[Misc] Add vision language model support to CPU backend (vllm-project…

…#3968)

vllm/executor/cpu_executor.py

@@ -45,6 +45,7 @@ def _init_worker(self):
             rank=0,
             distributed_init_method=distributed_init_method,
             lora_config=self.lora_config,
+            vision_language_config=self.vision_language_config,
             kv_cache_dtype=self.cache_config.cache_dtype,
             is_driver_worker=True,
         )

vllm/worker/cpu_model_runner.py

@@ -5,7 +5,7 @@
 
 from vllm.attention import AttentionMetadata, get_attn_backend
 from vllm.config import (DeviceConfig, LoadConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig)
+                         ParallelConfig, SchedulerConfig, VisionLanguageConfig)
 from vllm.distributed import broadcast_tensor_dict
 from vllm.logger import init_logger
 from vllm.model_executor import SamplingMetadata
@@ -29,6 +29,7 @@ def __init__(
         device_config: DeviceConfig,
         load_config: LoadConfig,
         lora_config: Optional[LoRAConfig],
+        vision_language_config: Optional[VisionLanguageConfig],
         kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
         *args,
@@ -38,6 +39,7 @@ def __init__(
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.lora_config = lora_config
+        self.vision_language_config = vision_language_config
         self.load_config = load_config
         self.is_driver_worker = is_driver_worker
 
@@ -59,13 +61,14 @@ def __init__(
         self.block_size: int  # Set after initial profiling.
 
     def load_model(self) -> None:
-        self.model = get_model(model_config=self.model_config,
-                               load_config=self.load_config,
-                               device_config=self.device_config,
-                               vision_language_config=None,
-                               lora_config=self.lora_config,
-                               parallel_config=self.parallel_config,
-                               scheduler_config=self.scheduler_config)
+        self.model = get_model(
+            model_config=self.model_config,
+            load_config=self.load_config,
+            device_config=self.device_config,
+            vision_language_config=self.vision_language_config,
+            lora_config=self.lora_config,
+            parallel_config=self.parallel_config,
+            scheduler_config=self.scheduler_config)
 
     def _prepare_prompt(
         self,
@@ -76,6 +79,7 @@ def _prepare_prompt(
         input_positions: List[int] = []
         slot_mapping: List[int] = []
         prompt_lens: List[int] = []
+        multi_modal_input_list: List[torch.Tensor] = []
 
         for seq_group_metadata in seq_group_metadata_list:
             assert seq_group_metadata.is_prompt
@@ -96,6 +100,10 @@ def _prepare_prompt(
             # is always the first token in the sequence.
             input_positions.extend(list(range(computed_len, prompt_len)))
 
+            if seq_group_metadata.multi_modal_data:
+                multi_modal_input_list.append(
+                    seq_group_metadata.multi_modal_data.data)
+
             # Compute the slot mapping.
             block_table = seq_group_metadata.block_tables[seq_id]
             # Mask the [0, start_idx) tokens of the prompt with _PAD_SLOT_ID,
@@ -118,6 +126,15 @@ def _prepare_prompt(
                 slot = block_number * self.block_size + block_offset
                 slot_mapping.append(slot)
 
+        if multi_modal_input_list:
+            assert self.vision_language_config, (
+                "Multi-modal inputs are only supported by "
+                "vision language models.")
+            multi_modal_input = torch.cat(multi_modal_input_list,
+                                          dim=0).to(self.device)
+        else:
+            multi_modal_input = None
+
         num_prompt_tokens = len(input_tokens)
 
         input_tokens = torch.tensor(input_tokens,
@@ -144,12 +161,8 @@ def _prepare_prompt(
             slot_mapping=slot_mapping,
             kv_cache_dtype=self.kv_cache_dtype,
         )
-        return (
-            input_tokens,
-            input_positions,
-            attn_metadata,
-            prompt_lens,
-        )
+        return (input_tokens, input_positions, attn_metadata, prompt_lens,
+                multi_modal_input)
 
     def _prepare_decode(
         self,
@@ -336,14 +349,16 @@ def prepare_input_tensors(
         seq_group_metadata_list: List[SequenceGroupMetadata],
     ) -> Tuple[torch.Tensor, torch.Tensor, AttentionMetadata,
                SamplingMetadata]:
+        multi_modal_input = None
         if self.is_driver_worker:
             # NOTE: We assume that all sequences in the group are all prompts or
             # all decodes.
             is_prompt = seq_group_metadata_list[0].is_prompt
             # Prepare input tensors.
             if is_prompt:
-                (input_tokens, input_positions, attn_metadata,
-                 prompt_lens) = self._prepare_prompt(seq_group_metadata_list)
+                (input_tokens, input_positions, attn_metadata, prompt_lens,
+                 multi_modal_input
+                 ) = self._prepare_prompt(seq_group_metadata_list)
             else:
                 (input_tokens, input_positions,
                  attn_metadata) = self._prepare_decode(seq_group_metadata_list)
@@ -376,20 +391,17 @@ def prepare_input_tensors(
                 perform_sampling=False,
             )
 
-        return (
-            input_tokens,
-            input_positions,
-            attn_metadata,
-            sampling_metadata,
-        )
+        return (input_tokens, input_positions, attn_metadata,
+                sampling_metadata, multi_modal_input)
 
     @torch.inference_mode()
     def execute_model(
         self,
         seq_group_metadata_list: List[SequenceGroupMetadata],
         kv_caches: List[torch.Tensor],
     ) -> Optional[SamplerOutput]:
-        (input_tokens, input_positions, attn_metadata, sampling_metadata
+        (input_tokens, input_positions, attn_metadata, sampling_metadata,
+         multi_modal_input
          ) = self.prepare_input_tensors(seq_group_metadata_list)
 
         model_executable = self.model
@@ -399,6 +411,8 @@ def execute_model(
             "kv_caches": kv_caches,
             "attn_metadata": attn_metadata,
         }
+        if self.vision_language_config:
+            execute_model_kwargs.update({"image_input": multi_modal_input})
 
         hidden_states = model_executable(**execute_model_kwargs)
 

vllm/worker/cpu_worker.py

@@ -6,7 +6,8 @@
 
 from vllm.attention import get_attn_backend
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
-                         ModelConfig, ParallelConfig, SchedulerConfig)
+                         ModelConfig, ParallelConfig, SchedulerConfig,
+                         VisionLanguageConfig)
 from vllm.distributed import (broadcast_tensor_dict,
                               ensure_model_parallel_initialized,
                               init_distributed_environment)
@@ -122,6 +123,7 @@ def __init__(
         rank: int,
         distributed_init_method: str,
         lora_config: Optional[LoRAConfig] = None,
+        vision_language_config: Optional[VisionLanguageConfig] = None,
         kv_cache_dtype: Optional[str] = "auto",
         is_driver_worker: bool = False,
     ) -> None:
@@ -135,21 +137,25 @@ def __init__(
         self.rank = rank
         self.distributed_init_method = distributed_init_method
         self.lora_config = lora_config
+        self.vision_language_config = vision_language_config
         self.is_driver_worker = is_driver_worker
         if self.is_driver_worker:
             assert self.rank == 0, "The driver worker must have rank 0."
+
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing
             from vllm.utils import init_cached_hf_modules
             init_cached_hf_modules()
-        self.model_runner = CPUModelRunner(model_config,
-                                           parallel_config,
-                                           scheduler_config,
-                                           device_config,
-                                           load_config=self.load_config,
-                                           lora_config=self.lora_config,
-                                           kv_cache_dtype=kv_cache_dtype,
-                                           is_driver_worker=is_driver_worker)
+        self.model_runner = CPUModelRunner(
+            model_config,
+            parallel_config,
+            scheduler_config,
+            device_config,
+            load_config=self.load_config,
+            lora_config=self.lora_config,
+            vision_language_config=self.vision_language_config,
+            kv_cache_dtype=kv_cache_dtype,
+            is_driver_worker=is_driver_worker)
         # Uninitialized cache engine. Will be initialized by
         # initialize_cache.
         self.cache_engine: CPUCacheEngine