[Model] Update Qwen2-Audio model support

vllm/model_executor/models/qwen2.py

@@ -51,7 +51,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.sequence import IntermediateTensors
 
 from .interfaces import SupportsLoRA
-from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers
+from .utils import PPMissingLayer, is_pp_missing_parameter, make_layers, make_empty_intermediate_tensors_factory
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.utils import pad_weight, gemm_bank_conf
@@ -265,6 +265,10 @@ class Qwen2Model(nn.Module):
             prefix=f"{prefix}.layers",
         )
 
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], config.hidden_size))
+        
         if get_pp_group().is_last_rank:
             self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         else:
@@ -370,6 +374,9 @@ class Qwen2ForCausalLM(nn.Module, SupportsLoRA):
         self.logits_processor = LogitsProcessor(config.vocab_size)
         self.sampler = Sampler()
         
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+        
         self.quant_method = None
         if quant_config is not None:
             self.quant_method=quant_config.get_name()

vllm/sequence.py

@@ -29,6 +29,11 @@ VLLM_TOKEN_ID_ARRAY_TYPE = "l"
 VLLM_INVALID_TOKEN_ID = -1
 
 
+def array_full(token_id: int, count: int):
+    """:class:`array` equivalent of :func:`numpy.full`."""
+    return array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
+
+
 # We use dataclass for now because it is used for
 # openai server output, and msgspec is not serializable.
 # TODO(sang): Fix it.
@@ -174,6 +179,26 @@ class SequenceData(msgspec.Struct,
 
     _first_step_flag: bool = True
 
+    @staticmethod
+    def from_prompt_token_counts(
+            *token_counts: Tuple[int, int]) -> "SequenceData":
+        """
+        Construct a :class:`SequenceData` instance by concatenating
+        prompt token sequences.
+
+        Each tuple represents one token sequence, expressed in the form
+        :code:`(token_id, count)`.
+        """
+        if len(token_counts) == 0:
+            return SequenceData.from_seqs([])
+
+        prompt_token_ids_arr = reduce(
+            array.__iadd__,
+            (array_full(token_id, count) for token_id, count in token_counts),
+        )
+
+        return SequenceData(prompt_token_ids_arr)
+    
     @staticmethod
     def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
         if len(token_counts) == 0: