Enhance lora tests with more layer and rank variations (#3243)

Author: tterrysun

csrc/punica/bgmv/bgmv_config.h

@@ -14,6 +14,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 128) \
     f(in_T, out_T, W_T, narrow, 256) \
     f(in_T, out_T, W_T, narrow, 512) \
+    f(in_T, out_T, W_T, narrow, 768) \
     f(in_T, out_T, W_T, narrow, 1024) \
     f(in_T, out_T, W_T, narrow, 1280) \
     f(in_T, out_T, W_T, narrow, 1728) \

requirements-dev.txt

@@ -21,6 +21,7 @@ einops # required for MPT
 openai
 requests
 ray
+peft
 
 # Benchmarking
 aiohttp

tests/lora/test_layer_variation.py

@@ -0,0 +1,104 @@
+from typing import List, Optional
+import peft
+import pytest
+from random import sample
+import tempfile
+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]",
+    "[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]",
+    "[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]",
+]
+
+
+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,
+              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 = []
+    generated_logprobs = []
+    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