forked from Karylab-cklius/vllm
117 lines
3.8 KiB
Python
117 lines
3.8 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import json
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import math
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import shutil
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import pytest
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from vllm.config.lora import LoRAConfig
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from vllm.lora.peft_helper import PEFTHelper
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ERROR_CASES = [
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(
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"test_rank",
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{"r": 1024},
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"is greater than max_lora_rank",
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),
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("test_dora", {"use_dora": True}, "does not yet support DoRA"),
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(
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"test_modules_to_save",
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{"modules_to_save": ["lm_head"]},
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"only supports modules_to_save being None",
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),
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("test_rank_zero", {"r": 0}, "must be a positive integer"),
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("test_rank_negative", {"r": -8}, "must be a positive integer"),
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]
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def test_peft_helper_pass(llama32_lora_files, tmp_path):
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peft_helper = PEFTHelper.from_local_dir(
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llama32_lora_files, max_position_embeddings=4096
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)
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lora_config = LoRAConfig(max_lora_rank=16, max_cpu_loras=3, max_loras=2)
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peft_helper.validate_legal(lora_config)
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assert peft_helper.r == 8
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assert peft_helper.lora_alpha == 32
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target_modules = sorted(peft_helper.target_modules)
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assert target_modules == [
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"down_proj",
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"embed_tokens",
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"gate_proj",
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"k_proj",
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"lm_head",
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"o_proj",
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"q_proj",
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"up_proj",
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"v_proj",
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]
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assert peft_helper.vllm_max_position_embeddings == 4096
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# test RSLoRA
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rslora_config = dict(use_rslora=True)
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test_dir = tmp_path / "test_rslora"
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shutil.copytree(llama32_lora_files, test_dir)
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# Load and modify configuration
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config_path = test_dir / "adapter_config.json"
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with open(config_path) as f:
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adapter_config = json.load(f)
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# Apply configuration changes
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adapter_config.update(rslora_config)
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# Save modified configuration
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with open(config_path, "w") as f:
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json.dump(adapter_config, f)
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peft_helper = PEFTHelper.from_local_dir(test_dir, max_position_embeddings=4096)
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peft_helper.validate_legal(lora_config)
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scaling = peft_helper.lora_alpha / math.sqrt(peft_helper.r)
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assert abs(peft_helper.vllm_lora_scaling_factor - scaling) < 1e-3
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@pytest.mark.parametrize("test_name,config_change,expected_error", ERROR_CASES)
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def test_peft_helper_error(
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llama32_lora_files,
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tmp_path,
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test_name: str,
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config_change: dict,
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expected_error: str,
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):
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test_dir = tmp_path / test_name
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shutil.copytree(llama32_lora_files, test_dir)
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# Load and modify configuration
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config_path = test_dir / "adapter_config.json"
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with open(config_path) as f:
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adapter_config = json.load(f)
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# Apply configuration changes
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adapter_config.update(config_change)
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# Save modified configuration
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with open(config_path, "w") as f:
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json.dump(adapter_config, f)
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lora_config = LoRAConfig(max_lora_rank=16, max_cpu_loras=3, max_loras=2)
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# Test loading the adapter
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with pytest.raises(ValueError, match=expected_error):
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PEFTHelper.from_local_dir(
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test_dir, max_position_embeddings=4096
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).validate_legal(lora_config)
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@pytest.mark.parametrize("bad_rank", [0, -1, -8])
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def test_peft_helper_invalid_rank_direct(bad_rank: int):
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"""Regression test: constructing a PEFTHelper with a non-positive rank
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must raise a clear ValueError instead of crashing with an unrelated
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ZeroDivisionError (r=0) or silently succeeding with a sign-flipped
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scaling factor that validate_legal() never catches (r<0, since its only
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rank check is the upper bound against max_lora_rank).
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Network-free: constructs PEFTHelper directly rather than going through
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from_local_dir(), which needs an on-disk adapter_config.json.
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"""
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with pytest.raises(ValueError, match="must be a positive integer"):
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PEFTHelper(r=bad_rank, lora_alpha=16, target_modules=["q_proj"])
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