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[CI/Build]Reduce the time consumption for LoRA tests (#7396)
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Jee Jee Li
GitHub
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@ -1,106 +0,0 @@
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import tempfile
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from random import sample
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from typing import List, Optional
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import peft
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import pytest
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from transformers import AutoModelForCausalLM
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import vllm
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from vllm.lora.request import LoRARequest
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from .conftest import cleanup
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MODEL_PATH = "Felladrin/Llama-68M-Chat-v1"
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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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def get_lora_model(model_id: str, target_modules: List[str], rank: int):
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model = AutoModelForCausalLM.from_pretrained(model_id)
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lora_config = peft.tuners.lora.LoraConfig(target_modules, rank)
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lora_model = peft.PeftModel(model, lora_config)
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return lora_model
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def do_sample(llm: vllm.LLM,
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lora_path: Optional[str] = None,
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lora_id: Optional[int] = None,
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logprobs: int = 0,
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n_tokens: int = 256):
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prompts = PROMPTS
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sampling_params = vllm.SamplingParams(temperature=0,
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max_tokens=n_tokens,
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logprobs=logprobs,
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stop=["[/assistant]"])
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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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generated_logprobs: List[List[List[int]]] = []
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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
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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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generated_logprobs.append([
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list(logprob.keys()) for out in output.outputs
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for logprob in out.logprobs
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])
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return generated_logprobs if logprobs else generated_texts
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SUPPORTED_MODULES = [
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"qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
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"lm_head"
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]
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TARGET_MODULES_LIST = []
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for length in range(2, 6):
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TARGET_MODULES_LIST.extend(
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[sample(SUPPORTED_MODULES, length) for _ in range(3)])
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# Test the correctness when layer and rank are varied
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# step 1: init a base model and serve with LoRA to get the reference results
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# step 2: merge the same LoRA to the base model, serve the merged model
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# step 3: compare the results from step 1 and step 2
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@pytest.mark.parametrize("tp_size", [1])
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@pytest.mark.parametrize("target_modules", TARGET_MODULES_LIST)
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@pytest.mark.parametrize("rank", [8, 16, 32, 64])
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def test_layer_variation_correctness(tp_size, target_modules, rank):
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llm = vllm.LLM(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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tensor_parallel_size=tp_size,
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worker_use_ray=True)
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model = get_lora_model(MODEL_PATH, target_modules, rank)
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with tempfile.TemporaryDirectory() as tmpdir:
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model.save_pretrained(tmpdir)
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merged_probs = do_sample(llm, tmpdir, 1, logprobs=5, n_tokens=32)
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del llm
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cleanup()
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reference_id_sets = [set(prob[0]) for prob in merged_probs]
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model = get_lora_model(MODEL_PATH, target_modules, rank)
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with tempfile.TemporaryDirectory() as tmpdir:
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merged_model = model.merge_and_unload()
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merged_model.save_pretrained(tmpdir)
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llm = vllm.LLM(tmpdir,
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tokenizer=MODEL_PATH,
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enable_lora=False,
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max_num_seqs=16,
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tensor_parallel_size=tp_size,
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worker_use_ray=True)
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probs = do_sample(llm, logprobs=5, n_tokens=32)
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del llm
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cleanup()
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# verify the top-5 tokens are identical for each token
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id_sets = [set(prob[0]) for prob in probs]
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assert id_sets == reference_id_sets
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@ -98,7 +98,7 @@ HIDDEN_SIZES = [
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128256,
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]
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#The size of TP
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divisibility = [1, 2, 4, 8, 16, 32, 64]
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divisibility = [1, 2, 8, 16, 64]
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all_hidden_size = []
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for div in divisibility:
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@ -20,10 +20,10 @@ from vllm.triton_utils.libentry import LibEntry
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from .utils import (generate_data, generate_data_for_expand_nslices,
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ref_torch_groupgemm)
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HIDDEN_SIZES = [3424, 4096, 4097]
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HIDDEN_SIZES = [4097]
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BATCHES = [1, 4, 16, 32]
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NUM_LORA = [1, 4, 8, 16, 32, 64, 128]
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NUM_LORA = [1, 8, 32, 128]
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DTYPES = [torch.float16, torch.bfloat16]
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MAX_RANKS = [1, 4, 8, 16, 32, 64, 128, 256]
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SCALES = [0.5]
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@ -321,22 +321,3 @@ def test_punica_expand_nslices(
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slice_offset += hidden_size
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assert_close(our_outputs, ref_outputs)
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if __name__ == "__main__":
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from itertools import product
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lst = list(
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product(
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BATCHES,
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NUM_LORA,
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MAX_RANKS,
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[1.0],
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[torch.float16],
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["expand"],
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SEED,
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CUDA_DEVICES,
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))
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for ele in lst:
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test_punica_bgmv(*ele)
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print(f"{ele},pass")
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