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132 lines
4.5 KiB
Python
132 lines
4.5 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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import torch
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from vllm.platforms import current_platform
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from ...registry import HF_EXAMPLE_MODELS
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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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# This list contains the model that are using AITER kernel.
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# Skip model that are not using AITER tests.
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# When more AITER kernels are added, this list will not be
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# needed as all the models will be calling AITER kernels
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# in parts of the operators
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AITER_MODEL_LIST = [
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"meta-llama/Llama-3.2-1B-Instruct",
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"openbmb/MiniCPM3-4B",
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"Qwen/Qwen-7B",
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"Qwen/Qwen2.5-0.5B-Instruct",
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"ehristoforu/Falcon3-MoE-2x7B-Insruct",
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]
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# @maybe_test_rocm_aiter
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@pytest.mark.parametrize(
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"model_arch",
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[
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pytest.param(
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"BloomForCausalLM", # 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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"GPT2LMHeadModel", # gpt2
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param("GPTJForCausalLM"),
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pytest.param("GPTBigCodeForCausalLM"),
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pytest.param("GPTNeoXForCausalLM"),
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pytest.param(
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"GemmaForCausalLM", # gemma
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marks=[pytest.mark.core_model, pytest.mark.cpu_model],
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),
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pytest.param("GlmForCausalLM"),
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pytest.param(
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"LlamaForCausalLM",
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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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"MiniCPM3ForCausalLM",
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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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"OPTForCausalLM",
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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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"PhiForCausalLM",
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marks=[pytest.mark.core_model],
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),
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pytest.param("QWenLMHeadModel", ),
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pytest.param(
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"Qwen2ForCausalLM",
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marks=[pytest.mark.core_model],
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),
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pytest.param("StableLmForCausalLM"),
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pytest.param("Starcoder2ForCausalLM"),
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pytest.param(
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"MixtralForCausalLM",
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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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@pytest.mark.parametrize(
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"use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
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def test_models(hf_runner, vllm_runner, example_prompts, model_arch: str,
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dtype: str, max_tokens: int, num_logprobs: int,
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use_rocm_aiter: bool, monkeypatch) -> None:
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model = HF_EXAMPLE_MODELS.get_hf_info(model_arch).default
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if model in REQUIRES_V0:
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monkeypatch.setenv("VLLM_USE_V1", "0")
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if use_rocm_aiter and (model in AITER_MODEL_LIST):
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monkeypatch.setenv("VLLM_ROCM_USE_AITER", "1")
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elif use_rocm_aiter and model not in AITER_MODEL_LIST:
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# Skip model that are not using AITER tests.
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# When more AITER kernels are added, this list will not be
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# needed as all the models will be calling AITER kernels
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# in parts of the operators
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pytest.skip(f"Skipping '{model}' model test with AITER kernel.")
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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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if use_rocm_aiter:
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# this is to ensure that vllm engine
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# has deallocated the memory before running the next
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# unit tests. On ROCm, when using AITER
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# the memory might not be deallocated completely
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# before running the next test case
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torch.cuda.synchronize()
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