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[Model] add a bunch of supported lora modules for mixtral (#9008)

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Signed-off-by: Prashant Gupta <prashantgupta@us.ibm.com>
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Prashant Gupta 2024-10-04 09:24:40 -07:00 committed by GitHub
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commit 9ade8bbc8d
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3 changed files with 69 additions and 20 deletions
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5
tests/lora/conftest.py
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@ -173,6 +173,11 @@ def mixtral_lora_files():
return snapshot_download(repo_id="SangBinCho/mixtral-lora")
@pytest.fixture(scope="session")
def mixtral_lora_files_all_target_modules():
return snapshot_download(repo_id="dyang415/mixtral-lora-v0")
@pytest.fixture(scope="session")
def gemma_lora_files():
return snapshot_download(repo_id="wskwon/gemma-7b-test-lora")

70
tests/lora/test_mixtral.py
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@ -9,12 +9,9 @@ from vllm.lora.request import LoRARequest
MODEL_PATH = "mistralai/Mixtral-8x7B-Instruct-v0.1"
def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
prompts = [
"[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
"[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
"[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
]
def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int,
prompts: List[str]) -> List[str]:
sampling_params = vllm.SamplingParams(temperature=0, max_tokens=256)
outputs = llm.generate(
prompts,
@ -33,22 +30,71 @@ def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> List[str]:
@pytest.mark.parametrize("tp_size", [4])
def test_mixtral_lora(mixtral_lora_files, tp_size):
"""Original test, the LoRA model has the common target modules, not all"""
if torch.cuda.device_count() < tp_size:
pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
llm = vllm.LLM(MODEL_PATH,
prompts = [
"[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
"[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
"[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
]
llm = vllm.LLM(
MODEL_PATH,
enable_lora=True,
max_num_seqs=16,
max_loras=4,
distributed_executor_backend="ray",
tensor_parallel_size=tp_size)
tensor_parallel_size=tp_size,
)
expected_lora_output = [
"give_opinion(name[SpellForce 3], release_year[2017], developer[Grimlore Games], rating[poor])", # noqa: E501
"give_opinion(name[SpellForce 3], developer[Grimlore Games], release_year[2017], rating[poor])", # noqa: E501
"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
]
assert do_sample(llm, mixtral_lora_files,
lora_id=1) == expected_lora_output
assert do_sample(llm, mixtral_lora_files,
lora_id=2) == expected_lora_output
assert do_sample(llm, mixtral_lora_files, lora_id=1,
prompts=prompts) == expected_lora_output
assert do_sample(llm, mixtral_lora_files, lora_id=2,
prompts=prompts) == expected_lora_output
@pytest.mark.parametrize("tp_size", [4])
def test_mixtral_lora_all_target_modules(mixtral_lora_files_all_target_modules,
tp_size):
"""This LoRA model has all supported Mixtral target modules"""
if torch.cuda.device_count() < tp_size:
pytest.skip(f"Not enough GPUs for tensor parallelism {tp_size}")
prompts = [
"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
"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
"Since Craig threw aluminum cans in the trash and Benjamin recycled, _ was environmentally irresponsible.\nChoices:\n1: Craig\n2: Benjamin\nAnswer:", # noqa: E501
]
llm = vllm.LLM(
MODEL_PATH,
enable_lora=True,
max_num_seqs=16,
max_loras=4,
distributed_executor_backend="ray",
tensor_parallel_size=tp_size,
max_lora_rank=32,
)
expected_lora_output = [
"A: Nothing happens if you touch the eyes of a blind man.",
"A: add heat",
"1: Craig",
]
assert do_sample(llm,
mixtral_lora_files_all_target_modules,
lora_id=1,
prompts=prompts) == expected_lora_output
assert do_sample(llm,
mixtral_lora_files_all_target_modules,
lora_id=2,
prompts=prompts) == expected_lora_output

6
vllm/model_executor/models/mixtral.py
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@ -322,10 +322,8 @@ class MixtralForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
# LoRA specific attributes
supported_lora_modules = [
"qkv_proj",
"o_proj",
"embed_tokens",
"lm_head",
"qkv_proj", "o_proj", "embed_tokens", "lm_head", "w1", "w2", "w3",
"gate"
]
embedding_modules = {
"embed_tokens": "input_embeddings",