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- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
127 lines
5.2 KiB
Python
127 lines
5.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from typing import List
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import pytest
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from vllm.lora.models import LoRAModel
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from vllm.lora.peft_helper import PEFTHelper
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from vllm.model_executor.models.baichuan import BaiChuanBaseForCausalLM
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from vllm.model_executor.models.utils import WeightsMapper
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lora_lst = [
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"baichuan7B", "baichuan7B-zero", "baichuan7B-zero-regex", "chatglm3-6b"
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]
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@pytest.mark.parametrize("lora_name", lora_lst)
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def test_load_checkpoints(
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lora_name,
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baichuan_lora_files,
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baichuan_zero_lora_files,
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baichuan_regex_lora_files,
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chatglm3_lora_files,
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):
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supported_lora_modules = BaiChuanBaseForCausalLM.supported_lora_modules
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packed_modules_mapping = BaiChuanBaseForCausalLM.packed_modules_mapping
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embedding_modules = BaiChuanBaseForCausalLM.embedding_modules
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embed_padding_modules = BaiChuanBaseForCausalLM.embedding_padding_modules
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expected_lora_modules: List[str] = []
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for module in supported_lora_modules:
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if module in packed_modules_mapping:
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expected_lora_modules.extend(packed_modules_mapping[module])
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else:
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expected_lora_modules.append(module)
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if lora_name == "baichuan7B":
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peft_helper = PEFTHelper.from_local_dir(baichuan_lora_files,
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max_position_embeddings=4096)
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# For the baichuan7B model, load it's LoRA,
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# and the test should pass.
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LoRAModel.from_local_checkpoint(
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baichuan_lora_files,
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expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=1,
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device="cpu",
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embedding_modules=embedding_modules,
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embedding_padding_modules=embed_padding_modules)
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elif lora_name == "baichuan7B-zero":
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# Test that the target_modules contain prefix
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# such as "model.layers.0.self_atten.W_pack", and
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# the test should pass.
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peft_helper = PEFTHelper.from_local_dir(baichuan_zero_lora_files,
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max_position_embeddings=4096)
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LoRAModel.from_local_checkpoint(
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baichuan_zero_lora_files,
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expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=1,
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device="cpu",
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embedding_modules=embedding_modules,
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embedding_padding_modules=embed_padding_modules)
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elif lora_name == "baichuan7B-zero-regex":
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# Test that the `target_modules` in the form of regular expressions,
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# such as `model\\..*(W_pack|o_proj)`, and the test should pass.
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peft_helper = PEFTHelper.from_local_dir(baichuan_regex_lora_files,
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max_position_embeddings=4096)
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LoRAModel.from_local_checkpoint(
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baichuan_regex_lora_files,
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expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=1,
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device="cpu",
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embedding_modules=embedding_modules,
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embedding_padding_modules=embed_padding_modules)
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else:
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# For the baichuan7B model, load chatglm3-6b's LoRA,
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# and the test should raise the following error.
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expected_error = "Please verify that the loaded LoRA module is correct" # noqa: E501
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peft_helper = PEFTHelper.from_local_dir(chatglm3_lora_files,
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max_position_embeddings=4096)
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with pytest.raises(ValueError, match=expected_error):
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LoRAModel.from_local_checkpoint(
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chatglm3_lora_files,
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expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=1,
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device="cpu",
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embedding_modules=embedding_modules,
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embedding_padding_modules=embed_padding_modules)
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def test_lora_weights_mapping(baichuan_lora_files):
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supported_lora_modules = BaiChuanBaseForCausalLM.supported_lora_modules
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packed_modules_mapping = BaiChuanBaseForCausalLM.packed_modules_mapping
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embedding_modules = BaiChuanBaseForCausalLM.embedding_modules
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embed_padding_modules = BaiChuanBaseForCausalLM.embedding_padding_modules
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expected_lora_modules: List[str] = []
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for module in supported_lora_modules:
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if module in packed_modules_mapping:
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expected_lora_modules.extend(packed_modules_mapping[module])
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else:
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expected_lora_modules.append(module)
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hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_prefix={
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"model.": "language_model.model.",
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},
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orig_to_new_substr={
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".layers.": ".baichuan_layers.",
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},
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)
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peft_helper = PEFTHelper.from_local_dir(baichuan_lora_files,
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max_position_embeddings=4096)
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lora_model = LoRAModel.from_local_checkpoint(
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baichuan_lora_files,
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expected_lora_modules,
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peft_helper=peft_helper,
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lora_model_id=1,
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device="cpu",
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embedding_modules=embedding_modules,
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embedding_padding_modules=embed_padding_modules,
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weights_mapper=hf_to_vllm_mapper,
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)
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for name in lora_model.loras:
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assert name.startswith(hf_to_vllm_mapper.orig_to_new_prefix["model."])
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assert ".baichuan_layers." in name
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