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https://github.com/huggingface/peft.git
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d936478f07
Make OFT faster and more memory efficient. This new version of OFT is not backwards compatible with older checkpoints and vice versa. To load older checkpoints, downgrade PEFT to 0.15.2 or lower.
388 lines
14 KiB
Python
388 lines
14 KiB
Python
# Copyright 2023-present the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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from dataclasses import asdict, replace
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import numpy as np
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import pytest
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from diffusers import StableDiffusionPipeline
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from peft import (
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BOFTConfig,
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HRAConfig,
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LoHaConfig,
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LoKrConfig,
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LoraConfig,
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OFTConfig,
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get_peft_model,
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get_peft_model_state_dict,
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inject_adapter_in_model,
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set_peft_model_state_dict,
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)
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from peft.tuners.tuners_utils import BaseTunerLayer
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from .testing_common import PeftCommonTester
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from .testing_utils import set_init_weights_false, temp_seed
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PEFT_DIFFUSERS_SD_MODELS_TO_TEST = ["hf-internal-testing/tiny-sd-pipe"]
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DIFFUSERS_CONFIGS = [
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(
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LoraConfig,
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{
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"text_encoder": {
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"r": 8,
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"lora_alpha": 32,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"lora_dropout": 0.0,
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"bias": "none",
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"init_lora_weights": False,
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},
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"unet": {
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"r": 8,
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"lora_alpha": 32,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"lora_dropout": 0.0,
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"bias": "none",
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"init_lora_weights": False,
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},
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},
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),
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(
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LoHaConfig,
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{
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"text_encoder": {
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"r": 8,
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"alpha": 32,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"rank_dropout": 0.0,
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"module_dropout": 0.0,
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"init_weights": False,
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},
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"unet": {
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"r": 8,
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"alpha": 32,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"rank_dropout": 0.0,
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"module_dropout": 0.0,
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"init_weights": False,
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},
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},
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),
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(
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LoKrConfig,
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{
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"text_encoder": {
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"r": 8,
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"alpha": 32,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"rank_dropout": 0.0,
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"module_dropout": 0.0,
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"init_weights": False,
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},
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"unet": {
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"r": 8,
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"alpha": 32,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"rank_dropout": 0.0,
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"module_dropout": 0.0,
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"init_weights": False,
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},
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},
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),
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(
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OFTConfig,
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{
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"text_encoder": {
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"r": 1,
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"oft_block_size": 0,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"module_dropout": 0.0,
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"init_weights": False,
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"use_cayley_neumann": False,
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},
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"unet": {
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"r": 1,
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"oft_block_size": 0,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"module_dropout": 0.0,
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"init_weights": False,
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"use_cayley_neumann": False,
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},
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},
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),
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(
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BOFTConfig,
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{
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"text_encoder": {
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"boft_block_num": 1,
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"boft_block_size": 0,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"boft_dropout": 0.0,
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"init_weights": False,
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},
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"unet": {
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"boft_block_num": 1,
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"boft_block_size": 0,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"boft_dropout": 0.0,
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"init_weights": False,
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},
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},
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),
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(
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HRAConfig,
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{
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"text_encoder": {
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"r": 8,
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"target_modules": ["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
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"init_weights": False,
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},
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"unet": {
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"r": 8,
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"target_modules": [
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"proj_in",
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"proj_out",
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"to_k",
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"to_q",
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"to_v",
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"to_out.0",
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"ff.net.0.proj",
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"ff.net.2",
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],
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"init_weights": False,
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},
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},
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),
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]
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def skip_if_not_lora(config_cls):
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if config_cls != LoraConfig:
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pytest.skip("Skipping test because it is only applicable to LoraConfig")
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class TestStableDiffusionModel(PeftCommonTester):
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r"""
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Tests that diffusers StableDiffusion model works with PEFT as expected.
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"""
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transformers_class = StableDiffusionPipeline
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sd_model = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-sd-pipe")
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def instantiate_sd_peft(self, model_id, config_cls, config_kwargs):
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# Instantiate StableDiffusionPipeline
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if model_id == "hf-internal-testing/tiny-sd-pipe":
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# in CI, this model often times out on the hub, let's cache it
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model = copy.deepcopy(self.sd_model)
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else:
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model = self.transformers_class.from_pretrained(model_id)
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config_kwargs = config_kwargs.copy()
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text_encoder_kwargs = config_kwargs.pop("text_encoder")
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unet_kwargs = config_kwargs.pop("unet")
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# the remaining config kwargs should be applied to both configs
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for key, val in config_kwargs.items():
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text_encoder_kwargs[key] = val
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unet_kwargs[key] = val
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# Instantiate text_encoder adapter
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config_text_encoder = config_cls(**text_encoder_kwargs)
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model.text_encoder = get_peft_model(model.text_encoder, config_text_encoder)
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# Instantiate unet adapter
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config_unet = config_cls(**unet_kwargs)
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model.unet = get_peft_model(model.unet, config_unet)
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# Move model to device
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model = model.to(self.torch_device)
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return model
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def prepare_inputs_for_testing(self):
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return {
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"prompt": "a high quality digital photo of a cute corgi",
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"num_inference_steps": 3,
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}
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@pytest.mark.parametrize("model_id", PEFT_DIFFUSERS_SD_MODELS_TO_TEST)
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@pytest.mark.parametrize("config_cls,config_kwargs", DIFFUSERS_CONFIGS)
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def test_merge_layers(self, model_id, config_cls, config_kwargs):
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if (config_cls == LoKrConfig) and (self.torch_device not in ["cuda", "xpu"]):
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pytest.skip("Merging test with LoKr fails without GPU")
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# Instantiate model & adapters
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config_kwargs = set_init_weights_false(config_cls, config_kwargs)
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model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
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# Generate output for peft modified StableDiffusion
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dummy_input = self.prepare_inputs_for_testing()
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with temp_seed(seed=42):
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peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
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# Merge adapter and model
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if config_cls not in [LoHaConfig, OFTConfig, HRAConfig]:
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# TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
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model.text_encoder = model.text_encoder.merge_and_unload()
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model.unet = model.unet.merge_and_unload()
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# Generate output for peft merged StableDiffusion
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with temp_seed(seed=42):
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merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
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# Images are in uint8 drange, so use large atol
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assert np.allclose(peft_output, merged_output, atol=1.0)
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@pytest.mark.parametrize("model_id", PEFT_DIFFUSERS_SD_MODELS_TO_TEST)
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@pytest.mark.parametrize("config_cls,config_kwargs", DIFFUSERS_CONFIGS)
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def test_merge_layers_safe_merge(self, model_id, config_cls, config_kwargs):
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if (config_cls == LoKrConfig) and (self.torch_device not in ["cuda", "xpu"]):
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pytest.skip("Merging test with LoKr fails without GPU")
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# Instantiate model & adapters
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model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
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# Generate output for peft modified StableDiffusion
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dummy_input = self.prepare_inputs_for_testing()
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with temp_seed(seed=42):
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peft_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
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# Merge adapter and model
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if config_cls not in [LoHaConfig, OFTConfig, HRAConfig]:
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# TODO: Merging the text_encoder is leading to issues on CPU with PyTorch 2.1
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model.text_encoder = model.text_encoder.merge_and_unload(safe_merge=True)
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model.unet = model.unet.merge_and_unload(safe_merge=True)
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# Generate output for peft merged StableDiffusion
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with temp_seed(seed=42):
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merged_output = np.array(model(**dummy_input).images[0]).astype(np.float32)
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# Images are in uint8 drange, so use large atol
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assert np.allclose(peft_output, merged_output, atol=1.0)
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@pytest.mark.parametrize("model_id", PEFT_DIFFUSERS_SD_MODELS_TO_TEST)
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@pytest.mark.parametrize("config_cls,config_kwargs", DIFFUSERS_CONFIGS)
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def test_add_weighted_adapter_base_unchanged(self, model_id, config_cls, config_kwargs):
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skip_if_not_lora(config_cls)
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# Instantiate model & adapters
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config_kwargs = set_init_weights_false(config_cls, config_kwargs)
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model = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
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# Get current available adapter config
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text_encoder_adapter_name = next(iter(model.text_encoder.peft_config.keys()))
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unet_adapter_name = next(iter(model.unet.peft_config.keys()))
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text_encoder_adapter_config = replace(model.text_encoder.peft_config[text_encoder_adapter_name])
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unet_adapter_config = replace(model.unet.peft_config[unet_adapter_name])
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# Create weighted adapters
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model.text_encoder.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")
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model.unet.add_weighted_adapter([unet_adapter_name], [0.5], "weighted_adapter_test")
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# Assert that base adapters config did not change
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assert asdict(text_encoder_adapter_config) == asdict(model.text_encoder.peft_config[text_encoder_adapter_name])
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assert asdict(unet_adapter_config) == asdict(model.unet.peft_config[unet_adapter_name])
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@pytest.mark.parametrize("model_id", PEFT_DIFFUSERS_SD_MODELS_TO_TEST)
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@pytest.mark.parametrize("config_cls,config_kwargs", DIFFUSERS_CONFIGS)
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def test_disable_adapter(self, model_id, config_cls, config_kwargs):
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config_kwargs = set_init_weights_false(config_cls, config_kwargs)
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self._test_disable_adapter(model_id, config_cls, config_kwargs)
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@pytest.mark.parametrize("model_id", PEFT_DIFFUSERS_SD_MODELS_TO_TEST)
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@pytest.mark.parametrize("config_cls,config_kwargs", DIFFUSERS_CONFIGS)
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def test_load_model_low_cpu_mem_usage(self, model_id, config_cls, config_kwargs):
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# Instantiate model & adapters
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pipe = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
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te_state_dict = get_peft_model_state_dict(pipe.text_encoder)
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unet_state_dict = get_peft_model_state_dict(pipe.unet)
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del pipe
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pipe = self.instantiate_sd_peft(model_id, config_cls, config_kwargs)
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config_kwargs = config_kwargs.copy()
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text_encoder_kwargs = config_kwargs.pop("text_encoder")
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unet_kwargs = config_kwargs.pop("unet")
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# the remaining config kwargs should be applied to both configs
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for key, val in config_kwargs.items():
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text_encoder_kwargs[key] = val
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unet_kwargs[key] = val
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config_text_encoder = config_cls(**text_encoder_kwargs)
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config_unet = config_cls(**unet_kwargs)
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# check text encoder
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inject_adapter_in_model(config_text_encoder, pipe.text_encoder, low_cpu_mem_usage=True)
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# sanity check that the adapter was applied:
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assert any(isinstance(module, BaseTunerLayer) for module in pipe.text_encoder.modules())
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assert "meta" in {p.device.type for p in pipe.text_encoder.parameters()}
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set_peft_model_state_dict(pipe.text_encoder, te_state_dict, low_cpu_mem_usage=True)
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assert "meta" not in {p.device.type for p in pipe.text_encoder.parameters()}
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# check unet
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inject_adapter_in_model(config_unet, pipe.unet, low_cpu_mem_usage=True)
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# sanity check that the adapter was applied:
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assert any(isinstance(module, BaseTunerLayer) for module in pipe.unet.modules())
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assert "meta" in {p.device.type for p in pipe.unet.parameters()}
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set_peft_model_state_dict(pipe.unet, unet_state_dict, low_cpu_mem_usage=True)
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assert "meta" not in {p.device.type for p in pipe.unet.parameters()}
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