[Model] Add LoRA support for Whisper models

tests/lora/conftest.py

@@ -289,6 +289,11 @@ def llama32_lora_files(llama32_lora_huggingface_id):
     return snapshot_download(repo_id=llama32_lora_huggingface_id)
 
 
+@pytest.fixture(scope="session")
+def whisper_lora_files():
+    return snapshot_download(repo_id="chengyili2005/whisper-small-mandarin-lora")
+
+
 @pytest.fixture
 def reset_default_device():
     """

tests/lora/test_whisper.py

@@ -0,0 +1,153 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Integration tests for Whisper models with LoRA adapters.
+
+These tests verify that Whisper models can correctly load and use LoRA adapters
+for speech-to-text transcription tasks.
+"""
+
+import pytest
+
+import vllm
+from vllm.assets.audio import AudioAsset
+from vllm.lora.request import LoRARequest
+
+from ..utils import create_new_process_for_each_test
+
+# Model configuration
+WHISPER_MODEL = "openai/whisper-small"
+
+# Test prompts for Whisper transcription
+WHISPER_PROMPT = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
+
+# Note: whisper_lora_files fixture is defined in conftest.py
+
+
+@pytest.fixture(autouse=True)
+def use_spawn_for_whisper(monkeypatch):
+    """Whisper has issues with forked workers, use spawn instead."""
+    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+
+
+def create_whisper_llm(enable_lora: bool = True, max_loras: int = 2):
+    """Create a Whisper LLM instance with optional LoRA support."""
+    return vllm.LLM(
+        model=WHISPER_MODEL,
+        enable_lora=enable_lora,
+        max_loras=max_loras if enable_lora else 1,
+        max_lora_rank=64,
+        max_model_len=448,
+        dtype="half",
+        enforce_eager=True,  # For stability in tests
+    )
+
+
+def run_whisper_inference(
+    llm: vllm.LLM,
+    lora_path: str | None = None,
+    lora_id: int = 1,
+) -> list[str]:
+    """Run Whisper inference with optional LoRA adapter."""
+    # Load test audio
+    audio_asset = AudioAsset("mary_had_lamb")
+    audio_data = audio_asset.audio_and_sample_rate
+
+    inputs = [
+        {
+            "prompt": WHISPER_PROMPT,
+            "multi_modal_data": {"audio": audio_data},
+        }
+    ]
+
+    sampling_params = vllm.SamplingParams(
+        temperature=0,
+        max_tokens=200,
+    )
+
+    # Prepare LoRA request if adapter path is provided
+    lora_request = None
+    if lora_path:
+        lora_request = LoRARequest(
+            lora_name=f"whisper_lora_{lora_id}",
+            lora_int_id=lora_id,
+            lora_path=lora_path,
+        )
+
+    outputs = llm.generate(inputs, sampling_params, lora_request=lora_request)
+
+    return [output.outputs[0].text for output in outputs]
+
+
+@create_new_process_for_each_test()
+def test_whisper_lora_inference(whisper_lora_files):
+    """Test basic Whisper inference with a LoRA adapter.
+
+    This test verifies that:
+    1. Whisper model can be loaded with LoRA support enabled
+    2. A LoRA adapter can be applied during inference
+    3. The model produces valid transcription output
+    """
+    llm = create_whisper_llm(enable_lora=True)
+
+    # Run inference with LoRA
+    outputs = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=1)
+
+    # Verify we got a non-empty transcription
+    assert len(outputs) == 1
+    assert len(outputs[0]) > 0, "Expected non-empty transcription output"
+
+    # The output should contain some recognizable words from the audio
+    # (Mary had a little lamb)
+    print(f"Transcription output: {outputs[0]}")
+
+
+@create_new_process_for_each_test()
+def test_whisper_multi_lora(whisper_lora_files):
+    """Test Whisper with multiple LoRA adapter IDs.
+
+    This test verifies that the same LoRA adapter can be loaded with
+    different IDs and produce consistent results.
+    """
+    llm = create_whisper_llm(enable_lora=True, max_loras=4)
+
+    # Test with different LoRA IDs using the same adapter
+    outputs_lora1 = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=1)
+    outputs_lora2 = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=2)
+
+    # Both should produce valid outputs
+    assert len(outputs_lora1[0]) > 0
+    assert len(outputs_lora2[0]) > 0
+
+    # Same adapter with different IDs should produce same output
+    assert outputs_lora1 == outputs_lora2, (
+        f"Expected same outputs for same adapter with different IDs. "
+        f"Got: {outputs_lora1} vs {outputs_lora2}"
+    )
+
+
+@create_new_process_for_each_test()
+def test_whisper_with_and_without_lora(whisper_lora_files):
+    """Test that Whisper produces different outputs with and without LoRA.
+
+    This test verifies that the LoRA adapter actually affects the model output.
+    """
+    llm = create_whisper_llm(enable_lora=True)
+
+    # Run with LoRA
+    outputs_with_lora = run_whisper_inference(
+        llm, lora_path=whisper_lora_files, lora_id=1
+    )
+
+    # Run without LoRA (base model only)
+    outputs_without_lora = run_whisper_inference(llm, lora_path=None)
+
+    # Both should produce valid outputs
+    assert len(outputs_with_lora[0]) > 0
+    assert len(outputs_without_lora[0]) > 0
+
+    print(f"Output with LoRA: {outputs_with_lora[0]}")
+    print(f"Output without LoRA: {outputs_without_lora[0]}")
+
+    # Note: Outputs may or may not differ depending on the adapter
+    # The main verification is that both configurations work

vllm/lora/worker_manager.py

@@ -49,7 +49,18 @@ def __init__(
         # Use get_text_config() in case of multimodal models
         text_config = vllm_config.model_config.hf_config.get_text_config()
 
-        self.max_position_embeddings = text_config.max_position_embeddings
+        # For encoder-decoder models (e.g., Whisper), use max_target_positions
+        # instead of max_position_embeddings
+        # TODO: Generalize max_position_embeddings handling for
+        # out-of-tree (OOT) encoder-decoder models
+        if vllm_config.model_config.is_encoder_decoder:
+            self.max_position_embeddings = getattr(
+                text_config, "max_target_positions", None
+            )
+        else:
+            self.max_position_embeddings = getattr(
+                text_config, "max_position_embeddings", None
+            )
         self.device = device
         # Lazily initialized by create_lora_manager.
         self._adapter_manager: LoRAModelManager

vllm/model_executor/models/whisper.py

@@ -31,6 +31,7 @@
 )
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
+    MergedColumnParallelLinear,
     QKVParallelLinear,
     RowParallelLinear,
 )
@@ -66,6 +67,7 @@
 
 from .interfaces import (
     MultiModalEmbeddings,
+    SupportsLoRA,
     SupportsMultiModal,
     SupportsTranscription,
 )
@@ -279,11 +281,12 @@ def _init_qkv(
             quant_config=quant_config,
             prefix=f"{prefix}.q_proj",
         )
-        self.kv_proj = QKVParallelLinear(
-            hidden_size=embed_dim,
-            head_size=self.head_dim,
-            total_num_heads=0,
-            total_num_kv_heads=self.total_num_heads,
+        # Use MergedColumnParallelLinear for K and V projections.
+        # This enables LoRA support via MergedColumnParallelLinearWithLoRA
+        # which handles 2-slice configurations.
+        self.kv_proj = MergedColumnParallelLinear(
+            input_size=embed_dim,
+            output_sizes=[embed_dim, embed_dim],
             bias=bias,
             quant_config=quant_config,
             prefix=f"{prefix}.kv_proj",
@@ -615,8 +618,9 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
             (".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
             (".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
             (".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
-            (".encoder_attn.kv_proj", ".encoder_attn.k_proj", "k"),
-            (".encoder_attn.kv_proj", ".encoder_attn.v_proj", "v"),
+            # MergedColumnParallelLinear uses integer indices (0, 1)
+            (".encoder_attn.kv_proj", ".encoder_attn.k_proj", 0),
+            (".encoder_attn.kv_proj", ".encoder_attn.v_proj", 1),
         ]
         params_dict = dict(self.named_parameters())
         loaded_params: set[str] = set()
@@ -790,14 +794,12 @@ class WhisperForConditionalGeneration(
     nn.Module,
     SupportsTranscription,
     SupportsMultiModal,
+    SupportsLoRA,
 ):
+    # LoRA-specific attributes
     packed_modules_mapping = {
-        "self_attn.qkv_proj": [
-            "self_attn.q_proj",
-            "self_attn.k_proj",
-            "self_attn.v_proj",
-        ],
-        "encoder_attn.kv_proj": ["encoder_attn.k_proj", "encoder_attn.v_proj"],
+        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
+        "kv_proj": ["k_proj", "v_proj"],
     }
 
     hf_to_vllm_mapper = WeightsMapper(