d4d93db2c5
Signed-off-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: rshaw@neuralmagic.com <rshaw@neuralmagic.com> Co-authored-by: Nicolò Lucchesi <nlucches@redhat.com> Co-authored-by: Tyler Michael Smith <tyler@neuralmagic.com> Co-authored-by: Michael Goin <michael@neuralmagic.com>
103 lines
3.3 KiB
Python
103 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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"""Compare the outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/models/test_models.py`.
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"""
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import pytest
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from ...utils import check_logprobs_close
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# These have unsupported head_dim for FA. We do not
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# not have a clean way to fall back, so we fail with
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# a clear msg when it happens.
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# https://github.com/vllm-project/vllm/issues/14524
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REQUIRES_V0 = ["microsoft/phi-2", "stabilityai/stablelm-3b-4e1t"]
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@pytest.mark.parametrize(
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"model",
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[
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pytest.param(
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"bigscience/bloom-560m", # bloom - testing alibi slopes
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param(
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"openai-community/gpt2", # gpt2
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param("Milos/slovak-gpt-j-405M"), # gptj
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pytest.param("bigcode/tiny_starcoder_py"), # gpt_bigcode
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pytest.param("EleutherAI/pythia-70m"), # gpt_neox
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pytest.param(
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"google/gemma-1.1-2b-it", # gemma
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param(
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"THUDM/chatglm3-6b", # chatglm (text-only)
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),
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pytest.param(
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"meta-llama/Llama-3.2-1B-Instruct", # llama
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param(
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"openbmb/MiniCPM3-4B",
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# fused_moe not supported on CPU
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marks=[pytest.mark.core_model],
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),
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pytest.param(
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"facebook/opt-125m", # opt
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param(
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"microsoft/phi-2", # phi
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marks=[pytest.mark.core_model],
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),
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pytest.param(
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"Qwen/Qwen-7B", # qwen (text-only)
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),
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pytest.param(
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"Qwen/Qwen2.5-0.5B-Instruct", # qwen2
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marks=[pytest.mark.core_model],
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),
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pytest.param("stabilityai/stablelm-3b-4e1t"), # stablelm
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pytest.param("bigcode/starcoder2-3b"), # starcoder2
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pytest.param(
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"ehristoforu/Falcon3-MoE-2x7B-Insruct", # mixtral
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marks=[pytest.mark.cpu_model],
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)
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])
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@pytest.mark.parametrize("dtype", ["half"])
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@pytest.mark.parametrize("max_tokens", [32])
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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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dtype: str,
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max_tokens: int,
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num_logprobs: int,
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monkeypatch,
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) -> None:
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if model in REQUIRES_V0:
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monkeypatch.setenv("VLLM_USE_V1", "0")
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with hf_runner(model, dtype=dtype) as hf_model:
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if model.startswith("THUDM/chatglm3"):
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hf_model.model.get_output_embeddings = lambda: \
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hf_model.model.transformer.output_layer
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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, dtype=dtype) 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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