rebase: adapt to vLLM v0.29 APIs

Signed-off-by: tzhouam <tzhouam@connect.ust.hk>

tests/model_executor/models/minicpmo_4_5/test_long_audio_processing.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
 """Regression tests for MiniCPM-o 4.5 >30s audio preprocessing.
 
 Covers:
@@ -99,10 +99,10 @@ def _make_processor(
     processor.info = SimpleNamespace(audio_pattern="(<audio>./</audio>)")
     processor.data_parser = MiniCPMOMultiModalDataParser(target_sr=_SAMPLE_RATE)
 
-    def fake_base_call_hf_processor(prompts, mm_data, mm_kwargs, tok_kwargs, *, out_keys):
+    def fake_call_hf_processor_on_prompts(prompts, mm_data, mm_kwargs, *, out_keys):
         return {key: fake_hf_outputs[key] for key in out_keys}
 
-    processor._base_call_hf_processor = fake_base_call_hf_processor
+    processor._call_hf_processor_on_prompts = fake_call_hf_processor_on_prompts
     return processor
 
 
@@ -120,7 +120,7 @@ class TestProcessAudiosUnpadding:
         }
         processor = _make_processor(fake_outputs)
 
-        result = processor.process_audios({"audios": [_audio_seconds(45)]}, {}, {})
+        result = processor.process_audios({"audios": [_audio_seconds(45)]}, {})
 
         features = result["audio_features"]
         assert [feat.shape[-1] for feat in features] == chunk_lens
@@ -138,7 +138,7 @@ class TestProcessAudiosUnpadding:
         }
         processor = _make_processor(fake_outputs)
 
-        result = processor.process_audios({"audios": [_audio_seconds(42), _audio_seconds(38)]}, {}, {})
+        result = processor.process_audios({"audios": [_audio_seconds(42), _audio_seconds(38)]}, {})
 
         widths = [feat.shape[-1] for feat in result["audio_features"]]
         assert widths == [_CHUNK_FRAMES, 1200, _CHUNK_FRAMES, 800]
@@ -151,7 +151,7 @@ class TestProcessAudiosUnpadding:
         }
         processor = _make_processor(fake_outputs)
 
-        result = processor.process_audios({"audios": [_audio_seconds(9.8)]}, {}, {})
+        result = processor.process_audios({"audios": [_audio_seconds(9.8)]}, {})
 
         features = result["audio_features"]
         assert len(features) == 1

tests/model_executor/models/qwen2_5_omni/test_per_video_audio_mask.py

@@ -266,8 +266,6 @@ def test_cached_apply_hf_processor_expands_pair_miss_for_audio_hit_video_miss():
     inputs = MagicMock()
     inputs.mm_data_items = mm_data_items
     inputs.hf_processor_mm_kwargs = {"use_audio_in_video": True}
-    inputs.tokenization_kwargs = {}
-    inputs.prompt = [1, 2, 3]
     inputs.get_mm_hashes = MagicMock(return_value={"video": ["v0"], "audio": ["a0"]})
 
     timing_ctx = MagicMock()
@@ -307,7 +305,7 @@ def test_cached_apply_hf_processor_expands_pair_miss_for_audio_hit_video_miss():
     )
     fake_self._paired_cache_keys = Qwen2_5OmniThinkerMultiModalProcessor._paired_cache_keys
     fake_self.info.parse_mm_data = MagicMock(return_value=SimpleNamespace(name="missing"))
-    fake_self._apply_hf_processor_main = MagicMock(return_value=([9], {"video_grid_thw": object()}, False))
+    fake_self._apply_hf_processor_main = MagicMock(return_value={"video_grid_thw": object()})
     fake_self._get_mm_fields_config = MagicMock(return_value={})
     fake_self._get_mm_prompt_updates = MagicMock(return_value={"video": [[object()]], "audio": [[object()]]})
     fake_self._merge_mm_kwargs = capture_merge
@@ -316,13 +314,12 @@ def test_cached_apply_hf_processor_expands_pair_miss_for_audio_hit_video_miss():
         "vllm_omni.model_executor.models.qwen2_5_omni.qwen2_5_omni_thinker.MultiModalKwargsItems.from_hf_inputs",
         return_value={"video": [object()], "audio": [object()]},
     ):
-        prompt_ids, mm_info, _ = Qwen2_5OmniThinkerMultiModalProcessor._cached_apply_hf_processor(
+        mm_info = Qwen2_5OmniThinkerMultiModalProcessor._cached_apply_hf_processor(
             fake_self,
             inputs,
             timing_ctx,
         )
 
-    assert prompt_ids == [9]
     # Both modalities were reprocessed as one unit after pair-miss expansion.
     assert fake_self.info.parse_mm_data.call_args.args[0] == {
         "video": ["video-0"],

tests/model_executor/models/test_weight_loader.py

@@ -0,0 +1,40 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
+
+import torch
+from torch import nn
+from vllm.model_executor.models.utils import WeightsMapper
+
+from vllm_omni.model_executor.models.weight_loader import AutoWeightsLoader
+
+
+class _TinyModel(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.kept = nn.Linear(1, 1, bias=False)
+        self.skipped = nn.Linear(1, 1, bias=False)
+        self.rotary_embed = nn.Module()
+        self.rotary_embed.register_parameter("inv_freq", nn.Parameter(torch.zeros(1)))
+
+
+def test_auto_weights_loader_preserves_removed_skip_arguments() -> None:
+    model = _TinyModel()
+    model.skipped.weight.data.zero_()
+
+    loaded = AutoWeightsLoader(
+        model,
+        skip_prefixes=["skipped."],
+        skip_substrs=["rotary_embed.inv_freq"],
+    ).load_weights(
+        [
+            ("checkpoint.kept.weight", torch.ones(1, 1)),
+            ("skipped.weight", torch.full((1, 1), 2.0)),
+            ("rotary_embed.inv_freq", torch.ones(1)),
+        ],
+        mapper=WeightsMapper(orig_to_new_prefix={"checkpoint.": ""}),
+    )
+
+    assert loaded == {"kept.weight"}
+    torch.testing.assert_close(model.kept.weight, torch.ones(1, 1))
+    torch.testing.assert_close(model.skipped.weight, torch.zeros(1, 1))
+    torch.testing.assert_close(model.rotary_embed.inv_freq, torch.zeros(1))