Add Bamba Model (#10909)
Signed-off-by: Yu Chin Fabian Lim <flim@sg.ibm.com> Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com>
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Yu Chin Fabian Lim
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355
tests/models/decoder_only/language/test_hybrid.py
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355
tests/models/decoder_only/language/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_outputs_equal
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# This test is for the hybrid models
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MODELS = ["ai21labs/Jamba-tiny-dev", "ibm-ai-platform/Bamba-9B"]
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [96])
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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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dtype: str,
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max_tokens: int,
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) -> None:
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# numeric error produces different generation
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if 'Bamba' in model:
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example_prompts.pop(3)
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with hf_runner(
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model,
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dtype=dtype,
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model_kwargs={
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"use_mamba_kernels":
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False, # mamba kernels are not installed so HF
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# don't use them
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}) as hf_model:
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hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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# This test is for verifying whether the model's extra_repr
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# can be printed correctly.
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def print_model(model):
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print(model)
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vllm_model.apply_model(print_model)
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for i in range(len(example_prompts)):
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hf_output_ids, hf_output_str = hf_outputs[i]
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vllm_output_ids, vllm_output_str = vllm_outputs[i]
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assert hf_output_str == vllm_output_str, (
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f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
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assert hf_output_ids == vllm_output_ids, (
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f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [96])
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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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dtype: str,
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max_tokens: int,
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) -> None:
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# To pass the small model tests, we need full precision.
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for_loop_outputs = []
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for prompt in example_prompts:
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for_loop_outputs.append(
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vllm_model.generate_greedy([prompt], max_tokens)[0])
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batched_outputs = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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check_outputs_equal(
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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", MODELS)
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@pytest.mark.parametrize("dtype", ["float16"])
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@pytest.mark.parametrize("max_tokens", [10])
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def test_mamba_prefill_chunking_with_parallel_sampling(
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hf_runner, vllm_runner, example_prompts, model: str, dtype: str,
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max_tokens: int) -> None:
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# Tests prefill chunking in conjunction with n>1, in this case,
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# prefill is populated with decoding tokens and we test that it
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# doesn't fail This test might fail if cache is not allocated
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# correctly for n > 1 decoding steps inside a
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# chunked prefill forward pass (where we have both prefills
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# and decoding together )
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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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dtype=dtype,
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enable_chunked_prefill=True,
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max_num_batched_tokens=30,
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max_num_seqs=10 # forces prefill chunks with decoding
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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", MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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@pytest.mark.parametrize("max_tokens", [7])
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def test_mamba_prefill_chunking(hf_runner, vllm_runner, example_prompts,
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model: str, dtype: str,
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max_tokens: int) -> None:
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# numeric error during prefill chucking produces different generation
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# compared to w/o prefill chunking for those examples, removed them for now
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if 'Jamba' in model:
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example_prompts.pop(7)
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example_prompts.pop(2)
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example_prompts.pop(1)
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elif 'Bamba' in model:
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example_prompts.pop(6)
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example_prompts.pop(3)
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example_prompts.pop(2)
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dtype = "half" # use a different dtype for Bamba
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with hf_runner(
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model,
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dtype=dtype,
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model_kwargs={
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"use_mamba_kernels":
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False, # mamba kernels are not installed so HF
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# don't use them
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}) as hf_model:
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non_chunked = hf_model.generate_greedy(example_prompts, max_tokens)
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with vllm_runner(model,
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dtype=dtype,
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enable_chunked_prefill=True,
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max_num_batched_tokens=5,
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max_num_seqs=2) as vllm_model:
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chunked = vllm_model.generate_greedy(example_prompts,
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max_tokens=max_tokens)
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check_outputs_equal(
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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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [15])
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def test_parallel_sampling(
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vllm_runner,
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example_prompts,
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model: str,
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dtype: str,
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max_tokens: int,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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for_loop_outputs = []
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for _ in range(10):
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for_loop_outputs.append(
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# using example_prompts index 1 instead of 0 since with 0 the
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# logprobs get really close and the test doesn't pass
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vllm_model.generate_greedy([example_prompts[1]], max_tokens)
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[0])
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sampling_params = SamplingParams(n=10,
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temperature=0.001,
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seed=0,
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max_tokens=max_tokens)
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n_lt_1_outputs = vllm_model.generate([example_prompts[1]],
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sampling_params)
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token_ids, texts = n_lt_1_outputs[0]
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n_lt_1_outputs = [(token_id, text)
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for token_id, text in zip(token_ids, texts)]
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check_outputs_equal(
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outputs_0_lst=n_lt_1_outputs,
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outputs_1_lst=for_loop_outputs,
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name_0="vllm_n_lt_1_outputs",
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name_1="vllm",
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)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["bfloat16"])
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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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dtype: str,
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max_tokens: int,
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) -> None:
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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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vllm_config = EngineArgs(model=model).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, dtype=dtype) 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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [20])
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def test_models_preemption_recompute(
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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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dtype: str,
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max_tokens: int,
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) -> None:
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# Tests that outputs are identical with and w/o preemtions (recompute)
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assert dtype == "float"
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with vllm_runner(model, dtype=dtype) as vllm_model:
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vllm_model.model.llm_engine.scheduler[
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0].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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vllm_model.model.llm_engine.scheduler[
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0].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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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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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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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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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try:
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with vllm_runner(model, dtype=dtype, max_num_seqs=10) 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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_state_cleanup(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is for verifying that the Hybrid state is cleaned up between
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# steps, If its not cleaned, an error would be expected.
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try:
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with vllm_runner(model, dtype=dtype) 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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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def test_multistep(
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vllm_runner,
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model: str,
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dtype: str,
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example_prompts,
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) -> None:
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# This test is verifying that multistep works correctly
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#on mamba-like models
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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_model.generate_greedy([example_prompts[0]] * 10, 1)
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [64])
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def test_multistep_correctness(vllm_runner, model: str, dtype: str,
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max_tokens: int, example_prompts) -> 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", MODELS)
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@pytest.mark.parametrize("dtype", ["float"])
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@pytest.mark.parametrize("max_tokens", [64])
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def test_hybrid_distributed_produces_identical_generation(
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vllm_runner, model: str, dtype: str, max_tokens: int,
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example_prompts) -> None:
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with vllm_runner(model, dtype=dtype, tensor_parallel_size=2) as vllm_model:
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vllm_outputs_tp_2 = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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with vllm_runner(model, dtype=dtype, tensor_parallel_size=1) as vllm_model:
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vllm_outputs_tp_1 = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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check_outputs_equal(
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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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