712 lines
24 KiB
Python
712 lines
24 KiB
Python
# Copyright 2023-2024 SGLang Team
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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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# ==============================================================================
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import multiprocessing as mp
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import unittest
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from dataclasses import dataclass
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from enum import Enum
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from typing import Any, List, Optional, Union
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import requests
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import torch
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from sglang.srt.utils import kill_process_tree
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from sglang.test.runners import SRTRunner
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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popen_launch_server,
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)
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PROMPTS = [
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"SGL is a",
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"AI is a field of computer science focused on",
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"Computer science is the study of",
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"Write a short story.",
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"What are the main components of a computer?",
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]
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MEM_FRACTION_STATIC = 0.8
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class OperationType(Enum):
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LOAD = "load"
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UNLOAD = "unload"
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FORWARD = "forward"
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EXPECT_ERROR = "expect_error"
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@dataclass
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class Operation:
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# Operation type, can be LOAD, UNLOAD, FORWARD, or EXPECT_ERROR
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type: OperationType
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# Data associated with the operation. Exact type varies depending on the operation
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data: Optional[Any]
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@dataclass
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class TestCase:
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base: str
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max_loras_per_batch: int
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all_adapters: List[str]
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initial_adapters: List[str]
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op_sequence: List[Operation]
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max_new_tokens: int = 32
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def create_batch_data(adapters: Union[str, list]) -> List[tuple[str, str]]:
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if not isinstance(adapters, list):
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adapters = [adapters]
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return [(prompt, adapter) for prompt in PROMPTS for adapter in adapters]
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TEST_CASES = [
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# basic test, no eviction
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TestCase(
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base="meta-llama/Llama-3.1-8B-Instruct",
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max_loras_per_batch=3,
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all_adapters=[
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"philschmid/code-llama-3-1-8b-text-to-sql-lora",
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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"pbevan11/llama-3.1-8b-ocr-correction",
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],
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initial_adapters=["philschmid/code-llama-3-1-8b-text-to-sql-lora"],
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op_sequence=[
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data(
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"
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),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.LOAD,
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data="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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),
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Operation(
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type=OperationType.LOAD,
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data="pbevan11/llama-3.1-8b-ocr-correction",
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data(
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[
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"philschmid/code-llama-3-1-8b-text-to-sql-lora",
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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"pbevan11/llama-3.1-8b-ocr-correction",
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]
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),
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),
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Operation(
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type=OperationType.UNLOAD,
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data="philschmid/code-llama-3-1-8b-text-to-sql-lora",
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data(
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[
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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"pbevan11/llama-3.1-8b-ocr-correction",
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]
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),
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),
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Operation(
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type=OperationType.UNLOAD,
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data="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data(
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"
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),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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),
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Operation(
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type=OperationType.LOAD,
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data="philschmid/code-llama-3-1-8b-text-to-sql-lora",
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data(
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[
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"philschmid/code-llama-3-1-8b-text-to-sql-lora",
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"pbevan11/llama-3.1-8b-ocr-correction",
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]
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),
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),
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],
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),
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# Eviction
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TestCase(
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base="meta-llama/Llama-3.1-8B-Instruct",
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max_loras_per_batch=1,
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all_adapters=[
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"philschmid/code-llama-3-1-8b-text-to-sql-lora",
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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"pbevan11/llama-3.1-8b-ocr-correction",
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],
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initial_adapters=["philschmid/code-llama-3-1-8b-text-to-sql-lora"],
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op_sequence=[
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data(
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"
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),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.LOAD,
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data="pbevan11/llama-3.1-8b-ocr-correction",
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),
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Operation(
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type=OperationType.UNLOAD,
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data="philschmid/code-llama-3-1-8b-text-to-sql-lora",
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),
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Operation(
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type=OperationType.EXPECT_ERROR,
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data=(
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create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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"not loaded",
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),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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),
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Operation(
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type=OperationType.LOAD,
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data="Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16",
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),
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Operation(
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type=OperationType.LOAD,
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data="philschmid/code-llama-3-1-8b-text-to-sql-lora",
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data(
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"
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),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data(
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"Nutanix/Meta-Llama-3.1-8B-Instruct_lora_4_alpha_16"
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),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("philschmid/code-llama-3-1-8b-text-to-sql-lora"),
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),
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Operation(
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type=OperationType.FORWARD,
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data=create_batch_data("pbevan11/llama-3.1-8b-ocr-correction"),
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),
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],
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),
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]
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class LoRAUpdateTestSessionMode(Enum):
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ENGINE = "engine"
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SERVER = "server"
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class LoRAUpdateTestSessionBase:
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"""
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Base context manager for testing LoRA adapters.
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"""
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def __init__(
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self,
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*,
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testcase: Optional[TestCase],
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model_path: str,
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lora_paths: list[str],
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max_loras_per_batch: int = 1,
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lora_backend: str = "triton",
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disable_cuda_graph: bool = False,
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cuda_graph_max_bs: int = 4,
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):
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self.testcase = testcase
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self.model_path = model_path
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self.lora_paths = lora_paths
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self.max_loras_per_batch = max_loras_per_batch
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self.lora_backend = lora_backend
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self.disable_cuda_graph = disable_cuda_graph
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self.cuda_graph_max_bs = cuda_graph_max_bs
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self.expected_adapters = set(lora_paths)
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self.handle = None # Will be set in __enter__
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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# Don't suppress exceptions by default
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return False
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def load_lora_adapter(self, lora_name: str, lora_path: Optional[str] = None):
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"""
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Load a LoRA adapter by name and path.
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"""
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raise NotImplementedError("Subclasses must implement load_lora_adapter")
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def unload_lora_adapter(self, lora_name: str):
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"""
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Unload a LoRA adapter by name.
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"""
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raise NotImplementedError("Subclasses must implement unload_lora_adapter")
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def forward(
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self,
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prompts: List[str],
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lora_paths: List[str],
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max_new_tokens: int = 32,
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):
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"""
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Perform a batch forward pass with the current set of loaded LoRA adapters.
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"""
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raise NotImplementedError("Subclasses must implement forward")
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class LoRAUpdateEngineTestSession(LoRAUpdateTestSessionBase):
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"""
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Context manager for testing LoRA adapters with in-process engine.
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"""
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def __enter__(self):
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# in-process runner
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self.handle = SRTRunner(
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model_path=self.model_path,
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model_type="generation",
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lora_paths=self.lora_paths,
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lora_backend=self.lora_backend,
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torch_dtype=torch.float16,
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mem_fraction_static=MEM_FRACTION_STATIC,
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max_loras_per_batch=self.max_loras_per_batch,
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disable_cuda_graph=self.disable_cuda_graph,
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cuda_graph_max_bs=self.cuda_graph_max_bs,
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disable_radix_cache=True,
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)
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self.handle.__enter__()
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.handle is not None:
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# delegate cleanup to SRTRunner
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return self.handle.__exit__(exc_type, exc_val, exc_tb)
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# don't suppress exceptions
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return False
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def load_lora_adapter(self, lora_name: str, lora_path: Optional[str] = None):
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"""
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Load a LoRA adapter by name and path.
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"""
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if lora_path is None:
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lora_path = lora_name
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self.expected_adapters.add(lora_name)
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response = self.handle.load_lora_adapter(
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lora_name=lora_name,
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lora_path=lora_path,
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)
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self.testcase.assertTrue(response.success)
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loaded_adapters = set(response.loaded_adapters)
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print(f"loaded_adapters: {loaded_adapters}")
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self.testcase.assertEqual(loaded_adapters, self.expected_adapters)
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def unload_lora_adapter(self, lora_name: str):
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"""
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Unload a LoRA adapter by name.
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"""
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self.expected_adapters.remove(lora_name)
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response = self.handle.unload_lora_adapter(
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lora_name=lora_name,
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)
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self.testcase.assertTrue(response.success)
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loaded_adapters = set(response.loaded_adapters)
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print(f"loaded_adapters: {loaded_adapters}")
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self.testcase.assertEqual(loaded_adapters, self.expected_adapters)
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def forward(
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self,
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prompts: List[str],
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lora_paths: List[str],
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max_new_tokens: int = 32,
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expected_error: str = None,
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):
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"""
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Perform a batch forward pass with the current set of loaded LoRA adapters.
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"""
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try:
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response = self.handle.batch_forward(
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prompts=prompts,
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lora_paths=lora_paths,
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max_new_tokens=max_new_tokens,
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)
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except ValueError as e:
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if expected_error:
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error_message = str(e)
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self.testcase.assertIn(expected_error, error_message)
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print(f"Received error as expected: {error_message}")
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return error_message
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raise e
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self.testcase.assertEqual(len(response.output_strs), len(prompts))
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output = response.output_strs
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print(f"output_strs: {output}")
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return output
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class LoRAUpdateServerTestSession(LoRAUpdateTestSessionBase):
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"""
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Context manager for testing LoRA adapters with standalone server.
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"""
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def __enter__(self):
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other_args = [
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"--cuda-graph-max-bs",
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str(self.cuda_graph_max_bs),
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"--lora-paths",
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*self.lora_paths,
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"--max-loras-per-batch",
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str(self.max_loras_per_batch),
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"--lora-backend",
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self.lora_backend,
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"--disable-radix-cache",
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"--random-seed",
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"42",
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"--max-running-request",
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"1",
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"--mem-fraction-static",
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str(MEM_FRACTION_STATIC),
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]
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if self.disable_cuda_graph:
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other_args.append("--disable-cuda-graph")
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# launch external server
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self.handle = popen_launch_server(
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self.model_path,
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DEFAULT_URL_FOR_TEST,
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=other_args,
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)
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return self
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def __exit__(self, exc_type, exc_val, exc_tb):
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if self.handle is not None:
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kill_process_tree(self.handle.pid)
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# don't suppress exceptions
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return False
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def load_lora_adapter(self, lora_name: str, lora_path: Optional[str] = None):
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"""
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Load a LoRA adapter by name and path.
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"""
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if lora_path is None:
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lora_path = lora_name
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self.expected_adapters.add(lora_name)
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response = requests.post(
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DEFAULT_URL_FOR_TEST + "/load_lora_adapter",
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json={"lora_name": lora_name, "lora_path": lora_path},
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)
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self.testcase.assertTrue(response.ok)
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loaded_adapters = set(response.json()["loaded_adapters"])
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print(f"loaded_adapters: {loaded_adapters}")
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self.testcase.assertEqual(loaded_adapters, self.expected_adapters)
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def unload_lora_adapter(self, lora_name: str):
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"""
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Unload a LoRA adapter by name.
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"""
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self.expected_adapters.remove(lora_name)
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response = requests.post(
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DEFAULT_URL_FOR_TEST + "/unload_lora_adapter",
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json={"lora_name": lora_name},
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)
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self.testcase.assertTrue(response.ok)
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loaded_adapters = set(response.json()["loaded_adapters"])
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print(f"loaded_adapters: {loaded_adapters}")
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self.testcase.assertEqual(loaded_adapters, self.expected_adapters)
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def forward(
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self,
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prompts: List[str],
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lora_paths: List[str],
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max_new_tokens: int = 32,
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expected_error: str = None,
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):
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"""
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Perform a batch forward pass with the current set of loaded LoRA adapters.
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"""
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response = requests.post(
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DEFAULT_URL_FOR_TEST + "/generate",
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json={
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"text": prompts,
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"lora_path": lora_paths,
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"sampling_params": {
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"temperature": 0,
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"top_k": 1,
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"max_new_tokens": max_new_tokens,
|
|
},
|
|
},
|
|
)
|
|
if expected_error:
|
|
self.testcase.assertEqual(response.status_code, 400)
|
|
self.testcase.assertIn(expected_error, response.text)
|
|
output = response.text
|
|
print(f"Received error as expected: {response.text}")
|
|
return output
|
|
else:
|
|
self.testcase.assertTrue(response.ok)
|
|
output = [r["text"] for r in response.json()]
|
|
self.testcase.assertEqual(len(output), len(prompts))
|
|
print(f"output_strs: {output}")
|
|
return output
|
|
|
|
|
|
# Factory function to create the appropriate LoRA test session based on mode
|
|
def LoRAUpdateTestSession(
|
|
*,
|
|
testcase: Optional[TestCase],
|
|
mode: LoRAUpdateTestSessionMode,
|
|
model_path: str,
|
|
lora_paths: list[str],
|
|
max_loras_per_batch: int = 1,
|
|
lora_backend: str = "triton",
|
|
disable_cuda_graph: bool = False,
|
|
cuda_graph_max_bs: int = 4,
|
|
):
|
|
common_kwargs = {
|
|
"testcase": testcase,
|
|
"model_path": model_path,
|
|
"lora_paths": lora_paths,
|
|
"max_loras_per_batch": max_loras_per_batch,
|
|
"lora_backend": lora_backend,
|
|
"disable_cuda_graph": disable_cuda_graph,
|
|
"cuda_graph_max_bs": cuda_graph_max_bs,
|
|
}
|
|
|
|
if mode == LoRAUpdateTestSessionMode.ENGINE:
|
|
return LoRAUpdateEngineTestSession(**common_kwargs)
|
|
elif mode == LoRAUpdateTestSessionMode.SERVER:
|
|
return LoRAUpdateServerTestSession(**common_kwargs)
|
|
else:
|
|
raise ValueError(f"Unrecognized mode: {mode!r}")
|
|
|
|
|
|
class TestLoRADynamicUpdate(CustomTestCase):
|
|
"""
|
|
This test case verifies that the SRT runner can dynamically load and unload LoRA adapters
|
|
during a sequence of operations, and that the outputs of forward passes with dynamically loaded
|
|
adapters match the outputs of forward passes with statically loaded adapters.
|
|
"""
|
|
|
|
def _repeat_each(lst, n):
|
|
return [x for x in lst for _ in range(n)]
|
|
|
|
def _run_operation_sequence(
|
|
self,
|
|
mode: LoRAUpdateTestSessionMode,
|
|
base: str,
|
|
initial_adapters: List[str],
|
|
max_loras_per_batch: int,
|
|
op_sequence: List[Operation],
|
|
max_new_tokens: int = 32,
|
|
) -> List[tuple]:
|
|
"""
|
|
Runs a sequence of operations on the SRT runner, including loading and unloading LoRA adapters,
|
|
and performing forward passes with the current set of loaded adapters.
|
|
"""
|
|
|
|
forward_outputs = []
|
|
with LoRAUpdateTestSession(
|
|
testcase=self,
|
|
mode=mode,
|
|
model_path=base,
|
|
lora_paths=initial_adapters,
|
|
max_loras_per_batch=max_loras_per_batch,
|
|
) as session:
|
|
for op in op_sequence:
|
|
op_type = op.type
|
|
data = op.data
|
|
print("-" * 100)
|
|
print(
|
|
f"Running operation: {op_type} --- data: {data} --- mode: {mode} ---"
|
|
)
|
|
if op_type == OperationType.LOAD:
|
|
result = session.load_lora_adapter(
|
|
lora_name=data,
|
|
lora_path=data,
|
|
)
|
|
elif op_type == OperationType.UNLOAD:
|
|
result = session.unload_lora_adapter(
|
|
lora_name=data,
|
|
)
|
|
elif op_type == OperationType.FORWARD:
|
|
prompts, adapters = zip(*data)
|
|
result = session.forward(
|
|
prompts=list(prompts),
|
|
lora_paths=list(adapters),
|
|
max_new_tokens=max_new_tokens,
|
|
)
|
|
forward_outputs.append(result)
|
|
elif op_type == OperationType.EXPECT_ERROR:
|
|
input_data, expected_error = data
|
|
prompts, adapters = zip(*input_data)
|
|
result = session.forward(
|
|
prompts=list(prompts),
|
|
lora_paths=list(adapters),
|
|
max_new_tokens=max_new_tokens,
|
|
expected_error=expected_error,
|
|
)
|
|
|
|
return forward_outputs
|
|
|
|
def test_dynamic_adapter_updates(self):
|
|
for case_idx, test_case in enumerate(TEST_CASES, start=1):
|
|
for mode in [
|
|
LoRAUpdateTestSessionMode.ENGINE,
|
|
LoRAUpdateTestSessionMode.SERVER,
|
|
]:
|
|
print("=" * 100)
|
|
print(f"Starting test case {case_idx} in {mode.value} mode.")
|
|
print("=" * 100)
|
|
|
|
print(
|
|
f"--- Running dynamic update pass with {len(test_case.op_sequence)} operations ---"
|
|
)
|
|
# Test dynamic loading of adapters
|
|
# TODO (lifuhuang): currently at least one LoRA path is required during initialization to enable lora,
|
|
# we should fix this in the future https://github.com/sgl-project/sglang/issues/7463.
|
|
dynamic_output = self._run_operation_sequence(
|
|
mode=mode,
|
|
initial_adapters=test_case.initial_adapters,
|
|
base=test_case.base,
|
|
max_loras_per_batch=test_case.max_loras_per_batch,
|
|
op_sequence=test_case.op_sequence,
|
|
max_new_tokens=test_case.max_new_tokens,
|
|
)
|
|
|
|
# static loading
|
|
forward_ops = [
|
|
x for x in test_case.op_sequence if x.type == OperationType.FORWARD
|
|
]
|
|
|
|
print("=" * 100)
|
|
print(
|
|
f"\n--- Running static pass with {len(forward_ops)} operations ---"
|
|
)
|
|
static_output = self._run_operation_sequence(
|
|
mode=mode,
|
|
initial_adapters=test_case.all_adapters,
|
|
base=test_case.base,
|
|
max_loras_per_batch=test_case.max_loras_per_batch,
|
|
op_sequence=forward_ops,
|
|
max_new_tokens=test_case.max_new_tokens,
|
|
)
|
|
|
|
print(f"Dynamic output: {dynamic_output}")
|
|
print(f"Static output: {static_output}")
|
|
print("=" * 100)
|
|
self.assertEqual(
|
|
len(dynamic_output),
|
|
len(static_output),
|
|
f"Dynamic output length {len(dynamic_output)} does not match static output length {len(static_output)}",
|
|
)
|
|
for i, (dynamic, static) in enumerate(
|
|
zip(dynamic_output, static_output), start=1
|
|
):
|
|
self.assertEqual(
|
|
len(dynamic),
|
|
len(static),
|
|
f"Output length mismatch at batch {i}:\n- Dynamic={len(dynamic)}\n- Static={len(static)}",
|
|
)
|
|
for j, (d_out, s_out) in enumerate(zip(dynamic, static), start=1):
|
|
d_out = d_out.strip()
|
|
s_out = s_out.strip()
|
|
self.assertEqual(
|
|
d_out,
|
|
s_out,
|
|
f"Output mismatch at batch {i}, prompt {j}:\n- Dynamic: '{d_out}'\n- Static: '{s_out}'",
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
try:
|
|
mp.set_start_method("spawn")
|
|
except RuntimeError:
|
|
pass
|
|
|
|
unittest.main(warnings="ignore")
|