Improve TransformersModel UX (#12785)

vllm/model_executor/models/transformers.py

@@ -15,7 +15,7 @@
 # limitations under the License.
 """Wrapper around `transformers` models"""
 import re
-from typing import Iterable, Optional, Union
+from typing import Iterable, Literal, Optional, Union
 
 import torch
 from torch import nn
@@ -72,15 +72,24 @@ def vllm_flash_attention_forward(
 ALL_ATTENTION_FUNCTIONS["vllm"] = vllm_flash_attention_forward
 
 
+def log_replacement(name: str, old_module: nn.Module, new_module: nn.Module):
+    logger.debug("%s: %s -> %s", name, old_module, new_module)
+
+
 def replace_linear_class(
         linear: nn.Linear,
-        style: str,
+        style: Literal["colwise", "rowwise"],
         quant_config=None) -> Union[ColumnParallelLinear, RowParallelLinear]:
     """
-    In model configurations, we use a neutral type (string) to specify parallel
-    styles, here we use it to translate nn.Linear into vllm-style tp Linear.
-
-    Quant config is not supported yet
+    Replace nn.Linear with one of vLLM's tensor parallel linear classes.
+    
+    `quant_config` is not yet supported.
+    Args:
+        linear (nn.Linear): `nn.Linear` to be replaced.
+        style (str): Tensor parallel style of the new linear, e.g. "colwise".
+        quant_config (QuantConfig): Quantization config for the new linear.
+    Returns:
+        Union[ColumnParallelLinear, RowParallelLinear]: The new linear.
     """
 
     if not isinstance(style, str):
@@ -93,7 +102,10 @@ def replace_linear_class(
     }.get(style)
 
     if vllm_linear_cls is None:
-        raise ValueError(f"Unsupported parallel style value: {style}")
+        logger.warning(
+            "Unsupported parallel style value: %s. "
+            "This layer will not be tensor parallelized.", style)
+        return linear
 
     class HFCompatibleLinear(vllm_linear_cls):
         """
@@ -119,25 +131,24 @@ class TransformersModel(nn.Module):
         super().__init__()
         logger.info("Using Transformers backend.")
 
-        self.vllm_config = vllm_config
         config = vllm_config.model_config.hf_config
         cache_config = vllm_config.cache_config
         quant_config = vllm_config.quant_config
-        self.quant_config = quant_config
+
         self.config = config
+        self.quant_config = quant_config
         self.vocab_size = config.vocab_size
         self.unpadded_vocab_size = config.vocab_size
 
         self.model: PreTrainedModel = AutoModel.from_config(
             self.config,
             attn_implementation="vllm",
-            torch_dtype=vllm_config.model_config.dtype,
             trust_remote_code=vllm_config.model_config.trust_remote_code,
         )
         prefix = self.model.base_model_prefix
 
         # MLP modifications
-        self.tensor_parallelize(self.model)
+        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()
@@ -170,13 +181,13 @@ class TransformersModel(nn.Module):
                                                 config.vocab_size, logit_scale)
         self.sampler = get_sampler()
 
-    def log_replacement(self, name: str, old_module: nn.Module,
-                        new_module: nn.Module):
-        logger.debug("%s: %s -> %s", name, old_module, new_module)
-
-    def tensor_parallelize(self, module: nn.Module, prefix: str = ""):
+    def apply_base_model_tp_plan(self, module: nn.Module, prefix: str = ""):
+        """
+        Apply the base model tensor parallelization plan to a module.
+        Currently only supports linear layers.
+        """
         if (self.config.base_model_tp_plan is None
-                and self.vllm_config.parallel_config.tensor_parallel_size > 1):
+                and get_tensor_model_parallel_world_size() > 1):
             raise ValueError(
                 "Trying to run tensor parallelization but the model does not "
                 "support it yet!")
@@ -189,9 +200,9 @@ class TransformersModel(nn.Module):
                     new_module = replace_linear_class(child_module, style,
                                                       self.quant_config)
                     setattr(module, child_name, new_module)
-                    self.log_replacement(qual_name, child_module, new_module)
+                    log_replacement(qual_name, child_module, new_module)
             else:
-                self.tensor_parallelize(child_module, prefix=qual_name)
+                self.apply_base_model_tp_plan(child_module, prefix=qual_name)
 
     def replace_vocab_embed_class(self, module: nn.Module):
         # Use native set input embeddings
@@ -201,8 +212,8 @@ class TransformersModel(nn.Module):
             org_num_embeddings=self.config.vocab_size,
             quant_config=None,
         )
-        self.log_replacement("input embedding",
-                             self.model.get_input_embeddings(), new_module)
+        log_replacement("input embedding", self.model.get_input_embeddings(),
+                        new_module)
         self.model.set_input_embeddings(new_module)
 
     def forward(