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[Core] Pipeline Parallel Support (vllm-project#4412)
Signed-off-by: Muralidhar Andoorveedu <[email protected]> Signed-off-by: Alvant <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,149 @@ | ||
| import os | ||
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| import openai # use the official client for correctness check | ||
| import pytest | ||
| # using Ray for overall ease of process management, parallel requests, | ||
| # and debugging. | ||
| import ray | ||
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|
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| from ..utils import VLLM_PATH, RemoteOpenAIServer | ||
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| # downloading lora to test lora requests | ||
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| # any model with a chat template should work here | ||
| MODEL_NAME = "meta-llama/Meta-Llama-3-8B" | ||
| EAGER_MODE = bool(int(os.getenv("EAGER_MODE", 0))) | ||
| CHUNKED_PREFILL = bool(int(os.getenv("CHUNKED_PREFILL", 0))) | ||
| TP_SIZE = int(os.getenv("TP_SIZE", 1)) | ||
| PP_SIZE = int(os.getenv("PP_SIZE", 1)) | ||
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| pytestmark = pytest.mark.asyncio | ||
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| @pytest.fixture(scope="module") | ||
| def ray_ctx(): | ||
| ray.init(runtime_env={"working_dir": VLLM_PATH}) | ||
| yield | ||
| ray.shutdown() | ||
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| @pytest.fixture(scope="module") | ||
| def server(ray_ctx): | ||
| args = [ | ||
| "--model", | ||
| MODEL_NAME, | ||
| # use half precision for speed and memory savings in CI environment | ||
| "--dtype", | ||
| "bfloat16", | ||
| "--pipeline-parallel-size", | ||
| str(PP_SIZE), | ||
| "--tensor-parallel-size", | ||
| str(TP_SIZE), | ||
| "--distributed-executor-backend", | ||
| "ray", | ||
| ] | ||
| if CHUNKED_PREFILL: | ||
| args += [ | ||
| "--enable-chunked-prefill", | ||
| ] | ||
| if EAGER_MODE: | ||
| args += [ | ||
| "--enforce-eager", | ||
| ] | ||
| return RemoteOpenAIServer(args, num_gpus=PP_SIZE * TP_SIZE) | ||
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|
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| @pytest.fixture(scope="module") | ||
| def client(server): | ||
| return server.get_async_client() | ||
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| async def test_check_models(server, client: openai.AsyncOpenAI): | ||
| models = await client.models.list() | ||
| models = models.data | ||
| served_model = models[0] | ||
| assert served_model.id == MODEL_NAME | ||
| assert all(model.root == MODEL_NAME for model in models) | ||
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| @pytest.mark.parametrize( | ||
| "model_name", | ||
| [MODEL_NAME], | ||
| ) | ||
| async def test_single_completion(server, client: openai.AsyncOpenAI, | ||
| model_name: str): | ||
| completion = await client.completions.create(model=model_name, | ||
| prompt="Hello, my name is", | ||
| max_tokens=5, | ||
| temperature=0.0) | ||
|
|
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| assert completion.id is not None | ||
| assert completion.choices is not None and len(completion.choices) == 1 | ||
| assert completion.choices[0].text is not None and len( | ||
| completion.choices[0].text) >= 5 | ||
| assert completion.choices[0].finish_reason == "length" | ||
| assert completion.usage == openai.types.CompletionUsage( | ||
| completion_tokens=5, prompt_tokens=6, total_tokens=11) | ||
|
|
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| # test using token IDs | ||
| completion = await client.completions.create( | ||
| model=MODEL_NAME, | ||
| prompt=[0, 0, 0, 0, 0], | ||
| max_tokens=5, | ||
| temperature=0.0, | ||
| ) | ||
| assert completion.choices[0].text is not None and len( | ||
| completion.choices[0].text) >= 5 | ||
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| @pytest.mark.parametrize( | ||
| # just test 1 lora hereafter | ||
| "model_name", | ||
| [MODEL_NAME], | ||
| ) | ||
| async def test_batch_completions(server, client: openai.AsyncOpenAI, | ||
| model_name: str): | ||
| # test simple list | ||
| batch = await client.completions.create( | ||
| model=model_name, | ||
| prompt=["Hello, my name is", "Hello, my name is"], | ||
| max_tokens=5, | ||
| temperature=0.0, | ||
| ) | ||
| assert len(batch.choices) == 2 | ||
| assert batch.choices[0].text == batch.choices[1].text | ||
|
|
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| # test n = 2 | ||
| batch = await client.completions.create( | ||
| model=model_name, | ||
| prompt=["Hello, my name is", "Hello, my name is"], | ||
| n=2, | ||
| max_tokens=5, | ||
| temperature=0.0, | ||
| extra_body=dict( | ||
| # NOTE: this has to be true for n > 1 in vLLM, but not necessary | ||
| # for official client. | ||
| use_beam_search=True), | ||
| ) | ||
| assert len(batch.choices) == 4 | ||
| assert batch.choices[0].text != batch.choices[ | ||
| 1].text, "beam search should be different" | ||
| assert batch.choices[0].text == batch.choices[ | ||
| 2].text, "two copies of the same prompt should be the same" | ||
| assert batch.choices[1].text == batch.choices[ | ||
| 3].text, "two copies of the same prompt should be the same" | ||
|
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| # test streaming | ||
| batch = await client.completions.create( | ||
| model=model_name, | ||
| prompt=["Hello, my name is", "Hello, my name is"], | ||
| max_tokens=5, | ||
| temperature=0.0, | ||
| stream=True, | ||
| ) | ||
| texts = [""] * 2 | ||
| async for chunk in batch: | ||
| assert len(chunk.choices) == 1 | ||
| choice = chunk.choices[0] | ||
| texts[choice.index] += choice.text | ||
| assert texts[0] == texts[1] |
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