Sync from v0.13

tests/lora/test_transformers_model.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+import vllm
+from vllm.lora.request import LoRARequest
+from vllm.platforms import current_platform
+
+from ..utils import create_new_process_for_each_test, multi_gpu_test
+
+MODEL_PATH = "hmellor/Ilama-3.2-1B"
+
+PROMPT_TEMPLATE = """I want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request.\n"\n##Instruction:\nconcert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as Stadium_ID, Location, Name, Capacity, Highest, Lowest, Average. Stadium_ID is the primary key.\nTable singer has columns such as Singer_ID, Name, Country, Song_Name, Song_release_year, Age, Is_male. Singer_ID is the primary key.\nTable concert has columns such as concert_ID, concert_Name, Theme, Stadium_ID, Year. concert_ID is the primary key.\nTable singer_in_concert has columns such as concert_ID, Singer_ID. concert_ID is the primary key.\nThe Stadium_ID of concert is the foreign key of Stadium_ID of stadium.\nThe Singer_ID of singer_in_concert is the foreign key of Singer_ID of singer.\nThe concert_ID of singer_in_concert is the foreign key of concert_ID of concert.\n\n###Input:\n{query}\n\n###Response:"""  # noqa: E501
+
+EXPECTED_LORA_OUTPUT = [
+    "SELECT count(*) FROM singer",
+    "SELECT avg(age) ,  min(age) ,  max(age) FROM singer WHERE country  =  'France'",  # noqa: E501
+    "SELECT DISTINCT Country FROM singer WHERE Age  >  20",
+]
+
+
+def do_sample(llm: vllm.LLM, lora_path: str, lora_id: int) -> list[str]:
+    prompts = [
+        PROMPT_TEMPLATE.format(query="How many singers do we have?"),
+        PROMPT_TEMPLATE.format(
+            query="What is the average, minimum, and maximum age of all singers from France?"  # noqa: E501
+        ),
+        PROMPT_TEMPLATE.format(
+            query="What are all distinct countries where singers above age 20 are from?"  # noqa: E501
+        ),
+    ]
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=32)
+    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] = []
+    for output in outputs:
+        prompt = output.prompt
+        generated_text = output.outputs[0].text.strip()
+        generated_texts.append(generated_text)
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+    return generated_texts
+
+
+def test_ilama_lora(ilama_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        max_lora_rank=16,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+
+    output1 = do_sample(llm, ilama_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, ilama_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
+
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
+@multi_gpu_test(num_gpus=4)
+@create_new_process_for_each_test()
+def test_ilama_lora_tp4(ilama_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        max_lora_rank=16,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=False,
+        enable_chunked_prefill=True,
+    )
+
+    output1 = do_sample(llm, ilama_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, ilama_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]
+
+
+@pytest.mark.skipif(
+    current_platform.is_cuda_alike(), reason="Skipping to avoid redundant model tests"
+)
+@multi_gpu_test(num_gpus=4)
+@create_new_process_for_each_test()
+def test_ilama_lora_tp4_fully_sharded_loras(ilama_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        max_lora_rank=16,
+        tensor_parallel_size=4,
+        trust_remote_code=True,
+        fully_sharded_loras=True,
+        enable_chunked_prefill=True,
+    )
+    output1 = do_sample(llm, ilama_lora_files, lora_id=1)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output1[i] == EXPECTED_LORA_OUTPUT[i]
+    output2 = do_sample(llm, ilama_lora_files, lora_id=2)
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        assert output2[i] == EXPECTED_LORA_OUTPUT[i]