[Model] Support Mamba (#6484)
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Tyler Michael Smith
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tests/models/decoder_only/language/test_mamba.py
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295
tests/models/decoder_only/language/test_mamba.py
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"""Compare the outputs of HF and vLLM when using greedy sampling for Mamba.
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Run `pytest tests/models/test_mamba.py`.
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"""
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import pytest
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from vllm.sampling_params import SamplingParams
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from vllm.worker.model_runner import _get_graph_batch_size
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from ...utils import check_outputs_equal
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MODELS = ["state-spaces/mamba-130m-hf"]
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# Use lower-level interfaces to create this greedy generator, as mamba will
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# choke on the model_kwarg 'attention_mask' if hf_model.generate_greedy is used.
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def generate_greedy(model_name, example_prompts, max_tokens):
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# Create a text generation pipeline
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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# Generate texts from the prompts
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outputs = []
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for prompt in example_prompts:
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# Tokenize the input prompt with truncation
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inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
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input_ids = inputs["input_ids"].to(model.device)
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# Generate text using the model's generate method directly
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generated_ids = model.generate(input_ids, max_new_tokens=max_tokens)
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generated_text = tokenizer.decode(generated_ids[0],
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skip_special_tokens=True)
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outputs.append((generated_ids[0].tolist(), generated_text))
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return outputs
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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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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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hf_outputs = generate_greedy(model, 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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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", ["float"])
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@pytest.mark.parametrize("max_tokens", [10])
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def test_chunked_prefill_with_parallel_sampling(vllm_runner, example_prompts,
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model: str, dtype: str,
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max_tokens: int) -> None:
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# Tests chunked prefill in conjunction with n>1. In this case, prefill is
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# populated with decoding tokens and 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 (where we have both
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# prefill and decode 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", ["float"])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("chunked_prefill_token_size", [1, 4, 16])
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def test_chunked_prefill(vllm_runner, example_prompts, model: str, dtype: str,
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max_tokens: int,
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chunked_prefill_token_size: int) -> None:
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"""
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Checks exact match decode between huggingface model and vllm runner with
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chunked prefill.
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"""
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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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non_chunked = generate_greedy(model, 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=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(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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while len(example_prompts) == _get_graph_batch_size(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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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 Mamba 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 Mamba 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("Mamba 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 Mamba 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("Mamba 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_model_print(
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vllm_runner,
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model: str,
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dtype: str,
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) -> None:
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with vllm_runner(model, dtype=dtype) as vllm_model:
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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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print(vllm_model.model.llm_engine.model_executor.driver_worker.
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model_runner.model)
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