DCO

Signed-off-by: Woosuk Kwon <[email protected]>

vllm/model_executor/models/llama.py

@@ -538,16 +538,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
             normalize=False,
             softmax=False)
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         model_output = self.model(input_ids, positions, kv_caches,
-                                  attn_metadata, intermediate_tensors)
+                                  attn_metadata, intermediate_tensors,
+                                  inputs_embeds)
         return model_output
 
     def compute_logits(

vllm/model_executor/models/llava.py

@@ -17,6 +17,7 @@
 from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.base import NestedTensors
 from vllm.sequence import IntermediateTensors
 from vllm.utils import is_list_of
 
@@ -448,13 +449,33 @@ def _process_image_input(self,
         image_features = self._process_image_pixels(image_input)
         return self.multi_modal_projector(image_features)
 
+    def process_mm_inputs(self, **kwargs):
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        vision_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
+        if vision_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, vision_embeddings,
+                self.config.image_token_index)
+        return inputs_embeds
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
         **kwargs: object,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         """Run forward pass for LLaVA-1.5.
@@ -494,24 +515,13 @@ def forward(
         """
         if intermediate_tensors is not None:
             inputs_embeds = None
-        else:
-            image_input = self._parse_and_validate_image_input(**kwargs)
-            if image_input is not None:
-                vision_embeddings = self._process_image_input(image_input)
-                inputs_embeds = self.language_model.model.get_input_embeddings(
-                    input_ids)
-
-                inputs_embeds = merge_multimodal_embeddings(
-                    input_ids, inputs_embeds, vision_embeddings,
-                    self.config.image_token_index)
-            else:
-                inputs_embeds = self.language_model.model.get_input_embeddings(
-                    input_ids)
-
-        # always pass the input via `inputs_embeds`
-        # to make sure the computation graph is consistent
-        # for `torch.compile` integration
-        input_ids = None
+        elif inputs_embeds is None:
+            vision_embeddings = self.process_mm_inputs(**kwargs)
+            # always pass the input via `inputs_embeds`
+            # to make sure the computation graph is consistent
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
 
         hidden_states = self.language_model.model(input_ids,
                                                   positions,

vllm/model_executor/models/opt.py

@@ -360,16 +360,21 @@ def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         self.make_empty_intermediate_tensors = (
             self.model.make_empty_intermediate_tensors)
 
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
     def forward(
         self,
         input_ids: torch.Tensor,
         positions: torch.Tensor,
         kv_caches: List[torch.Tensor],
         attn_metadata: AttentionMetadata,
         intermediate_tensors: Optional[IntermediateTensors] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, kv_caches,
-                                   attn_metadata, intermediate_tensors)
+                                   attn_metadata, intermediate_tensors,
+                                   inputs_embeds)
         return hidden_states
 
     def compute_logits(

vllm/model_executor/models/phi3v.py

@@ -39,6 +39,7 @@
 from vllm.model_executor.pooling_metadata import PoolingMetadata
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
+from vllm.multimodal.base import NestedTensors, PlaceholderRange
 from vllm.multimodal.utils import cached_get_tokenizer, repeat_and_pad_token
 from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.utils import is_list_of
@@ -500,23 +501,29 @@ def input_processor_for_phi3v(ctx: InputContext,
 
     # TODO: Move this to utils or integrate with clip.
     new_token_ids: List[int] = []
+    placeholder_ranges: List[PlaceholderRange] = []
     placeholder_idx = 0
     while merged_token_ids:
         token_id = merged_token_ids.pop(0)
         if token_id == _IMAGE_TOKEN_ID:
-            new_token_ids.extend(
-                repeat_and_pad_token(
-                    _IMAGE_TOKEN_ID,
-                    repeat_count=image_feature_size[placeholder_idx],
-                ))
+            replacement_ids = repeat_and_pad_token(
+                _IMAGE_TOKEN_ID,
+                repeat_count=image_feature_size[placeholder_idx],
+            )
+            placeholder_ranges.append({
+                "offset": len(new_token_ids),
+                "length": len(replacement_ids)
+            })
+            new_token_ids.extend(replacement_ids)
             placeholder_idx += 1
         else:
             new_token_ids.append(token_id)
 
     # NOTE: Create a defensive copy of the original inputs
     return token_inputs(prompt_token_ids=new_token_ids,
                         prompt=new_prompt,
-                        multi_modal_data=multi_modal_data)
+                        multi_modal_data=multi_modal_data,
+                        multi_modal_placeholders={"image": placeholder_ranges})
 
 
 @MULTIMODAL_REGISTRY.register_image_input_mapper()
@@ -669,32 +676,42 @@ def _process_image_input(
 
         return image_embeds
 
+    def process_mm_inputs(self, **kwargs):
+        image_input = self._parse_and_validate_image_input(**kwargs)
+        if image_input is None:
+            return None
+        vision_embeddings = self._process_image_input(image_input)
+        return vision_embeddings
+
+    def get_input_embeddings(
+        self,
+        input_ids: torch.Tensor,
+        vision_embeddings: Optional[NestedTensors] = None,
+    ) -> torch.Tensor:
+        inputs_embeds = self.embed_tokens(input_ids)
+        if vision_embeddings is not None:
+            inputs_embeds = merge_multimodal_embeddings(
+                input_ids, inputs_embeds, vision_embeddings,
+                self.image_token_id)
+        return inputs_embeds
+
     def forward(self,
                 input_ids: torch.Tensor,
                 positions: torch.Tensor,
                 kv_caches: List[torch.Tensor],
                 attn_metadata: AttentionMetadata,
                 intermediate_tensors: Optional[IntermediateTensors] = None,
+                inputs_embeds: Optional[torch.Tensor] = None,
                 **kwargs: object):
         if intermediate_tensors is not None:
             inputs_embeds = None
-        else:
-            image_input = self._parse_and_validate_image_input(**kwargs)
-
-            if image_input is not None:
-                vision_embeddings = self._process_image_input(image_input)
-                inputs_embeds = self.embed_tokens(input_ids)
-                inputs_embeds = merge_multimodal_embeddings(
-                    input_ids, inputs_embeds, vision_embeddings,
-                    self.image_token_id)
-            else:
-                inputs_embeds = self.language_model.model.embed_tokens(
-                    input_ids)
-
-        # always pass the input via `inputs_embeds`
-        # to make sure the computation graph is consistent
-        # for `torch.compile` integration
-        input_ids = None
+        elif inputs_embeds is None:
+            vision_embeddings = self.process_mm_inputs(**kwargs)
+            # always pass the input via `inputs_embeds`
+            # to make sure the computation graph is consistent
+            inputs_embeds = self.get_input_embeddings(input_ids,
+                                                      vision_embeddings)
+            input_ids = None
 
         hidden_states = self.language_model.model(input_ids,
                                                   positions,

vllm/v1/core/encoder_cache_manager.py

@@ -0,0 +1,48 @@
+from typing import Dict, List, Set, Tuple
+
+from vllm.v1.request import Request
+
+
+class EncoderCacheManager:
+
+    def __init__(self, cache_size: int):
+        self.cache_size = cache_size
+        self.num_free_slots = cache_size
+        # req_id -> cached input ids
+        self.cached: Dict[str, Set[int]] = {}
+        # List of [req_id, input_id]
+        self.freed: List[Tuple[str, int]] = []
+
+    def has_cache(self, request: Request, input_id: int) -> bool:
+        req_id = request.request_id
+        return req_id in self.cached and input_id in self.cached[req_id]
+
+    def can_allocate(self, request: Request, input_id: int) -> bool:
+        num_tokens = request.get_num_encoder_tokens(input_id)
+        return num_tokens <= self.num_free_slots
+
+    def allocate(self, request: Request, input_id: int) -> None:
+        req_id = request.request_id
+        if req_id not in self.cached:
+            self.cached[req_id] = set()
+        self.cached[req_id].add(input_id)
+        self.num_free_slots -= request.get_num_encoder_tokens(input_id)
+
+    def get_cached_input_ids(self, request: Request) -> Set[int]:
+        return self.cached.get(request.request_id, set())
+
+    def free(self, request: Request, input_id: int) -> None:
+        req_id = request.request_id
+        if req_id not in self.cached:
+            return
+
+        self.cached[req_id].discard(input_id)
+        if len(self.cached[req_id]) == 0:
+            del self.cached[req_id]
+        self.num_free_slots += request.get_num_encoder_tokens(input_id)
+        self.freed.append((req_id, input_id))
+
+    def get_freed_ids(self) -> List[Tuple[str, int]]:
+        freed = self.freed
+        self.freed = []
+        return freed