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 7, 05:15 UTC
[Model] Add LoRA support for Whisper models (#29856)
Signed-off-by: daje0601 <englishmt4118@gmail.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
daje0601 committed
Mar 5, 2026 at 11:38 UTC
3b23d57c960c77edbc31f9bcae9dcb69a491fd19
4 files changed
+185
-14
tests/lora/conftest.py
+5
@@ -289,6 +289,11 @@ def llama32_lora_files(llama32_lora_huggingface_id):
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return snapshot_download(repo_id=llama32_lora_huggingface_id)
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+@pytest.fixture(scope="session")
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+def whisper_lora_files():
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+ return snapshot_download(repo_id="chengyili2005/whisper-small-mandarin-lora")
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+
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+
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@pytest.fixture
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def reset_default_device():
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"""
tests/lora/test_whisper.py
new
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@@ -0,0 +1,153 @@
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+# SPDX-License-Identifier: Apache-2.0
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+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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+"""
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+Integration tests for Whisper models with LoRA adapters.
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+
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+These tests verify that Whisper models can correctly load and use LoRA adapters
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+for speech-to-text transcription tasks.
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+"""
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+
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+import pytest
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+
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+import vllm
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+from vllm.assets.audio import AudioAsset
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+from vllm.lora.request import LoRARequest
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+
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+from ..utils import create_new_process_for_each_test
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+
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+# Model configuration
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+WHISPER_MODEL = "openai/whisper-small"
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+
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+# Test prompts for Whisper transcription
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+WHISPER_PROMPT = "<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"
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+
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+# Note: whisper_lora_files fixture is defined in conftest.py
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+
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+
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+@pytest.fixture(autouse=True)
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+def use_spawn_for_whisper(monkeypatch):
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+ """Whisper has issues with forked workers, use spawn instead."""
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+ monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
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+
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+
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+def create_whisper_llm(enable_lora: bool = True, max_loras: int = 2):
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+ """Create a Whisper LLM instance with optional LoRA support."""
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+ return vllm.LLM(
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+ model=WHISPER_MODEL,
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+ enable_lora=enable_lora,
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+ max_loras=max_loras if enable_lora else 1,
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+ max_lora_rank=64,
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+ max_model_len=448,
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+ dtype="half",
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+ enforce_eager=True, # For stability in tests
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+ )
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+
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+
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+def run_whisper_inference(
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+ llm: vllm.LLM,
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+ lora_path: str | None = None,
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+ lora_id: int = 1,
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+) -> list[str]:
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+ """Run Whisper inference with optional LoRA adapter."""
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+ # Load test audio
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+ audio_asset = AudioAsset("mary_had_lamb")
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+ audio_data = audio_asset.audio_and_sample_rate
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+
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+ inputs = [
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+ {
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+ "prompt": WHISPER_PROMPT,
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+ "multi_modal_data": {"audio": audio_data},
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+ }
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+ ]
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+
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+ sampling_params = vllm.SamplingParams(
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+ temperature=0,
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+ max_tokens=200,
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+ )
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+
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+ # Prepare LoRA request if adapter path is provided
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+ lora_request = None
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+ if lora_path:
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+ lora_request = LoRARequest(
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+ lora_name=f"whisper_lora_{lora_id}",
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+ lora_int_id=lora_id,
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+ lora_path=lora_path,
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+ )
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+
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+ outputs = llm.generate(inputs, sampling_params, lora_request=lora_request)
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+
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+ return [output.outputs[0].text for output in outputs]
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+
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+
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+@create_new_process_for_each_test()
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+def test_whisper_lora_inference(whisper_lora_files):
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+ """Test basic Whisper inference with a LoRA adapter.
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+
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+ This test verifies that:
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+ 1. Whisper model can be loaded with LoRA support enabled
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+ 2. A LoRA adapter can be applied during inference
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+ 3. The model produces valid transcription output
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+ """
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+ llm = create_whisper_llm(enable_lora=True)
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+
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+ # Run inference with LoRA
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+ outputs = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=1)
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+
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+ # Verify we got a non-empty transcription
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+ assert len(outputs) == 1
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+ assert len(outputs[0]) > 0, "Expected non-empty transcription output"
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+
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+ # The output should contain some recognizable words from the audio
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+ # (Mary had a little lamb)
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+ print(f"Transcription output: {outputs[0]}")
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+
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+
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+@create_new_process_for_each_test()
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+def test_whisper_multi_lora(whisper_lora_files):
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+ """Test Whisper with multiple LoRA adapter IDs.
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+
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+ This test verifies that the same LoRA adapter can be loaded with
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+ different IDs and produce consistent results.
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+ """
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+ llm = create_whisper_llm(enable_lora=True, max_loras=4)
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+
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+ # Test with different LoRA IDs using the same adapter
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+ outputs_lora1 = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=1)
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+ outputs_lora2 = run_whisper_inference(llm, lora_path=whisper_lora_files, lora_id=2)
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+
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+ # Both should produce valid outputs
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+ assert len(outputs_lora1[0]) > 0
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+ assert len(outputs_lora2[0]) > 0
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+
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+ # Same adapter with different IDs should produce same output
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+ assert outputs_lora1 == outputs_lora2, (
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+ f"Expected same outputs for same adapter with different IDs. "
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+ f"Got: {outputs_lora1} vs {outputs_lora2}"
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+ )
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+
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+
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+@create_new_process_for_each_test()
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+def test_whisper_with_and_without_lora(whisper_lora_files):
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+ """Test that Whisper produces different outputs with and without LoRA.
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+
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+ This test verifies that the LoRA adapter actually affects the model output.
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+ """
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+ llm = create_whisper_llm(enable_lora=True)
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+
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+ # Run with LoRA
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+ outputs_with_lora = run_whisper_inference(
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+ llm, lora_path=whisper_lora_files, lora_id=1
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+ )
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+
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+ # Run without LoRA (base model only)
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+ outputs_without_lora = run_whisper_inference(llm, lora_path=None)
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+
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+ # Both should produce valid outputs
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+ assert len(outputs_with_lora[0]) > 0
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+ assert len(outputs_without_lora[0]) > 0
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+
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+ print(f"Output with LoRA: {outputs_with_lora[0]}")
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+ print(f"Output without LoRA: {outputs_without_lora[0]}")
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+
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+ # Note: Outputs may or may not differ depending on the adapter
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+ # The main verification is that both configurations work
vllm/lora/worker_manager.py
+12
-1
@@ -49,7 +49,18 @@ class WorkerLoRAManager:
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# Use get_text_config() in case of multimodal models
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text_config = vllm_config.model_config.hf_config.get_text_config()
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- self.max_position_embeddings = text_config.max_position_embeddings
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+ # For encoder-decoder models (e.g., Whisper), use max_target_positions
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+ # instead of max_position_embeddings
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+ # TODO: Generalize max_position_embeddings handling for
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+ # out-of-tree (OOT) encoder-decoder models
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+ if vllm_config.model_config.is_encoder_decoder:
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+ self.max_position_embeddings = getattr(
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+ text_config, "max_target_positions", None
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+ )
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+ else:
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+ self.max_position_embeddings = getattr(
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+ text_config, "max_position_embeddings", None
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+ )
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self.device = device
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# Lazily initialized by create_lora_manager.
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self._adapter_manager: LoRAModelManager
vllm/model_executor/models/whisper.py
+15
-13
@@ -31,6 +31,7 @@ from vllm.model_executor.layers.attention import (
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)
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from vllm.model_executor.layers.linear import (
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ColumnParallelLinear,
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+ MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
@@ -66,6 +67,7 @@ from vllm.v1.attention.backend import (
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from .interfaces import (
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MultiModalEmbeddings,
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+ SupportsLoRA,
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SupportsMultiModal,
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SupportsTranscription,
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)
@@ -279,11 +281,12 @@ class WhisperCrossAttention(WhisperAttention):
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj",
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)
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- self.kv_proj = QKVParallelLinear(
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- hidden_size=embed_dim,
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- head_size=self.head_dim,
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- total_num_heads=0,
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- total_num_kv_heads=self.total_num_heads,
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+ # Use MergedColumnParallelLinear for K and V projections.
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+ # This enables LoRA support via MergedColumnParallelLinearWithLoRA
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+ # which handles 2-slice configurations.
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+ self.kv_proj = MergedColumnParallelLinear(
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+ input_size=embed_dim,
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+ output_sizes=[embed_dim, embed_dim],
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.kv_proj",
@@ -615,8 +618,9 @@ class WhisperModel(nn.Module):
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(".self_attn.qkv_proj", ".self_attn.q_proj", "q"),
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(".self_attn.qkv_proj", ".self_attn.k_proj", "k"),
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(".self_attn.qkv_proj", ".self_attn.v_proj", "v"),
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- (".encoder_attn.kv_proj", ".encoder_attn.k_proj", "k"),
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- (".encoder_attn.kv_proj", ".encoder_attn.v_proj", "v"),
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+ # MergedColumnParallelLinear uses integer indices (0, 1)
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+ (".encoder_attn.kv_proj", ".encoder_attn.k_proj", 0),
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+ (".encoder_attn.kv_proj", ".encoder_attn.v_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
@@ -790,14 +794,12 @@ class WhisperForConditionalGeneration(
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nn.Module,
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SupportsTranscription,
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SupportsMultiModal,
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+ SupportsLoRA,
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):
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+ # LoRA-specific attributes
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packed_modules_mapping = {
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- "self_attn.qkv_proj": [
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- "self_attn.q_proj",
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- "self_attn.k_proj",
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- "self_attn.v_proj",
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- ],
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- "encoder_attn.kv_proj": ["encoder_attn.k_proj", "encoder_attn.v_proj"],
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+ "qkv_proj": ["q_proj", "k_proj", "v_proj"],
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+ "kv_proj": ["k_proj", "v_proj"],
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}
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hf_to_vllm_mapper = WeightsMapper(