Rename from_counts to from_token_counts

Author: DarkLight1337

vllm/inputs/registry.py

@@ -125,7 +125,7 @@ def _default_dummy_data_factory(
         # 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

@@ -62,7 +62,7 @@ def dummy_seq_data_for_blip(
     else:
         image_feature_size = image_feature_size_override
 
-    return SequenceData.from_counts(
+    return SequenceData.from_token_counts(
         (image_token_id, image_feature_size * num_images),
         (0, seq_len - image_feature_size * num_images),
     )

vllm/model_executor/models/blip2.py

@@ -427,7 +427,7 @@ def dummy_seq_data_for_blip2(
     else:
         image_feature_size = image_feature_size_override
 
-    return SequenceData.from_counts(
+    return SequenceData.from_token_counts(
         (image_token_id, image_feature_size * num_images),
         (0, seq_len - image_feature_size * num_images),
     )

vllm/model_executor/models/chameleon.py

@@ -70,7 +70,7 @@ def dummy_seq_data_for_chameleon(
     else:
         image_feature_size = image_feature_size_override
 
-    return SequenceData.from_counts(
+    return SequenceData.from_token_counts(
         (image_token_id, image_feature_size * num_images),
         (0, seq_len - image_feature_size * num_images),
     )

vllm/model_executor/models/clip.py

@@ -61,7 +61,7 @@ def dummy_seq_data_for_clip(
     else:
         image_feature_size = image_feature_size_override
 
-    return SequenceData.from_counts(
+    return SequenceData.from_token_counts(
         (image_token_id, image_feature_size * num_images),
         (0, seq_len - image_feature_size * num_images),
     )

vllm/model_executor/models/minicpmv.py

@@ -257,7 +257,7 @@ def get_max_minicpmv_image_tokens(ctx: InputContext):
 
 
 def dummy_seq_data_for_minicpmv(seq_len: int, num_images: int):
-    return SequenceData.from_counts((0, seq_len))
+    return SequenceData.from_token_counts((0, seq_len))
 
 
 def dummy_image_for_minicpmv(hf_config: PretrainedConfig, num_images: int):

vllm/model_executor/models/pixtral.py

@@ -61,7 +61,7 @@ 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_counts(
+    seq_data = SequenceData.from_token_counts(
         (image_token_id, num_image_tokens),
         (0, seq_len - num_image_tokens),
     )

vllm/model_executor/models/qwen.py

@@ -817,7 +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.from_counts((0, seq_len))
+        seq_data = SequenceData.from_token_counts((0, seq_len))
         mm_data = None
         return seq_data, mm_data
 

vllm/model_executor/models/qwen2_vl.py

@@ -680,7 +680,7 @@ def dummy_data_for_qwen2_vl(
 
     hf_config = ctx.get_hf_config(Qwen2VLConfig)
 
-    dummy_seqdata = SequenceData.from_counts(
+    dummy_seqdata = SequenceData.from_token_counts(
         (hf_config.vision_start_token_id, 1),
         (hf_config.image_token_id, max_llm_image_tokens),
         (hf_config.vision_end_token_id, 1),

vllm/model_executor/models/siglip.py

@@ -66,7 +66,7 @@ def dummy_seq_data_for_siglip(
     else:
         image_feature_size = image_feature_size_override
 
-    return SequenceData.from_counts(
+    return SequenceData.from_token_counts(
         (image_token_id, image_feature_size * num_images),
         (0, seq_len - image_feature_size * num_images),
     )

vllm/sequence.py

@@ -171,7 +171,7 @@ class SequenceData(msgspec.Struct,
     _mrope_position_delta: Optional[int] = None
 
     @staticmethod
-    def from_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
+    def from_token_counts(*token_counts: Tuple[int, int]) -> "SequenceData":
         if len(token_counts) == 0:
             return SequenceData.from_seqs([])