[V0 Deprecation] Remove MultiModalPlaceholderMap (#25366)

Signed-off-by: DarkLight1337 <[email protected]>

Author: DarkLight1337

tests/kernels/utils.py

@@ -959,7 +959,6 @@ def make_test_metadata(
         return attn_backend_obj.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=(None if kv_mmap is None else kv_mmap.slot_mapping),
-            multi_modal_placeholder_index_maps=None,
             enable_kv_scales_calculation=True,
             num_prefill_tokens=num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,
@@ -1009,7 +1008,6 @@ def make_test_metadata(
         return attn_backend_obj.make_metadata(
             num_prefills=num_prefills,
             slot_mapping=kv_mmap.slot_mapping,
-            multi_modal_placeholder_index_maps=None,
             enable_kv_scales_calculation=True,
             num_prefill_tokens=num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,

vllm/attention/backends/abstract.py

@@ -10,7 +10,6 @@
 import torch
 
 from vllm.model_executor.layers.quantization.utils.quant_utils import QuantKey
-from vllm.multimodal import MultiModalPlaceholderMap
 
 
 class AttentionType:
@@ -116,15 +115,6 @@ class AttentionMetadata:
     # in block 0, and 1st slot in block 1, respectively.
     slot_mapping: torch.Tensor
 
-    # The index maps that relate multi-modal embeddings to the corresponding
-    # placeholders.
-    #
-    # N.B. These aren't really related to attention and don't belong on this
-    # type -- this is just a temporary solution to make them available to
-    # `model_executable`.
-    multi_modal_placeholder_index_maps: Optional[Dict[
-        str, MultiModalPlaceholderMap.IndexMap]]
-
     # Enable/disable KV scales calculation. This is so that we can disable the
     # calculation until after prefill and cuda graph capture.
     enable_kv_scales_calculation: bool

vllm/attention/backends/placeholder_attn.py

@@ -1,18 +1,16 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from collections import defaultdict
 from dataclasses import dataclass
 from itertools import accumulate
-from typing import Dict, List, Optional, Tuple, Type
+from typing import List, Optional, Tuple, Type
 
 import torch
 
 from vllm.attention.backends.abstract import (AttentionBackend, AttentionImpl,
                                               AttentionMetadata,
                                               AttentionMetadataBuilder)
 from vllm.attention.backends.utils import CommonAttentionState
-from vllm.multimodal import MultiModalPlaceholderMap
 from vllm.utils import async_tensor_h2d
 
 # Placeholder attention backend for models like Mamba and pooling models that
@@ -141,8 +139,6 @@ def prefill_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
             num_prefill_tokens=self.num_prefill_tokens,
             num_decode_tokens=0,
             slot_mapping=slot_mapping,
-            multi_modal_placeholder_index_maps=self.
-            multi_modal_placeholder_index_maps,
             enable_kv_scales_calculation=self.enable_kv_scales_calculation,
             seq_lens=seq_lens,
             seq_lens_tensor=seq_lens_tensor,
@@ -178,7 +174,6 @@ def decode_metadata(self) -> Optional["PlaceholderAttentionMetadata"]:
             num_prefill_tokens=0,
             num_decode_tokens=self.num_decode_tokens,
             slot_mapping=slot_mapping,
-            multi_modal_placeholder_index_maps=None,
             enable_kv_scales_calculation=True,
             seq_lens=None,
             seq_lens_tensor=seq_lens_tensor,
@@ -210,9 +205,6 @@ def prepare(self):
         self.prefill_seq_lens: List[int] = []
         self.context_lens: List[int] = []
         self.curr_seq_lens: List[int] = []
-        self.multimodal_placeholder_maps: Dict[
-            str,
-            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
         self.num_prefills = 0
         self.num_prefill_tokens = 0
         self.num_decode_tokens = 0
@@ -232,12 +224,6 @@ def _add_seq_group(self, inter_data, chunked_prefill_enabled: bool):
             self.context_lens.append(context_len)
 
             if is_prompt:
-                mm_maps = inter_data.multi_modal_placeholder_maps
-                if mm_maps:
-                    for modality, placeholders in mm_maps.items():
-                        self.multimodal_placeholder_maps[modality].extend(
-                            placeholders)
-
                 self.num_prefills += 1
                 self.num_prefill_tokens += token_len
                 self.prefill_seq_lens.append(seq_len)
@@ -295,20 +281,13 @@ def build(self, seq_lens: List[int], query_lens: List[int],
         seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
                                                 device, self.runner.pin_memory)
 
-        placeholder_index_maps = {
-            modality: placeholder_map.index_map()
-            for modality, placeholder_map in
-            self.multimodal_placeholder_maps.items()
-        }
-
         # Placeholders
         slot_mapping_tensor = torch.empty(0)
         block_tables = torch.empty(0)
 
         return PlaceholderAttentionMetadata(
             num_prefills=self.num_prefills,
             slot_mapping=slot_mapping_tensor,
-            multi_modal_placeholder_index_maps=placeholder_index_maps,
             enable_kv_scales_calculation=True,
             num_prefill_tokens=self.num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,

vllm/attention/backends/utils.py

@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Attention backend utils"""
-from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
 from itertools import accumulate
@@ -15,7 +14,6 @@
 from vllm.attention.backends.abstract import AttentionType
 from vllm.config import ModelConfig
 from vllm.logger import init_logger
-from vllm.multimodal import MultiModalPlaceholderMap
 from vllm.utils import async_tensor_h2d, make_tensor_with_pad
 
 logger = init_logger(__name__)
@@ -135,9 +133,6 @@ def prepare(self):
         self.context_lens: List[int] = []
         self.block_tables: List[List[int]] = []
         self.curr_seq_lens: List[int] = []
-        self.multimodal_placeholder_maps: Dict[
-            str,
-            MultiModalPlaceholderMap] = defaultdict(MultiModalPlaceholderMap)
         self.num_prefills = 0
         self.num_prefill_tokens = 0
         self.num_decode_tokens = 0
@@ -154,12 +149,6 @@ def _add_seq_group(self, inter_data, chunked_prefill_enabled: bool):
                  inter_data.curr_sliding_window_blocks):
             self.context_lens.append(context_len)
             if is_prompt:
-                mm_maps = inter_data.multi_modal_placeholder_maps
-                if mm_maps:
-                    for modality, placeholders in mm_maps.items():
-                        self.multimodal_placeholder_maps[modality].extend(
-                            placeholders)
-
                 self.num_prefills += 1
                 self.num_prefill_tokens += token_len
                 self.prefill_seq_lens.append(seq_len)
@@ -254,16 +243,10 @@ def build(self, seq_lens: List[int], query_lens: List[int],
                                                   self.runner.pin_memory)
         seq_start_loc_tensor = async_tensor_h2d(seq_start_loc, torch.int32,
                                                 device, self.runner.pin_memory)
-        placeholder_index_maps = {
-            modality: placeholder_map.index_map()
-            for modality, placeholder_map in
-            self.multimodal_placeholder_maps.items()
-        }
 
         return self._metadata_cls(  # type: ignore
             num_prefills=self.num_prefills,
             slot_mapping=slot_mapping_tensor,
-            multi_modal_placeholder_index_maps=placeholder_index_maps,
             enable_kv_scales_calculation=True,
             num_prefill_tokens=self.num_prefill_tokens,
             num_decode_tokens=num_decode_tokens,
@@ -320,7 +303,6 @@ def graph_capture_get_metadata_for_batch(
             num_prefill_tokens=0,
             num_decode_tokens=batch_size,
             slot_mapping=self._graph_slot_mapping[:batch_size],
-            multi_modal_placeholder_index_maps=None,
             enable_kv_scales_calculation=True,
             seq_lens=None,
             seq_lens_tensor=self._graph_seq_lens[:batch_size],

vllm/multimodal/__init__.py

@@ -1,6 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from .base import MultiModalPlaceholderMap
 from .hasher import MultiModalHasher
 from .inputs import (BatchedTensorInputs, ModalityData, MultiModalDataBuiltins,
                      MultiModalDataDict, MultiModalKwargs,
@@ -27,7 +26,6 @@
     "MultiModalKwargs",
     "MultiModalKwargsItems",
     "MultiModalPlaceholderDict",
-    "MultiModalPlaceholderMap",
     "MultiModalUUIDDict",
     "NestedTensors",
     "MULTIMODAL_REGISTRY",

vllm/multimodal/base.py

@@ -3,83 +3,11 @@
 
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Generic, NamedTuple, TypeVar
+from typing import Generic, TypeVar
 
 _T = TypeVar("_T")
 
 
-class MultiModalPlaceholderMap:
-    """
-    Relates multi-modal embeddings to their corresponding placeholders.
-
-    Note: This is only used in V0.
-    """
-
-    class IndexMap(NamedTuple):
-        src: list[int]
-        dest: list[int]
-
-    src_ranges: list[range]
-    """
-    The indices of the multi-modal embeddings that will replace the
-    corresponding placeholder embeddings pointed to by ``dest_ranges``.
-    """
-
-    src_len: int
-    """
-    The total number of flattened multi-modal embeddings.
-    """
-
-    dest_ranges: list[range]
-    """
-    The indices of the placeholder embeddings that will be replaced by the
-    multimodal embeddings.
-    """
-
-    dest_len: int
-    """
-    The total number of embeddings in the destination tensor.
-    """
-
-    def __init__(self):
-        self.src_ranges = []
-        self.src_len = 0
-        self.dest_ranges = []
-        self.dest_len = 0
-
-    def extend(self, other: "MultiModalPlaceholderMap"):
-        """
-        Adds the placeholders from another ``MultiModalPlaceholderMap`` to this
-        instance based on the source and destination tensors being
-        concatenated.
-        """
-
-        self.src_ranges.extend(
-            range(self.src_len + r.start, self.src_len + r.stop)
-            for r in other.src_ranges)
-        self.src_len += other.src_len
-        self.dest_ranges.extend(
-            range(self.dest_len + r.start, self.dest_len + r.stop)
-            for r in other.dest_ranges)
-        self.dest_len += other.dest_len
-
-    def index_map(self) -> "IndexMap":
-        """
-        Finalizes the placeholder map into lists of indices that can be used to
-        index the source and destination tensors.
-        """
-
-        src_indices = [i for r in self.src_ranges for i in r]
-        dest_indices = [i for r in self.dest_ranges for i in r]
-
-        if len(src_indices) != len(dest_indices):
-            raise ValueError(
-                f"The number of source ({len(src_indices)}) and destination "
-                f"indices ({len(dest_indices)}) must be the same.")
-
-        return self.IndexMap(src=src_indices, dest=dest_indices)
-
-
 class MediaIO(ABC, Generic[_T]):
 
     @abstractmethod

vllm/v1/attention/backends/cpu_attn.py

@@ -425,7 +425,6 @@ def build(self,
                                                     num_prompt_req],  # prefill
             query_start_loc=query_start_loc_cpu[:num_reqs +
                                                 1],  # for logits index
-            multi_modal_placeholder_index_maps=None,
             enable_kv_scales_calculation=False,
         )