[Models] Support Qwen model with PP

docs/source/serving/distributed_serving.rst

@@ -50,7 +50,7 @@ You can also additionally specify :code:`--pipeline-parallel-size` to enable pip
     $     --pipeline-parallel-size 2
 
 .. note::
-    Pipeline parallel is a beta feature. It is only supported for online serving as well as LLaMa, GPT2, and Mixtral style models.
+    Pipeline parallel is a beta feature. It is only supported for online serving as well as LLaMa, GPT2, Mixtral, Qwen, Qwen2, and Nemotron style models.
 
 Multi-Node Inference and Serving
 --------------------------------

vllm/config.py

@@ -42,6 +42,7 @@
     "NemotronForCausalLM",
     "Qwen2ForCausalLM",
     "Qwen2MoeForCausalLM",
+    "QWenLMHeadModel",
 ]
 
 

vllm/model_executor/models/qwen.py

@@ -12,7 +12,7 @@
 
 from vllm.attention import Attention, AttentionMetadata
 from vllm.config import CacheConfig
-from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
@@ -30,6 +30,8 @@
 from vllm.sequence import IntermediateTensors, SamplerOutput
 from vllm.utils import print_warning_once
 
+from .utils import is_pp_missing_parameter, make_layers
+
 
 class QWenMLP(nn.Module):
 
@@ -186,6 +188,7 @@ def __init__(
         config: PretrainedConfig,
         cache_config: Optional[CacheConfig] = None,
         quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
     ):
         super().__init__()
         self.config = config
@@ -195,10 +198,10 @@ def __init__(
             config.vocab_size,
             config.hidden_size,
         )
-        self.h = nn.ModuleList([
-            QWenBlock(config, cache_config, quant_config)
-            for _ in range(config.num_hidden_layers)
-        ])
+        self.start_layer, self.end_layer, self.h = make_layers(
+            config.num_hidden_layers,
+            lambda prefix: QWenBlock(config, cache_config, quant_config),
+            prefix=f"{prefix}.h")
         self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
     def forward(
@@ -207,18 +210,29 @@ def forward(
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
+        intermediate_tensors: Optional[IntermediateTensors],
     ) -> torch.Tensor:
-        hidden_states = self.wte(input_ids)
-        residual = None
-        for i in range(len(self.h)):
+        if get_pp_group().is_first_rank:
+            hidden_states = self.wte(input_ids)
+            residual = None
+        else:
+            assert intermediate_tensors is not None
+            hidden_states = intermediate_tensors["hidden_states"]
+            residual = intermediate_tensors["residual"]
+        for i in range(self.start_layer, self.end_layer):
             layer = self.h[i]
             hidden_states, residual = layer(
                 positions,
                 hidden_states,
-                kv_caches[i],
+                kv_caches[i - self.start_layer],
                 attn_metadata,
                 residual,
             )
+        if not get_pp_group().is_last_rank:
+            return IntermediateTensors({
+                "hidden_states": hidden_states,
+                "residual": residual
+            })
         hidden_states, _ = self.ln_f(hidden_states, residual)
         return hidden_states
 
@@ -250,9 +264,23 @@ def forward(
         intermediate_tensors: Optional[IntermediateTensors] = None,
     ) -> torch.Tensor:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
-                                         attn_metadata)
+                                         attn_metadata, intermediate_tensors)
         return hidden_states
 
+    def make_empty_intermediate_tensors(
+            self, batch_size: int, dtype: torch.dtype,
+            device: torch.device) -> IntermediateTensors:
+        return IntermediateTensors({
+            "hidden_states":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+            "residual":
+            torch.zeros((batch_size, self.config.hidden_size),
+                        dtype=dtype,
+                        device=device),
+        })
+
     def compute_logits(self, hidden_states: torch.Tensor,
                        sampling_metadata: SamplingMetadata) -> torch.Tensor:
         logits = self.logits_processor(self.lm_head, hidden_states,
@@ -284,6 +312,9 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
                 param = params_dict[name]
                 weight_loader = param.weight_loader
                 weight_loader(param, loaded_weight, shard_id)
@@ -301,6 +332,9 @@ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
                         "Only text inputs are allowed. Images won't be handled "
                         "until Qwen-VL models are fully supported.")
                     continue
+                # Skip layers on other devices.
+                if is_pp_missing_parameter(name, self):
+                    continue
                 param = params_dict[name]
                 weight_loader = getattr(param, "weight_loader",
                                         default_weight_loader)