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 8, 05:00 UTC
[ROCm][CI] Fix plugins test group; updating terratorch and dependencies (#34589)
Signed-off-by: Andreas Karatzas <akaratza@amd.com>
Andreas Karatzas committed
Feb 16, 2026 at 09:33 UTC
03a8770a6d9ca4fcb7ff24fa5e6c75c25662919c
2 files changed
+7
-3
requirements/rocm-test.txt
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-2
@@ -67,8 +67,6 @@ multiprocess==0.70.16
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# Required for v1/metrics/test_engine_logger_apis.py
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ray[cgraph,default]>=2.48.0
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-# Plugins test
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-terratorch @ git+https://github.com/IBM/terratorch.git@07184fcf91a1324f831ff521dd238d97fe350e3e
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torchgeo==0.7.0
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# via terratorch
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# MTEB Benchmark Test
@@ -98,3 +96,9 @@ transformers==4.57.3
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huggingface-hub==0.36.2
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# Pin Mistral Common
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mistral-common[image,audio]==1.9.1
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+# Required for Prithvi tests
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+terratorch==1.2.2
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+# Required for Prithvi tests
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+segmentation-models-pytorch==0.5.0
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+# Required for Prithvi tests
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+imagehash==4.3.2
vllm/model_executor/models/terratorch.py
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@@ -209,7 +209,7 @@ class TerratorchMultiModalProcessor(BaseMultiModalProcessor[TerratorchProcessing
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_, passthrough_data = self._get_hf_mm_data(mm_items)
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mm_processed_data = BatchFeature(
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- {k: torch.tensor(v).unsqueeze(0) for k, v in passthrough_data.items()},
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+ {k: torch.as_tensor(v).unsqueeze(0) for k, v in passthrough_data.items()},
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tensor_type="pt",
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)
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mm_placeholders = {"image": [PlaceholderRange(offset=0, length=0)]}