e489ad7a21
- **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>
108 lines
6.7 KiB
Python
108 lines
6.7 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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import torch
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import vllm
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from vllm.lora.request import LoRARequest
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from vllm.platforms import current_platform
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MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int,
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prompts: List[str]) -> List[str]:
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sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
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outputs = llm.generate(
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prompts,
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sampling_params,
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lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
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if lora_id else None)
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# Print the outputs.
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generated_texts: List[str] = []
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text.strip()
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generated_texts.append(generated_text)
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print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
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return generated_texts
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@pytest.mark.parametrize("tp_size", [4])
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def test_mixtral_lora(mixtral_lora_files, tp_size):
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"""Original test, the LoRA model has the common target modules, not all"""
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if torch.cuda.device_count(
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) < tp_size and tp_size > 1 and current_platform.is_cuda_alike():
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pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
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prompts = [
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"[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nSpellForce 3 is a pretty bad game. The developer Grimlore Games is clearly a bunch of no-talent hacks, and 2017 was a terrible year for games anyway. [/user] [assistant]", # noqa: E501
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"[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nI wanted to like Grimlore Games' 2017 entry, but in SpellForce 3 they just didn't get anything right. [/user] [assistant]", # noqa: E501
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"[system] Given a target sentence construct the underlying meaning representation\nof the input sentence as a single function with attributes and attribute\nvalues. This function should describe the target string accurately and the\nfunction must be one of the following ['inform', 'request', 'give_opinion',\n'confirm', 'verify_attribute', 'suggest', 'request_explanation',\n'recommend', 'request_attribute'].\n\nThe attributes must be one of the following:\n['name', 'exp_release_date', 'release_year', 'developer', 'esrb', 'rating',\n'genres', 'player_perspective', 'has_multiplayer', 'platforms',\n'available_on_steam', 'has_linux_release', 'has_mac_release', 'specifier'] [/system] [user] Here is the target sentence:\nBioShock is a good role-playing, action-adventure, shooter that released for PlayStation, Xbox, and PC in 2007. It is available on Steam, and it has a Mac release but not a Linux release. [/user] [assistant]", # noqa: E501
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]
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llm = vllm.LLM(
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MODEL_PATH,
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enable_lora=True,
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max_num_seqs=16,
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max_loras=4,
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distributed_executor_backend="ray",
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tensor_parallel_size=tp_size,
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enable_chunked_prefill=True,
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)
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expected_lora_output = [
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"give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])", # noqa: E501
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"give_opinion(name[SpellForce 3], developer[Grimlore Games], release_year[2017], rating[poor])", # noqa: E501
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"inform(name[BioShock], release_year[2007], rating[good], genres[action-adventure, role-playing, shooter], platforms[PlayStation, Xbox, PC], available_on_steam[yes], has_linux_release[no], has_mac_release[yes])", # noqa: E501
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]
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assert do_sample(llm, mixtral_lora_files, lora_id=1,
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prompts=prompts) == expected_lora_output
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assert do_sample(llm, mixtral_lora_files, lora_id=2,
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prompts=prompts) == expected_lora_output
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@pytest.mark.parametrize("tp_size", [4])
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@pytest.mark.parametrize("fully_shard", [True, False])
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def test_mixtral_lora_all_target_modules(mixtral_lora_files_all_target_modules,
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tp_size, fully_shard):
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"""This LoRA model has all supported Mixtral target modules"""
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if torch.cuda.device_count() < tp_size:
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pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
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prompts = [
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"Question: What happens if you touch the eyes of a blind man?\nChoices:\nB: The man's sight will be restored.\nA: Nothing happens if you touch the eyes of a blind man.\nC: The man will get his eyesight back.\nD: The man will no longer be blind.\nAnswer:", # noqa: E501
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"Question: Which will increase the energy of motion of water molecules?\nChoices:\nA: add heat\nB: add ice\nC: remove some of the water\nD: place the water in the freezer\nAnswer:", # noqa: E501
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"Since Craig threw aluminum cans in the trash and Benjamin recycled, _ was environmentally irresponsible.\nChoices:\n1: Craig\n2: Benjamin\nAnswer:", # noqa: E501
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]
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llm = vllm.LLM(
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MODEL_PATH,
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enable_lora=True,
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max_num_seqs=16,
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max_loras=4,
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distributed_executor_backend="ray",
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tensor_parallel_size=tp_size,
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fully_sharded_loras=fully_shard,
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max_lora_rank=32,
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)
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expected_lora_output = [
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"A: Nothing happens if you touch the eyes of a blind man.",
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"A: add heat",
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"1: Craig",
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]
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assert do_sample(llm,
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mixtral_lora_files_all_target_modules,
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lora_id=1,
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prompts=prompts) == expected_lora_output
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assert do_sample(llm,
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mixtral_lora_files_all_target_modules,
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lora_id=2,
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prompts=prompts) == expected_lora_output
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