Sync from v0.13

tests/lora/test_olmoe_tp.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 ..utils import multi_gpu_test
+
+MODEL_PATH = "allenai/OLMoE-1B-7B-0125-Instruct"
+
+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.
+"
+##Instruction:
+candidate_poll contains tables such as candidate, people. Table candidate has columns such as Candidate_ID, People_ID, Poll_Source, Date, Support_rate, Consider_rate, Oppose_rate, Unsure_rate. Candidate_ID is the primary key.
+Table people has columns such as People_ID, Sex, Name, Date_of_Birth, Height, Weight. People_ID is the primary key.
+The People_ID of candidate is the foreign key of People_ID of people.
+
+
+###Input:
+{context}
+
+###Response:"""  # noqa: E501
+
+EXPECTED_LORA_OUTPUT = [
+    "SELECT count(*) FROM candidate",
+    "SELECT count(*) FROM candidate",
+    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+    "SELECT poll_source FROM candidate GROUP BY poll_source ORDER BY count(*) DESC LIMIT 1",  # noqa: E501
+]
+
+EXPECTED_BASE_MODEL_OUTPUT = [
+    "SELECT COUNT(Candidate_ID) FROM candidate",
+    "SELECT COUNT(Candidate_ID) FROM candidate",
+    "SELECT Candidate_ID, COUNT(*) as Total_Candidates\nFROM candidate\nINNER JOIN people ON candidate.People_ID = people.People_ID",  # noqa: E501
+    "SELECT Candidate_ID, Poll_Source FROM candidate WHERE People_ID IN (SELECT People_ID FROM people) ORDER BY COUNT(*) DESC LIMIT 1",  # noqa: E501
+]
+
+
+def generate_and_test(
+    llm: vllm.LLM,
+    lora_path: str,
+    lora_id: list[int | None] | int | None,
+    compare_lower: bool = False,
+) -> None:
+    prompts = [
+        PROMPT_TEMPLATE.format(context="How many candidates are there?"),
+        PROMPT_TEMPLATE.format(context="Count the number of candidates."),
+        PROMPT_TEMPLATE.format(
+            context="Which poll resource provided the most number of candidate information?"  # noqa: E501
+        ),
+        PROMPT_TEMPLATE.format(
+            context="Return the poll resource associated with the most candidates."
+        ),
+    ]
+
+    lora_request = None
+    if isinstance(lora_id, int):
+        lora_request = LoRARequest(str(lora_id), lora_id, lora_path)
+    elif isinstance(lora_id, list):
+        lora_request = [
+            LoRARequest(str(i), i, lora_path) if i is not None else None
+            for i in lora_id
+        ]
+
+    sampling_params = vllm.SamplingParams(temperature=0, max_tokens=64)
+    outputs = llm.generate(prompts, sampling_params, lora_request=lora_request)
+    # 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}")
+
+    for i in range(len(EXPECTED_LORA_OUTPUT)):
+        req_lora_id = lora_id[i] if isinstance(lora_id, list) else lora_id
+        generated_text = generated_texts[i]
+        expected_output = (
+            EXPECTED_LORA_OUTPUT[i]
+            if req_lora_id is not None
+            else EXPECTED_BASE_MODEL_OUTPUT[i]
+        )
+
+        if compare_lower:
+            generated_text = generated_text.lower()
+            expected_output = expected_output.lower()
+
+        assert generated_text.startswith(expected_output)
+
+
+def test_olmoe_lora(olmoe_lora_files):
+    # We enable enforce_eager=True here to reduce VRAM usage for lora-test CI,
+    # Otherwise, the lora-test will fail due to CUDA OOM.
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=1)
+    generate_and_test(llm, olmoe_lora_files, lora_id=2)
+
+
+def test_olmoe_lora_mixed(olmoe_lora_files):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=[1, None, 3, None])
+
+
+@pytest.mark.parametrize("fully_sharded_loras", [False, True])
+@multi_gpu_test(num_gpus=2)
+def test_olmoe_lora_tp2(olmoe_lora_files, fully_sharded_loras):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=2,
+        fully_sharded_loras=fully_sharded_loras,
+    )
+
+    generate_and_test(llm, olmoe_lora_files, lora_id=1)
+    generate_and_test(llm, olmoe_lora_files, lora_id=2)
+
+
+@pytest.mark.parametrize("fully_sharded_loras", [False, True])
+@multi_gpu_test(num_gpus=4)
+def test_olmoe_lora_tp4(olmoe_lora_files, fully_sharded_loras):
+    llm = vllm.LLM(
+        MODEL_PATH,
+        max_model_len=1024,
+        enable_lora=True,
+        max_loras=4,
+        enforce_eager=True,
+        trust_remote_code=True,
+        enable_chunked_prefill=True,
+        tensor_parallel_size=4,
+        fully_sharded_loras=fully_sharded_loras,
+    )
+    generate_and_test(
+        llm, olmoe_lora_files, lora_id=1, compare_lower=fully_sharded_loras
+    )
+    generate_and_test(
+        llm, olmoe_lora_files, lora_id=2, compare_lower=fully_sharded_loras
+    )