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3468f17ebe
Signed-off-by: Matthew Bonanni <mbonanni@redhat.com> Signed-off-by: Matthew Bonanni <mbonanni001@gmail.com>
63 lines
2.4 KiB
Python
63 lines
2.4 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""Test whether spec decoding handles the max model length properly."""
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import pytest
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from tests.utils import get_attn_backend_list_based_on_platform
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from vllm import LLM, SamplingParams
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from vllm.platforms import current_platform
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_PROMPTS = [
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"1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1",
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"Repeat the following sentence 10 times: Consistency is key to mastering any skill.", # noqa: E501
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"Who won the Turing Award in 2018, and for what contribution? Describe in detail.", # noqa: E501
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]
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@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
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def test_ngram_max_len(num_speculative_tokens: int):
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llm = LLM(
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model="facebook/opt-125m",
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max_model_len=100,
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enforce_eager=True, # For faster initialization.
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speculative_config={
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"method": "ngram",
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"prompt_lookup_max": 5,
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"prompt_lookup_min": 3,
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"num_speculative_tokens": num_speculative_tokens,
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},
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)
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sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
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llm.generate(_PROMPTS, sampling_params)
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@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 10])
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@pytest.mark.parametrize("attn_backend",
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get_attn_backend_list_based_on_platform())
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def test_eagle_max_len(monkeypatch: pytest.MonkeyPatch,
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num_speculative_tokens: int, attn_backend: str):
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_V1", "1")
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m.setenv("VLLM_ATTENTION_BACKEND", attn_backend)
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if (attn_backend == "TRITON_ATTN" and not current_platform.is_rocm()):
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pytest.skip("TRITON_ATTN does not support "
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"multi-token eagle spec decode on current platform")
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if attn_backend == "FLASH_ATTN" and current_platform.is_rocm():
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m.setenv("VLLM_ROCM_USE_AITER", "1")
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llm = LLM(
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model="meta-llama/Meta-Llama-3-8B-Instruct",
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enforce_eager=True, # For faster initialization.
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speculative_config={
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"method": "eagle",
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"model": "yuhuili/EAGLE-LLaMA3-Instruct-8B",
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"num_speculative_tokens": num_speculative_tokens,
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},
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max_model_len=100,
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)
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sampling_params = SamplingParams(max_tokens=100, ignore_eos=True)
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llm.generate(_PROMPTS, sampling_params)
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