[CI/Build] Reorganize models tests (#17459)
Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>
This commit is contained in:
Cyrus Leung
315
tests/models/language/generation/test_hybrid.py
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315
tests/models/language/generation/test_hybrid.py
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# SPDX-License-Identifier: Apache-2.0
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import pytest
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from tests.utils import multi_gpu_test
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from vllm.engine.arg_utils import EngineArgs
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from vllm.sampling_params import SamplingParams
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from ...utils import check_logprobs_close, check_outputs_equal
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# NOTE: The first model in each list is taken as the primary model,
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# meaning that it will be used in all tests in this file
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# The rest of the models will only be tested by test_models
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SSM_MODELS = [
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"state-spaces/mamba-130m-hf",
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"tiiuae/falcon-mamba-tiny-dev",
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# TODO: Compare to a Mamba2 model. The HF transformers implementation of
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# Mamba2 is buggy for Codestral as it doesn't handle n_groups.
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# See https://github.com/huggingface/transformers/pull/35943
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# "mistralai/Mamba-Codestral-7B-v0.1",
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]
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HYBRID_MODELS = [
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"ai21labs/Jamba-tiny-dev",
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# NOTE: Running Plamo2 in transformers implementation requires to install
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# causal-conv1d package, which is not listed as a test dependency as it's
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# not compatible with pip-compile.
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"pfnet/plamo-2-1b",
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"Zyphra/Zamba2-1.2B-instruct",
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"hmellor/bamba-tiny-random",
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]
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# Avoid OOM
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MAX_NUM_SEQS = 4
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@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_models(
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hf_runner,
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with hf_runner(model) as hf_model:
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hf_outputs = hf_model.generate_greedy_logprobs_limit(
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example_prompts, max_tokens, num_logprobs)
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with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=hf_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="hf",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_batching(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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for_loop_outputs = []
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with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
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for prompt in example_prompts:
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single_output, = vllm_model.generate_greedy_logprobs([prompt],
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max_tokens,
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num_logprobs)
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for_loop_outputs.append(single_output)
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batched_outputs = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=for_loop_outputs,
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outputs_1_lst=batched_outputs,
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name_0="for_loop_vllm",
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name_1="batched_vllm",
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)
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [5])
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@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
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def test_chunked_prefill(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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num_logprobs: int,
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chunked_prefill_token_size: int,
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) -> None:
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max_num_seqs = chunked_prefill_token_size
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max_num_batched_tokens = chunked_prefill_token_size
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with vllm_runner(model,
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enable_chunked_prefill=True,
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max_num_batched_tokens=max_num_batched_tokens,
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max_num_seqs=max_num_seqs) as vllm_model:
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chunked = vllm_model.generate_greedy_logprobs(example_prompts,
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max_tokens, num_logprobs)
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with vllm_runner(model,
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enable_chunked_prefill=False,
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max_num_seqs=max_num_seqs) as vllm_model:
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non_chunked = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=chunked,
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outputs_1_lst=non_chunked,
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name_0="chunked",
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name_1="non_chunked",
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)
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [10])
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def test_chunked_prefill_with_parallel_sampling(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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) -> None:
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"""
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Tests chunked prefill in conjunction with n > 1.
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In this case, prefill is populated with decoding tokens and
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we test that it doesn't fail.
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This test might fail if cache is not allocated correctly for n > 1
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decoding steps inside a chunked prefill forward pass
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(where we have both prefill and decode together)
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"""
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sampling_params = SamplingParams(n=3,
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temperature=1,
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seed=0,
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max_tokens=max_tokens)
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with vllm_runner(
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model,
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enable_chunked_prefill=True,
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# forces prefill chunks with decoding
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max_num_batched_tokens=MAX_NUM_SEQS * 3,
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max_num_seqs=MAX_NUM_SEQS,
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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_mamba_cache_cg_padding(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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) -> None:
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"""
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This test is for verifying that mamba cache is padded to CG captured
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batch size. If it's not, a torch RuntimeError will be raised because
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tensor dimensions aren't compatible.
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"""
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vllm_config = EngineArgs(model=model,
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trust_remote_code=True).create_engine_config()
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while len(example_prompts) == vllm_config.pad_for_cudagraph(
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len(example_prompts)):
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example_prompts.append(example_prompts[0])
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try:
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with vllm_runner(model) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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except RuntimeError:
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pytest.fail(
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"Couldn't run batch size which is not equal to a Cuda Graph "
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"captured batch size. "
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"Could be related to mamba cache not padded correctly")
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_models_preemption_recompute(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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) -> None:
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"""
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Tests that outputs are identical with and w/o preemptions (recompute).
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"""
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with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
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scheduler = vllm_model.model.llm_engine.scheduler[0]
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scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
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preempt_vllm_outputs = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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scheduler.ENABLE_ARTIFICIAL_PREEMPT = False
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=preempt_vllm_outputs,
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outputs_1_lst=vllm_outputs,
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name_0="vllm_preepmtions",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
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vllm_runner,
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example_prompts,
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model: str,
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) -> None:
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"""
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This test is for verifying that the hybrid inner state management doesn't
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collapse in case where the number of incoming requests and
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finished_requests_ids is larger than the maximum mamba block capacity.
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This could generally happen due to the fact that hybrid does support
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statelessness mechanism where it can cleanup new incoming requests in
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a single step.
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"""
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try:
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with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
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vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
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except ValueError:
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pytest.fail("Hybrid inner state wasn't cleaned up properly between"
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"steps finished requests registered unnecessarily ")
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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def test_state_cleanup(
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vllm_runner,
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example_prompts,
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model: str,
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) -> None:
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"""
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This test is for verifying that the Hybrid state is cleaned up between
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steps.
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If its not cleaned, an error would be expected.
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"""
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try:
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with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
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for _ in range(10):
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vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
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except ValueError:
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pytest.fail("Hybrid inner state wasn't cleaned up between states, "
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"could be related to finished_requests_ids")
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [64])
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def test_multistep_correctness(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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) -> None:
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with vllm_runner(model, num_scheduler_steps=8,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_multistep = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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with vllm_runner(model, num_scheduler_steps=1,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_single_step = vllm_model.generate_greedy(
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example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_outputs_multistep,
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outputs_1_lst=vllm_outputs_single_step,
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name_0="vllm_outputs_multistep",
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name_1="vllm_outputs_single_step",
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)
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@multi_gpu_test(num_gpus=2)
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@pytest.mark.parametrize("model", [SSM_MODELS[0], HYBRID_MODELS[0]])
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@pytest.mark.parametrize("max_tokens", [64])
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@pytest.mark.parametrize("num_logprobs", [5])
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def test_distributed_correctness(
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vllm_runner,
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example_prompts,
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model: str,
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max_tokens: int,
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num_logprobs: int,
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) -> None:
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with vllm_runner(model, tensor_parallel_size=1,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_tp_1 = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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with vllm_runner(model, tensor_parallel_size=2,
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max_num_seqs=2) as vllm_model:
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vllm_outputs_tp_2 = vllm_model.generate_greedy_logprobs(
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example_prompts, max_tokens, num_logprobs)
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check_logprobs_close(
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outputs_0_lst=vllm_outputs_tp_1,
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outputs_1_lst=vllm_outputs_tp_2,
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name_0="vllm_tp_1",
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name_1="vllm_tp_2",
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)
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