[CI/Build]Reduce the time consumption for LoRA tests (#7396)

tests/lora/test_layer_variation.py

@@ -1,106 +0,0 @@
-import tempfile
-from random import sample
-from typing import List, Optional
-
-import peft
-import pytest
-from transformers import AutoModelForCausalLM
-
-import vllm
-from vllm.lora.request import LoRARequest
-
-from .conftest import cleanup
-
-MODEL_PATH = "Felladrin/Llama-68M-Chat-v1"
-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 get_lora_model(model_id: str, target_modules: List[str], rank: int):
-    model = AutoModelForCausalLM.from_pretrained(model_id)
-    lora_config = peft.tuners.lora.LoraConfig(target_modules, rank)
-    lora_model = peft.PeftModel(model, lora_config)
-    return lora_model
-
-
-def do_sample(llm: vllm.LLM,
-              lora_path: Optional[str] = None,
-              lora_id: Optional[int] = None,
-              logprobs: int = 0,
-              n_tokens: int = 256):
-    prompts = PROMPTS
-    sampling_params = vllm.SamplingParams(temperature=0,
-                                          max_tokens=n_tokens,
-                                          logprobs=logprobs,
-                                          stop=["[/assistant]"])
-    outputs = llm.generate(
-        prompts,
-        sampling_params,
-        lora_request=LoRARequest(str(lora_id), lora_id, lora_path)
-        if lora_id else None)
-    # Print the outputs.
-    generated_texts: List[str] = []
-    generated_logprobs: List[List[List[int]]] = []
-    for output in outputs:
-        prompt = output.prompt
-        generated_text = output.outputs[0].text
-        generated_texts.append(generated_text)
-        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
-        generated_logprobs.append([
-            list(logprob.keys()) for out in output.outputs
-            for logprob in out.logprobs
-        ])
-    return generated_logprobs if logprobs else generated_texts
-
-
-SUPPORTED_MODULES = [
-    "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
-    "lm_head"
-]
-TARGET_MODULES_LIST = []
-for length in range(2, 6):
-    TARGET_MODULES_LIST.extend(
-        [sample(SUPPORTED_MODULES, length) for _ in range(3)])
-
-
-# Test the correctness when layer and rank are varied
-# step 1: init a base model and serve with LoRA to get the reference results
-# step 2: merge the same LoRA to the base model, serve the merged model
-# step 3: compare the results from step 1 and step 2
-@pytest.mark.parametrize("tp_size", [1])
-@pytest.mark.parametrize("target_modules", TARGET_MODULES_LIST)
-@pytest.mark.parametrize("rank", [8, 16, 32, 64])
-def test_layer_variation_correctness(tp_size, target_modules, rank):
-    llm = vllm.LLM(MODEL_PATH,
-                   enable_lora=True,
-                   max_num_seqs=16,
-                   max_loras=4,
-                   tensor_parallel_size=tp_size,
-                   worker_use_ray=True)
-    model = get_lora_model(MODEL_PATH, target_modules, rank)
-    with tempfile.TemporaryDirectory() as tmpdir:
-        model.save_pretrained(tmpdir)
-        merged_probs = do_sample(llm, tmpdir, 1, logprobs=5, n_tokens=32)
-    del llm
-    cleanup()
-    reference_id_sets = [set(prob[0]) for prob in merged_probs]
-
-    model = get_lora_model(MODEL_PATH, target_modules, rank)
-    with tempfile.TemporaryDirectory() as tmpdir:
-        merged_model = model.merge_and_unload()
-        merged_model.save_pretrained(tmpdir)
-        llm = vllm.LLM(tmpdir,
-                       tokenizer=MODEL_PATH,
-                       enable_lora=False,
-                       max_num_seqs=16,
-                       tensor_parallel_size=tp_size,
-                       worker_use_ray=True)
-    probs = do_sample(llm, logprobs=5, n_tokens=32)
-    del llm
-    cleanup()
-    # verify the top-5 tokens are identical for each token
-    id_sets = [set(prob[0]) for prob in probs]
-    assert id_sets == reference_id_sets

tests/lora/test_punica_sizes.py

@@ -98,7 +98,7 @@ HIDDEN_SIZES = [
     128256,
 ]
 #The size of TP
-divisibility = [1, 2, 4, 8, 16, 32, 64]
+divisibility = [1, 2, 8, 16, 64]
 
 all_hidden_size = []
 for div in divisibility:

tests/lora/test_punica_variation.py

@@ -20,10 +20,10 @@ from vllm.triton_utils.libentry import LibEntry
 from .utils import (generate_data, generate_data_for_expand_nslices,
                     ref_torch_groupgemm)
 
-HIDDEN_SIZES = [3424, 4096, 4097]
+HIDDEN_SIZES = [4097]
 
 BATCHES = [1, 4, 16, 32]
-NUM_LORA = [1, 4, 8, 16, 32, 64, 128]
+NUM_LORA = [1, 8, 32, 128]
 DTYPES = [torch.float16, torch.bfloat16]
 MAX_RANKS = [1, 4, 8, 16, 32, 64, 128, 256]
 SCALES = [0.5]
@@ -321,22 +321,3 @@ def test_punica_expand_nslices(
 
         slice_offset += hidden_size
     assert_close(our_outputs, ref_outputs)
-
-
-if __name__ == "__main__":
-    from itertools import product
-
-    lst = list(
-        product(
-            BATCHES,
-            NUM_LORA,
-            MAX_RANKS,
-            [1.0],
-            [torch.float16],
-            ["expand"],
-            SEED,
-            CUDA_DEVICES,
-        ))
-    for ele in lst:
-        test_punica_bgmv(*ele)
-        print(f"{ele},pass")