prefix=f"{i}.attn") for i in range(config.num_hidden_layers)
@@ -163,7 +166,7 @@ class TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
         self.replace_vocab_embed_class(self.model)
 
         # ForCausalLM modifications
-        self.lm_head = ParallelLMHead(config.vocab_size,
+        self.lm_head = ParallelLMHead(self.vocab_size,
                                       config.hidden_size,
                                       quant_config=self.quant_config,
                                       prefix=maybe_prefix(prefix, "lm_head"))
@@ -172,7 +175,7 @@ class TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
 
         logit_scale = getattr(config, "logit_scale", 1.0)
         self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
-                                                config.vocab_size, logit_scale)
+                                                self.vocab_size, logit_scale)
         self.sampler = get_sampler()
 
     def apply_base_model_tp_plan(self, module: nn.Module, prefix: str = ""):
@@ -203,12 +206,12 @@ class TransformersModel(nn.Module, SupportsQuant, SupportsLoRA):
         new_module = VocabParallelEmbedding(
             self.vocab_size,
             self.config.hidden_size,
-            org_num_embeddings=self.config.vocab_size,
+            org_num_embeddings=self.vocab_size,
             quant_config=None,
         )
         log_replacement("input embedding", self.model.get_input_embeddings(),
                         new_module)
-        self.model.set_input_embeddings(new_module)
+        module.set_input_embeddings(new_module)
 
     def forward(
         self,