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67e6d8c691
* Set prefix caching as default * Set prefix caching as default * Set prefix caching as default * skip dynamic load scene * fix kill bug * fix kill bug * fix kill bug * fix * fix * fix ci
73 lines
2.2 KiB
Python
73 lines
2.2 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import os
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import weakref
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import pytest
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from fastdeploy.entrypoints.llm import LLM
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bash_path = os.getenv("MODEL_PATH")
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FD_ENGINE_QUEUE_PORTS = [
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[9961, 9962],
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[9971, 9972],
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[9981, 9982],
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[9991, 9992],
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]
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FD_CACHE_QUEUE_PORT = int(os.getenv("FD_CACHE_QUEUE_PORT", 8333))
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FD_CACHE_QUEUE_PORTS = [FD_CACHE_QUEUE_PORT, FD_CACHE_QUEUE_PORT + 1, FD_CACHE_QUEUE_PORT + 2, FD_CACHE_QUEUE_PORT + 3]
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models = [
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"ernie-4_5-fake-w4a8-unpermuted",
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"ernie-4_5-fake-w4a8-permuted",
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"ernie-4_5-fake-w4afp8-unpermuted",
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"ernie-4_5-fake-w4afp8-permuted",
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]
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prompts = ["解释下“温故而知新"]
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@pytest.fixture(scope="module", params=models)
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def llm(request):
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"""LLM测试夹具"""
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model_path = os.path.join(bash_path, request.param)
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try:
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port_index = models.index(request.param) % len(FD_ENGINE_QUEUE_PORTS)
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llm_instance = LLM(
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model=model_path,
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tensor_parallel_size=1,
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data_parallel_size=2,
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max_model_len=8192,
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num_gpu_blocks_override=1024,
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engine_worker_queue_port=FD_ENGINE_QUEUE_PORTS[port_index],
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cache_queue_port=FD_CACHE_QUEUE_PORTS[port_index],
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load_choices="default",
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enable_expert_parallel=True,
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)
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yield weakref.proxy(llm_instance)
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except Exception as e:
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assert False, f"LLM initialization failed: {e}"
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@pytest.mark.timeout(60)
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def test_generation(llm):
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print(f"testing generation with model: {llm}")
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# topp_params = SamplingParams(temperature=0.1, top_p=0, max_tokens=20)
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# output = llm.generate(prompts=prompts, sampling_params=topp_params)
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# print(output)
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