vllm-project--vllm-omni
wehub-resource-sync
620 行
21 KiB
Python
620 行
21 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from __future__ import annotations
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import pytest
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import torch
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from vllm.lora.lora_weights import LoRALayerWeights
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from vllm.lora.utils import get_supported_lora_modules
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from tests.diffusion.lora.helpers import (
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DummyBaseLayerWithLoRA,
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FakeLinearBase,
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fake_replace_submodule,
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)
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from vllm_omni.diffusion.lora.manager import DiffusionLoRAManager
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from vllm_omni.lora.request import LoRARequest
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pytestmark = [pytest.mark.core_model, pytest.mark.cpu]
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class _DummyLoRALayer:
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def __init__(self, n_slices: int, output_slices: tuple[int, ...]):
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self.n_slices = n_slices
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self.output_slices = output_slices
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self.set_calls: list[
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tuple[list[torch.Tensor | None] | torch.Tensor, list[torch.Tensor | None] | torch.Tensor]
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] = []
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self.reset_calls: int = 0
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def set_lora(self, index: int, lora_a, lora_b):
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assert index == 0
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self.set_calls.append((lora_a, lora_b))
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def reset_lora(self, index: int):
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assert index == 0
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self.reset_calls += 1
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# Aliases for backward compatibility within this file
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_FakeLinearBase = FakeLinearBase
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_DummyBaseLayerWithLoRA = DummyBaseLayerWithLoRA
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class _DummyPipeline(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.transformer = torch.nn.Module()
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self.transformer.foo = _FakeLinearBase()
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class _DummyLM(torch.nn.Module):
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"""LoRA enabled wrapper for _DummyPipeline."""
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def __init__(self, rank: int):
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super().__init__()
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self.transformer = torch.nn.Module()
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self.transformer.foo = _DummyBaseLayerWithLoRA(_FakeLinearBase())
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self.rank = rank
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self.loras = self.get_lora_modules()
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def get_lora_modules(self):
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return {"transformer.foo": self._get_initial_lora(self.rank)}
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def get_lora(self, k: str) -> LoRALayerWeights:
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"""Get the unscaled LoRA weights for transformer.foo"""
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return self.loras[k]
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def _get_initial_lora(self, rank: int) -> LoRALayerWeights:
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"""Initializes a dummy LoRA for the current rank."""
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A = torch.ones((rank, 4))
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B = torch.ones((4, rank))
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return LoRALayerWeights(
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module_name="foo",
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rank=rank,
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lora_alpha=rank,
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lora_a=A,
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lora_b=B,
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)
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def test_lora_manager_supported_modules_are_stable_with_wrapped_layers(monkeypatch):
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# Simulate a pipeline that already contains LoRA wrappers where the original
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# LinearBase is nested under ".base_layer".
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import vllm_omni.diffusion.lora.manager as manager_mod
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monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
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pipeline = _DummyLM(rank=2)
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# vLLM helper would see only the nested LinearBase and yield "base_layer".
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assert get_supported_lora_modules(pipeline) == ["base_layer"]
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manager = DiffusionLoRAManager(
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pipeline=pipeline,
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=1,
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)
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assert "foo" in manager._supported_lora_modules
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assert "base_layer" not in manager._supported_lora_modules
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def test_lora_manager_replace_layers_does_not_rewrap_base_layer(monkeypatch):
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import vllm_omni.diffusion.lora.manager as manager_mod
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monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
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def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
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if isinstance(layer, _FakeLinearBase):
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return _DummyBaseLayerWithLoRA(layer)
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return layer
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replace_calls: list[str] = []
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def _fake_replace_submodule(root: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
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replace_calls.append(module_name)
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setattr(root, module_name, submodule)
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monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
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monkeypatch.setattr(manager_mod, "replace_submodule", _fake_replace_submodule)
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pipeline = torch.nn.Module()
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pipeline.transformer = torch.nn.Module()
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pipeline.transformer.foo = _FakeLinearBase()
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manager = DiffusionLoRAManager(
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pipeline=pipeline,
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=1,
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)
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peft_helper = type("_PH", (), {"r": 1})()
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manager._replace_layers_with_lora(peft_helper)
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manager._replace_layers_with_lora(peft_helper)
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# Only the top-level layer should have been replaced; nested ".base_layer"
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# must be skipped to avoid nesting LoRA wrappers.
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assert replace_calls == ["foo"]
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def test_lora_manager_replaces_packed_layer_when_targeting_sublayers(monkeypatch):
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import vllm_omni.diffusion.lora.manager as manager_mod
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monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
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def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
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return _DummyBaseLayerWithLoRA(layer)
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replace_calls: list[str] = []
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def _fake_replace_submodule(root: torch.nn.Module, module_name: str, submodule: torch.nn.Module):
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replace_calls.append(module_name)
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setattr(root, module_name, submodule)
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monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
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monkeypatch.setattr(manager_mod, "replace_submodule", _fake_replace_submodule)
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pipeline = torch.nn.Module()
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pipeline.stacked_params_mapping = [
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(".to_qkv.", ".to_q.", "q"),
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(".to_qkv.", ".to_k.", "k"),
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(".to_qkv.", ".to_v.", "v"),
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]
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pipeline.transformer = torch.nn.Module()
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pipeline.transformer.to_qkv = _FakeLinearBase()
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manager = DiffusionLoRAManager(
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pipeline=pipeline,
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=1,
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)
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# Treat the dummy layer as a packed 3-slice projection so the manager uses
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# `stacked_params_mapping` to decide replacement based on target_modules.
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monkeypatch.setattr(manager, "_get_packed_modules_list", lambda _module: ["q", "k", "v"])
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peft_helper = type("_PH", (), {"r": 1, "target_modules": ["to_q"]})()
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manager._replace_layers_with_lora(peft_helper)
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assert replace_calls == ["to_qkv"]
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def test_lora_manager_activates_fused_lora_on_packed_layer():
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manager = DiffusionLoRAManager(
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pipeline=torch.nn.Module(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=1,
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)
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packed_layer = _DummyLoRALayer(n_slices=3, output_slices=(2, 1, 1))
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manager._lora_modules = {"transformer.blocks.0.attn.to_qkv": packed_layer}
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rank = 2
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A = torch.ones((rank, 4))
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B = torch.arange(0, sum(packed_layer.output_slices) * rank, dtype=torch.bfloat16).view(-1, rank)
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lora = LoRALayerWeights(
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module_name="transformer.blocks.0.attn.to_qkv",
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rank=rank,
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lora_alpha=rank,
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lora_a=A,
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lora_b=B,
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)
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manager._registered_adapters = {
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7: type(
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"LM",
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(),
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{
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"id": 7,
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"loras": {"transformer.blocks.0.attn.to_qkv": lora},
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"get_lora": lambda self, k: self.loras.get(k),
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},
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)()
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}
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manager._activate_adapter(7, 0.5)
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assert packed_layer.reset_calls == 0
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assert len(packed_layer.set_calls) == 1
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lora_a_list, lora_b_list = packed_layer.set_calls[0]
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assert isinstance(lora_a_list, list)
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assert isinstance(lora_b_list, list)
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assert len(lora_a_list) == 3
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assert len(lora_b_list) == 3
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assert all(torch.allclose(a, A) for a in lora_a_list)
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# B should be split into 3 slices and scaled.
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b0, b1, b2 = lora_b_list
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assert b0.shape[0] == 2 and b1.shape[0] == 1 and b2.shape[0] == 1
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assert torch.allclose(torch.cat([b0, b1, b2], dim=0), B * 0.5)
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def test_lora_manager_activates_packed_lora_from_sublayers():
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pipeline = torch.nn.Module()
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pipeline.stacked_params_mapping = [
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(".to_qkv", ".to_q", "q"),
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(".to_qkv", ".to_k", "k"),
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(".to_qkv", ".to_v", "v"),
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]
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manager = DiffusionLoRAManager(
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pipeline=pipeline,
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=1,
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)
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packed_layer = _DummyLoRALayer(n_slices=3, output_slices=(2, 1, 1))
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manager._lora_modules = {"transformer.blocks.0.attn.to_qkv": packed_layer}
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rank = 2
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loras: dict[str, LoRALayerWeights] = {}
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for name, out_dim in zip(["to_q", "to_k", "to_v"], [2, 1, 1]):
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loras[f"transformer.blocks.0.attn.{name}"] = LoRALayerWeights(
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module_name=f"transformer.blocks.0.attn.{name}",
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rank=rank,
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lora_alpha=rank,
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lora_a=torch.ones((rank, 4)) * (1 if name == "to_q" else 2),
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lora_b=torch.ones((out_dim, rank)) * (3 if name == "to_q" else 4),
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)
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manager._registered_adapters = {
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1: type("LM", (), {"id": 1, "loras": loras, "get_lora": lambda self, k: self.loras.get(k)})()
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}
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manager._activate_adapter(1, scale=2.0)
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assert packed_layer.reset_calls == 0
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assert len(packed_layer.set_calls) == 1
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lora_a_list, lora_b_list = packed_layer.set_calls[0]
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assert isinstance(lora_a_list, list)
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assert isinstance(lora_b_list, list)
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assert len(lora_a_list) == 3
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assert len(lora_b_list) == 3
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# Scale should apply to B only.
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assert torch.allclose(lora_b_list[0], torch.ones((2, rank)) * 3 * 2.0)
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assert torch.allclose(lora_b_list[1], torch.ones((1, rank)) * 4 * 2.0)
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assert torch.allclose(lora_b_list[2], torch.ones((1, rank)) * 4 * 2.0)
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def _dummy_lora_request(adapter_id: int) -> LoRARequest:
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return LoRARequest(
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lora_name=f"adapter_{adapter_id}",
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lora_int_id=adapter_id,
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lora_path=f"/tmp/adapter_{adapter_id}",
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)
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def test_lora_manager_evicts_lru_adapter_when_cache_full(monkeypatch):
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manager = DiffusionLoRAManager(
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pipeline=torch.nn.Module(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=2,
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)
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def _fake_load(_req: LoRARequest):
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lora_model = type("LM", (), {"id": _req.lora_int_id})()
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peft_helper = type("PH", (), {})()
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return lora_model, peft_helper
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monkeypatch.setattr(manager, "_load_adapter", _fake_load)
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monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
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monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id, scale: None)
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req1 = _dummy_lora_request(1)
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req2 = _dummy_lora_request(2)
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req3 = _dummy_lora_request(3)
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manager.set_active_adapter(req1, lora_scale=1.0)
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manager.set_active_adapter(req2, lora_scale=1.0)
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# Touch adapter 1 so adapter 2 becomes LRU.
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manager.set_active_adapter(req1, lora_scale=1.0)
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manager.set_active_adapter(req3, lora_scale=1.0)
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assert set(manager.list_adapters()) == {1, 3}
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def test_lora_manager_does_not_evict_pinned_adapter(monkeypatch):
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manager = DiffusionLoRAManager(
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pipeline=torch.nn.Module(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=2,
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)
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def _fake_load(_req: LoRARequest):
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lora_model = type("LM", (), {"id": _req.lora_int_id})()
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peft_helper = type("PH", (), {})()
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return lora_model, peft_helper
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monkeypatch.setattr(manager, "_load_adapter", _fake_load)
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monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
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monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id, scale: None)
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manager.set_active_adapter(_dummy_lora_request(1), lora_scale=1.0)
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assert manager.pin_adapter(1)
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manager.set_active_adapter(_dummy_lora_request(2), lora_scale=1.0)
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manager.set_active_adapter(_dummy_lora_request(3), lora_scale=1.0)
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assert set(manager.list_adapters()) == {1, 3}
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def test_lora_manager_warns_when_all_adapters_pinned(monkeypatch):
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manager = DiffusionLoRAManager(
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pipeline=torch.nn.Module(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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max_cached_adapters=2,
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)
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def _fake_load(_req: LoRARequest):
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lora_model = type("LM", (), {"id": _req.lora_int_id})()
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peft_helper = type("PH", (), {})()
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return lora_model, peft_helper
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monkeypatch.setattr(manager, "_load_adapter", _fake_load)
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monkeypatch.setattr(manager, "_replace_layers_with_lora", lambda _peft: None)
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monkeypatch.setattr(manager, "_activate_adapter", lambda _adapter_id, scale: None)
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manager.set_active_adapter(_dummy_lora_request(1), lora_scale=1.0)
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manager.set_active_adapter(_dummy_lora_request(2), lora_scale=1.0)
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assert manager.pin_adapter(1)
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assert manager.pin_adapter(2)
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manager.max_cached_adapters = 1
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manager._evict_for_new_adapter()
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assert set(manager.list_adapters()) == {1, 2}
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def test_lora_manager_applies_multiple_scales_correctly(monkeypatch):
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"""Ensure that the LoRA manager applies scales correctly when the
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active adapter receives a different scale, i.e., the rank is unchanged.
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"""
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import vllm_omni.diffusion.lora.manager as manager_mod
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monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyLoRALayer)
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rank = 2
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adapter_id = 7
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req1 = _dummy_lora_request(adapter_id)
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scale_1 = 0.25
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scale_2 = 0.5
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lora_model = _DummyLM(rank=rank)
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manager = DiffusionLoRAManager(
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pipeline=_DummyPipeline(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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)
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def _fake_load(_req: LoRARequest):
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peft_helper = type("PH", (), {"r": rank})()
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return lora_model, peft_helper
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monkeypatch.setattr(manager, "_load_adapter", _fake_load)
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manager._registered_adapters = {
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adapter_id: lora_model,
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}
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manager._lora_modules = {"transformer.foo": lora_model.transformer.foo}
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# After the first scale, all B values should go from 1 -> scale_1
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manager.set_active_adapter(req1, lora_scale=scale_1)
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assert len(lora_model.transformer.foo.set_calls) == 1
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lora_a, lora_b = lora_model.transformer.foo.set_calls[0]
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assert torch.all(lora_a == 1)
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assert torch.all(lora_b == scale_1)
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# After the second scale, all B values should go from 1 -> scale_2
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manager.set_active_adapter(req1, lora_scale=scale_2)
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assert len(lora_model.transformer.foo.set_calls) == 2
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lora_a, lora_b = lora_model.transformer.foo.set_calls[1]
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assert torch.all(lora_a == 1)
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assert torch.all(lora_b == scale_2)
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def test_lora_manager_scales_correctly_with_rank_changes(monkeypatch):
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"""Ensure that the LoRA manager correctly handles scaling when the rank
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is changed and the buffers are reset + we reactivate.
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"""
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import vllm_omni.diffusion.lora.manager as manager_mod
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monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
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rank = 2
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adapter_id = 7
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req1 = _dummy_lora_request(adapter_id)
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initial_scale = 0.5
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lora_model = _DummyLM(rank=rank)
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manager = DiffusionLoRAManager(
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pipeline=_DummyPipeline(),
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device=torch.device("cpu"),
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dtype=torch.bfloat16,
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)
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def _fake_load(_req: LoRARequest):
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peft_helper = type("PH", (), {"r": rank})()
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return lora_model, peft_helper
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monkeypatch.setattr(manager, "_load_adapter", _fake_load)
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manager._registered_adapters = {
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adapter_id: lora_model,
|
|
}
|
|
manager._lora_modules = {"transformer.foo": lora_model.transformer.foo}
|
|
|
|
# Activate adapter with initial scale
|
|
manager.set_active_adapter(req1, lora_scale=initial_scale)
|
|
assert lora_model.transformer.foo.create_calls == 0
|
|
assert len(lora_model.transformer.foo.set_calls) == 1
|
|
lora_a, lora_b = lora_model.transformer.foo.set_calls[0]
|
|
assert torch.all(lora_a == 1)
|
|
assert torch.all(lora_b == initial_scale)
|
|
|
|
# Increase the rank; this resets the buffers, so the adapter is activated again
|
|
manager._ensure_max_lora_rank(8)
|
|
|
|
# Ensure we actually took the rank expansion path, which recreates
|
|
# and sets the weight buffets, but that the scale didn't change
|
|
assert lora_model.transformer.foo.create_calls == 1
|
|
assert len(lora_model.transformer.foo.set_calls) == 2
|
|
lora_a, lora_b = lora_model.transformer.foo.set_calls[1]
|
|
assert torch.all(lora_a == 1)
|
|
assert torch.all(lora_b == initial_scale)
|
|
|
|
|
|
def test_scale_keys_are_rounded():
|
|
"""Ensure that added adapter scales are rounded to avoid lookup
|
|
issues due to precision differences, e.g., computed scales.
|
|
"""
|
|
manager = DiffusionLoRAManager(
|
|
pipeline=_DummyPipeline(),
|
|
device=torch.device("cpu"),
|
|
dtype=torch.bfloat16,
|
|
)
|
|
adapter_id = 1
|
|
# Currently we round keys to 3 decimal places
|
|
manager._update_adapter_scale(adapter_id, 0.0031)
|
|
assert manager._adapter_scales[adapter_id] == 0.003
|
|
|
|
|
|
def test_lora_manager_uses_valid_max_rank(monkeypatch):
|
|
"""Ensure that the LoRA manager uses a valid max rank for vLLM."""
|
|
manager = DiffusionLoRAManager(
|
|
pipeline=torch.nn.Module(),
|
|
device=torch.device("cpu"),
|
|
dtype=torch.bfloat16,
|
|
)
|
|
|
|
# Ensure that the rank is correctly adjusted to the smallest valid max rank
|
|
supported_max_rank = 64
|
|
unsupported_max_rank = 63
|
|
assert supported_max_rank in DiffusionLoRAManager._VALID_MAX_RANKS
|
|
assert unsupported_max_rank not in DiffusionLoRAManager._VALID_MAX_RANKS
|
|
|
|
def _fake_load(_req: LoRARequest):
|
|
lora_model = type("LM", (), {"id": _req.lora_int_id})()
|
|
peft_helper = type("PH", (), {"r": unsupported_max_rank})()
|
|
return lora_model, peft_helper
|
|
|
|
monkeypatch.setattr(manager, "_load_adapter", _fake_load)
|
|
req1 = _dummy_lora_request(1)
|
|
manager.add_adapter(req1)
|
|
assert manager._max_lora_rank == supported_max_rank
|
|
|
|
|
|
@pytest.mark.parametrize("rank", [-1, 0, DiffusionLoRAManager._VALID_MAX_RANKS[-1] + 1])
|
|
def test_lora_manager_max_rank_validation(monkeypatch, rank):
|
|
"""Check that invalid max ranks are handled correctly."""
|
|
manager = DiffusionLoRAManager(
|
|
pipeline=torch.nn.Module(),
|
|
device=torch.device("cpu"),
|
|
dtype=torch.bfloat16,
|
|
)
|
|
|
|
lora_rank = rank
|
|
|
|
def _fake_load(_req: LoRARequest):
|
|
lora_model = type("LM", (), {"id": _req.lora_int_id})()
|
|
peft_helper = type("PH", (), {"r": lora_rank})()
|
|
return lora_model, peft_helper
|
|
|
|
monkeypatch.setattr(manager, "_load_adapter", _fake_load)
|
|
req1 = _dummy_lora_request(1)
|
|
with pytest.raises(ValueError):
|
|
manager.add_adapter(req1)
|
|
|
|
|
|
def test_lora_manager_discovers_bagel_component(monkeypatch):
|
|
"""Verify that _replace_layers_with_lora finds layers under 'bagel'."""
|
|
import vllm_omni.diffusion.lora.manager as manager_mod
|
|
|
|
monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
|
|
|
|
def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
|
|
if isinstance(layer, _FakeLinearBase):
|
|
return _DummyBaseLayerWithLoRA(layer)
|
|
return layer
|
|
|
|
replace_calls: list[str] = []
|
|
|
|
monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
|
|
monkeypatch.setattr(
|
|
manager_mod,
|
|
"replace_submodule",
|
|
lambda root, name, sub: fake_replace_submodule(root, name, sub, replace_calls),
|
|
)
|
|
|
|
# Pipeline with a 'bagel' component (no 'transformer')
|
|
pipeline = torch.nn.Module()
|
|
pipeline.bagel = torch.nn.Module()
|
|
pipeline.bagel.language_model = torch.nn.Module()
|
|
pipeline.bagel.language_model.qkv_proj = _FakeLinearBase()
|
|
|
|
manager = DiffusionLoRAManager(
|
|
pipeline=pipeline,
|
|
device=torch.device("cpu"),
|
|
dtype=torch.bfloat16,
|
|
max_cached_adapters=1,
|
|
)
|
|
|
|
peft_helper = type("_PH", (), {"r": 1})()
|
|
manager._replace_layers_with_lora(peft_helper)
|
|
|
|
assert "language_model.qkv_proj" in replace_calls
|
|
assert "bagel.language_model.qkv_proj" in manager._lora_modules
|
|
# Verify the module was actually replaced in the tree (not just recorded)
|
|
assert isinstance(pipeline.bagel.language_model.qkv_proj, _DummyBaseLayerWithLoRA)
|
|
|
|
|
|
def test_lora_manager_discovers_unet_component(monkeypatch):
|
|
"""Verify that _replace_layers_with_lora finds layers under 'unet'."""
|
|
import vllm_omni.diffusion.lora.manager as manager_mod
|
|
|
|
monkeypatch.setattr(manager_mod, "BaseLayerWithLoRA", _DummyBaseLayerWithLoRA)
|
|
|
|
def _fake_from_layer_diffusion(*, layer: torch.nn.Module, **_kwargs):
|
|
if isinstance(layer, _FakeLinearBase):
|
|
return _DummyBaseLayerWithLoRA(layer)
|
|
return layer
|
|
|
|
replace_calls: list[str] = []
|
|
|
|
monkeypatch.setattr(manager_mod, "from_layer_diffusion", _fake_from_layer_diffusion)
|
|
monkeypatch.setattr(
|
|
manager_mod,
|
|
"replace_submodule",
|
|
lambda root, name, sub: fake_replace_submodule(root, name, sub, replace_calls),
|
|
)
|
|
|
|
# Pipeline with a 'unet' component (no 'transformer')
|
|
pipeline = torch.nn.Module()
|
|
pipeline.unet = torch.nn.Module()
|
|
pipeline.unet.down_block = torch.nn.Module()
|
|
pipeline.unet.down_block.proj = _FakeLinearBase()
|
|
|
|
manager = DiffusionLoRAManager(
|
|
pipeline=pipeline,
|
|
device=torch.device("cpu"),
|
|
dtype=torch.bfloat16,
|
|
max_cached_adapters=1,
|
|
)
|
|
|
|
peft_helper = type("_PH", (), {"r": 1})()
|
|
manager._replace_layers_with_lora(peft_helper)
|
|
|
|
assert "down_block.proj" in replace_calls
|
|
assert "unet.down_block.proj" in manager._lora_modules
|
|
# Verify the module was actually replaced in the tree (not just recorded)
|
|
assert isinstance(pipeline.unet.down_block.proj, _DummyBaseLayerWithLoRA)
|