A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 14, 05:30 UTC
Update to Transformers `v4.56.2` (#24638)
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk> Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com> Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Harry Mellor committed
Oct 1, 2025 at 06:07 UTC
2a69ab489917915da191b6d200c5ed17a667c6c3
5 files changed
+20
-34
requirements/nightly_torch_test.txt
+2
-2
@@ -29,8 +29,8 @@ opencv-python-headless >= 4.11.0 # required for video test
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datamodel_code_generator # required for minicpm3 test
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lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test
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mteb>=1.38.11, <2 # required for mteb test
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-transformers==4.52.4
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-tokenizers==0.21.1
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+transformers==4.56.2
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+tokenizers==0.22.0
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schemathesis>=3.39.15 # Required for openai schema test.
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# quantization
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bitsandbytes>=0.46.1
requirements/test.in
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-2
@@ -37,8 +37,8 @@ datamodel_code_generator # required for minicpm3 test
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# TODO: Use lm-eval[api]==0.4.10 once released
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lm-eval[api] @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d # required for model evaluation test
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mteb[bm25s]>=1.38.11, <2 # required for mteb test
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-transformers==4.55.2
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-tokenizers==0.21.1
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+transformers==4.56.2
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+tokenizers==0.22.0
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schemathesis>=3.39.15 # Required for openai schema test.
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# quantization
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bitsandbytes==0.46.1
requirements/test.txt
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-2
@@ -1072,7 +1072,7 @@ timm==1.0.17
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# segmentation-models-pytorch
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# terratorch
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# torchgeo
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-tokenizers==0.21.1
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+tokenizers==0.22.0
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# via
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# -r requirements/test.in
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# transformers
@@ -1153,7 +1153,7 @@ tqdm==4.66.6
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# transformers
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tqdm-multiprocess==0.0.11
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# via lm-eval
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-transformers==4.55.2
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+transformers==4.56.2
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# via
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# -r requirements/test.in
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# genai-perf
tests/models/multimodal/generation/test_common.py
+3
-1
@@ -214,7 +214,9 @@ VLM_TEST_SETTINGS = {
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vllm_runner_kwargs={
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"model_impl": "transformers",
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},
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- marks=[large_gpu_mark(min_gb=32)],
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+ # FIXME: Investigate mrope issue
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+ marks=[large_gpu_mark(min_gb=32),
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+ pytest.mark.skip(reason="Mrope issue")],
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),
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#### Extended model tests
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"aria": VLMTestInfo(
vllm/model_executor/models/transformers.py
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-27
@@ -51,7 +51,6 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
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BaseProcessingInfo)
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from vllm.multimodal.profiling import BaseDummyInputsBuilder
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from vllm.sequence import IntermediateTensors
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-from vllm.utils import is_list_of
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from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
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SupportsMultiModal, SupportsPP, SupportsQuant)
@@ -217,9 +216,6 @@ def init_on_device_without_buffers(device: torch.device):
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class MultiModalProcessingInfo(BaseProcessingInfo):
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- def get_hf_config(self):
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- return self.ctx.model_config.hf_config
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-
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def get_supported_mm_limits(self):
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return {"image": None}
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@@ -784,6 +780,7 @@ def flatten_and_concat(x: list[torch.Tensor]) -> torch.Tensor:
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},
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enable_if=can_enable_torch_compile)
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class TransformersForMultimodalLM(TransformersForCausalLM, SupportsMultiModal):
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+ merge_by_field_config = True
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# Backwards compatibility for prev released models. State dicts back then
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# had different formats and cannot be loaded with `AutoModel` mapping as is
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hf_to_vllm_mapper = WeightsMapper(
@@ -828,40 +825,27 @@ class TransformersForMultimodalLM(TransformersForCausalLM, SupportsMultiModal):
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return self.model
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def get_multimodal_embeddings(self, **kwargs):
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- pixel_values = kwargs.pop("pixel_values", None)
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- pixel_values = pixel_values if pixel_values is not None else kwargs.pop(
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- "image_patches", None)
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- image_embeds = kwargs.pop("image_embeds", None)
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+ pixel_values: Optional[torch.Tensor] = kwargs.pop("pixel_values", None)
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+ image_embeds: Optional[torch.Tensor] = kwargs.pop("image_embeds", None)
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+ # Model might use `image_patches` instead of `pixel_values`
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+ if pixel_values is None:
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+ pixel_values = kwargs.pop("image_patches", None)
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if image_embeds is not None:
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return image_embeds
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- if pixel_values is None and image_embeds is None:
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+ if pixel_values is None:
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return None
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num_image_patches = kwargs.pop("num_image_patches")
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if pixel_values is not None:
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- if isinstance(pixel_values, torch.Tensor):
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- pixel_values = flatten_bn(pixel_values).to(self.dtype)
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- elif is_list_of(pixel_values, torch.Tensor):
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- pixel_values = flatten_and_concat(pixel_values).to(self.dtype)
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- else:
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- raise ValueError(
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- f"Unsupported pixel_values type {type(pixel_values)}. "
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- "Expected `torch.Tensor` or list of `torch.Tensor`.")
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-
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- if isinstance(num_image_patches, list):
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- num_image_patches = torch.cat(num_image_patches)
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-
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vision_embeddings = self.model.get_image_features(
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- pixel_values,
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- **{
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- k: v.flatten(0, 1)
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- for k, v in kwargs.items()
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- },
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- )
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+ pixel_values, **kwargs)
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if isinstance(vision_embeddings, torch.Tensor):
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+ if isinstance(num_image_patches, list):
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+ num_image_patches = torch.cat(num_image_patches)
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+
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if vision_embeddings.ndim == 2:
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vision_embeddings = vision_embeddings.unsqueeze(0)
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