[VLM] Use SequenceData.from_token_counts to create dummy data (#8687)

vllm/inputs/registry.py

@@ -125,7 +125,7 @@ class InputRegistry:
         # Avoid circular import
         from vllm.sequence import SequenceData
 
-        dummy_seq_data = SequenceData.from_counts({0: seq_len})
+        dummy_seq_data = SequenceData.from_token_counts((0, seq_len))
         dummy_multi_modal_data = None
 
         return dummy_seq_data, dummy_multi_modal_data

vllm/model_executor/models/blip.py

@@ -1,6 +1,5 @@
 """Minimal implementation of BlipVisionModel intended to be only used 
 within a vision language model."""
-from array import array
 from typing import Optional, Union
 
 import torch
@@ -19,7 +18,7 @@ from vllm.model_executor.layers.linear import (ColumnParallelLinear,
 from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    repeat_and_pad_placeholder_tokens)
-from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
+from vllm.sequence import SequenceData
 
 try:
     from xformers import ops as xops
@@ -53,6 +52,7 @@ def get_max_blip_image_tokens(
 def dummy_seq_data_for_blip(
     hf_config: Union[BlipVisionConfig, Blip2VisionConfig],
     seq_len: int,
+    num_images: int,
     *,
     image_token_id: int,
     image_feature_size_override: Optional[int] = None,
@@ -62,11 +62,10 @@ def dummy_seq_data_for_blip(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    return SequenceData.from_token_counts(
-                      [image_token_id]) * image_feature_size
+        (image_token_id, image_feature_size * num_images),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (0, seq_len - image_feature_size * num_images),
-                       [0]) * (seq_len - image_feature_size)
+    )
-    return SequenceData(token_ids)
 
 
 def dummy_image_for_blip(

vllm/model_executor/models/blip2.py

@@ -1,4 +1,3 @@
-from array import array
 from typing import (Iterable, List, Literal, Mapping, Optional, Tuple,
                     TypedDict, Union)
 
@@ -18,8 +17,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.model_executor.models.opt import OPTModel
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 
 from .blip import (BlipVisionModel, dummy_image_for_blip,
                    get_max_blip_image_tokens)
@@ -429,11 +427,10 @@ def dummy_seq_data_for_blip2(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    return SequenceData.from_token_counts(
-                      [image_token_id]) * image_feature_size * num_images
+        (image_token_id, image_feature_size * num_images),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (0, seq_len - image_feature_size * num_images),
-                       [0]) * (seq_len - image_feature_size * num_images)
+    )
-    return SequenceData(token_ids)
 
 
 def dummy_data_for_blip2(ctx: InputContext, seq_len: int,

vllm/model_executor/models/chameleon.py

@@ -1,4 +1,3 @@
-from array import array
 from functools import cached_property
 from typing import (Any, Dict, Iterable, List, Literal, Mapping, Optional,
                     Tuple, TypedDict)
@@ -32,8 +31,7 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    repeat_and_pad_placeholder_tokens)
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 from vllm.utils import print_warning_once
 
 from .interfaces import SupportsMultiModal
@@ -72,11 +70,10 @@ def dummy_seq_data_for_chameleon(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    return SequenceData.from_token_counts(
-                      [image_token_id]) * image_feature_size * num_images
+        (image_token_id, image_feature_size * num_images),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (0, seq_len - image_feature_size * num_images),
-                       [0]) * (seq_len - image_feature_size * num_images)
+    )
-    return SequenceData(token_ids)
 
 
 def dummy_image_for_chameleon(

vllm/model_executor/models/clip.py

@@ -1,6 +1,5 @@
 """Minimal implementation of CLIPVisionModel intended to be only used
 within a vision language model."""
-from array import array
 from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
@@ -20,7 +19,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    repeat_and_pad_placeholder_tokens)
-from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
+from vllm.sequence import SequenceData
 
 try:
     from xformers import ops as xops
@@ -62,11 +61,10 @@ def dummy_seq_data_for_clip(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    return SequenceData.from_token_counts(
-                      [image_token_id]) * image_feature_size * num_images
+        (image_token_id, image_feature_size * num_images),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (0, seq_len - image_feature_size * num_images),
-                       [0]) * (seq_len - image_feature_size * num_images)
+    )
-    return SequenceData(token_ids)
 
 
 def dummy_image_for_clip(

vllm/model_executor/models/minicpmv.py

@@ -23,7 +23,6 @@
 """Inference-only MiniCPM-V model compatible with HuggingFace weights."""
 import math
 import re
-from array import array
 from functools import partial
 from typing import (Any, Callable, Iterable, List, Mapping, Optional, Tuple,
                     TypedDict)
@@ -56,8 +55,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.multimodal.utils import cached_get_tokenizer
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 
 from .idefics2_vision_model import Idefics2VisionTransformer
 
@@ -259,8 +257,7 @@ def get_max_minicpmv_image_tokens(ctx: InputContext):
 
 
 def dummy_seq_data_for_minicpmv(seq_len: int, num_images: int):
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE, [0]) * seq_len
+    return SequenceData.from_token_counts((0, seq_len))
-    return SequenceData(token_ids)
 
 
 def dummy_image_for_minicpmv(hf_config: PretrainedConfig, num_images: int):

vllm/model_executor/models/pixtral.py

@@ -1,4 +1,3 @@
-from array import array
 from dataclasses import dataclass, fields
 from itertools import tee
 from typing import Iterable, List, Mapping, Optional, Tuple, Union
@@ -24,8 +23,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 
 from .interfaces import SupportsMultiModal
 from .utils import init_vllm_registered_model
@@ -63,13 +61,11 @@ def dummy_data_for_pixtral(ctx: InputContext, seq_len: int,
     image_feature_size = (size**2) // (patch_size**2)
 
     num_image_tokens = image_feature_size * num_images
+    seq_data = SequenceData.from_token_counts(
+        (image_token_id, num_image_tokens),
+        (0, seq_len - num_image_tokens),
+    )
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                      [image_token_id]) * num_image_tokens
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
-                       [0]) * (seq_len - num_image_tokens)
-
-    seq_data = SequenceData(token_ids)
     mm_data = {"image": num_images * [image]}
     return seq_data, mm_data
 

vllm/model_executor/models/qwen.py

@@ -7,7 +7,6 @@
 
 import math
 import re
-from array import array
 from functools import partial
 from typing import (Any, Callable, Dict, Iterable, List, Literal, Mapping,
                     Optional, Tuple, TypedDict, Union)
@@ -45,8 +44,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.base import MultiModalInputs
 from vllm.multimodal.utils import cached_get_tokenizer
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 from vllm.utils import is_list_of
 
 from .utils import flatten_bn, is_pp_missing_parameter, make_layers
@@ -819,7 +817,7 @@ def dummy_data_for_qwen(
     # The presence of a visual config indicates this is a multimodal model.
     # If we don't have it, the model is considered an LLM for warmup purposes.
     if not hasattr(hf_config, "visual"):
-        seq_data = SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, [0] * seq_len))
+        seq_data = SequenceData.from_token_counts((0, seq_len))
         mm_data = None
         return seq_data, mm_data
 
@@ -846,11 +844,13 @@ def dummy_data_for_qwen(
     if len(toks) < seq_len:
         toks += [0] * (seq_len - len(toks))
 
+    seq_data = SequenceData.from_seqs(toks)
+
     # Build the input images; width/height doesn't actually matter here since
     # the data will get resized and the # of tokens per image is constant
     image = Image.new("RGB", (224, 224), color=0)
     mm_data = {"image": image if num_images == 1 else [image] * num_images}
-    return SequenceData(array(VLLM_TOKEN_ID_ARRAY_TYPE, toks)), mm_data
+    return seq_data, mm_data
 
 
 @MULTIMODAL_REGISTRY.register_image_input_mapper(input_mapper_for_qwen)

vllm/model_executor/models/qwen2_vl.py

@@ -22,7 +22,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """Inference-only Qwen2-VL model compatible with HuggingFace weights."""
-from array import array
 from functools import lru_cache, partial
 from typing import (Iterable, List, Mapping, Optional, Tuple, Type, TypedDict,
                     Union)
@@ -66,8 +65,7 @@ from vllm.multimodal import (MULTIMODAL_REGISTRY, MultiModalDataDict,
 from vllm.multimodal.base import MultiModalData
 from vllm.multimodal.image import cached_get_image_processor
 from vllm.platforms import current_platform
-from vllm.sequence import (VLLM_TOKEN_ID_ARRAY_TYPE, IntermediateTensors,
+from vllm.sequence import IntermediateTensors, SequenceData
-                           SequenceData)
 from vllm.transformers_utils.processor import get_processor
 
 logger = init_logger(__name__)
@@ -681,15 +679,14 @@ def dummy_data_for_qwen2_vl(
             "--limit-mm-per-prompt.")
 
     hf_config = ctx.get_hf_config(Qwen2VLConfig)
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+
-                      [hf_config.vision_start_token_id])
+    dummy_seqdata = SequenceData.from_token_counts(
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (hf_config.vision_start_token_id, 1),
-                       [hf_config.image_token_id]) * max_llm_image_tokens
+        (hf_config.image_token_id, max_llm_image_tokens),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (hf_config.vision_end_token_id, 1),
-                       [hf_config.vision_end_token_id])
+        (0, seq_len - max_llm_image_tokens - 2),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    )
-                       [0]) * (seq_len - max_llm_image_tokens - 2)
+
-    dummy_seqdata = SequenceData(token_ids)
     dummy_image = Image.new("RGB", (max_resized_width, max_resized_height),
                             color=0)
 

vllm/model_executor/models/siglip.py

@@ -2,7 +2,6 @@
 within a vision language model."""
 
 import math
-from array import array
 from typing import Iterable, List, Optional, Tuple, Union
 
 import torch
@@ -24,7 +23,7 @@ from vllm.model_executor.layers.vocab_parallel_embedding import (
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
 from vllm.multimodal.utils import (cached_get_tokenizer,
                                    repeat_and_pad_placeholder_tokens)
-from vllm.sequence import VLLM_TOKEN_ID_ARRAY_TYPE, SequenceData
+from vllm.sequence import SequenceData
 
 try:
     from xformers import ops as xops
@@ -67,11 +66,10 @@ def dummy_seq_data_for_siglip(
     else:
         image_feature_size = image_feature_size_override
 
-    token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
+    return SequenceData.from_token_counts(
-                      [image_token_id]) * image_feature_size
+        (image_token_id, image_feature_size * num_images),
-    token_ids += array(VLLM_TOKEN_ID_ARRAY_TYPE,
+        (0, seq_len - image_feature_size * num_images),
-                       [0]) * (seq_len - image_feature_size)
+    )
-    return SequenceData(token_ids)
 
 
 def dummy_image_for_siglip(

vllm/model_executor/models/ultravox.py

@@ -77,15 +77,11 @@ def get_ultravox_max_audio_tokens(ctx: InputContext):
     return math.ceil(feature_extractor.chunk_length * _AUDIO_TOKENS_PER_SECOND)
 
 
-def dummy_data_for_ultravox(
+def dummy_seq_data_for_ultravox(
     ctx: InputContext,
     seq_len: int,
-    mm_counts: Mapping[str, int],
+    audio_count: int,
 ):
-    feature_extractor = whisper_feature_extractor(ctx)
-
-    audio_count = mm_counts["audio"]
-
     audio_placeholder = array(
         VLLM_TOKEN_ID_ARRAY_TYPE,
         [_AUDIO_PLACEHOLDER_TOKEN]) * get_ultravox_max_audio_tokens(ctx)
@@ -96,10 +92,28 @@ def dummy_data_for_ultravox(
     other_token_ids = array(VLLM_TOKEN_ID_ARRAY_TYPE,
                             [0]) * (seq_len - len(audio_token_ids))
 
-    audio_and_sr = (np.array([0.0] * feature_extractor.chunk_length), 1)
+    return SequenceData(audio_token_ids + other_token_ids)
-    mm_dict = {"audio": [audio_and_sr] * audio_count}
 
-    return (SequenceData(audio_token_ids + other_token_ids), mm_dict)
+
+def dummy_audio_for_ultravox(
+    ctx: InputContext,
+    audio_count: int,
+):
+    feature_extractor = whisper_feature_extractor(ctx)
+    audio_and_sr = (np.array([0.0] * feature_extractor.chunk_length), 1)
+    return {"audio": [audio_and_sr] * audio_count}
+
+
+def dummy_data_for_ultravox(
+    ctx: InputContext,
+    seq_len: int,
+    mm_counts: Mapping[str, int],
+):
+    audio_count = mm_counts["audio"]
+    seq_data = dummy_seq_data_for_ultravox(ctx, seq_len, audio_count)
+    mm_dict = dummy_audio_for_ultravox(ctx, audio_count)
+
+    return (seq_data, mm_dict)
 
 
 def input_mapper_for_ultravox(ctx: InputContext, data: object):

vllm/sequence.py

@@ -171,13 +171,13 @@ class SequenceData(msgspec.Struct,
     _mrope_position_delta: Optional[int] = None
 
     @staticmethod
-    def from_counts(counts_by_token: Mapping[int, int]) -> "SequenceData":
+    def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
-        if len(counts_by_token) == 0:
+        if len(token_counts) == 0:
             return SequenceData.from_seqs([])
 
         arrs = [
             array(VLLM_TOKEN_ID_ARRAY_TYPE, [token_id]) * count
-            for token_id, count in counts_by_token.items()
+            for token_id, count in token_counts
         ]
 
         return SequenceData(reduce(array.__add__, arrs))