mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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775edcc09a
* add start intercept * Adjustment GraphOptConfig * pre-commit * default use cudagraph * set default value * default use cuda graph * pre-commit * fix test case bug * disable rl * fix moba attention * only support gpu * Temporarily disable PD Disaggregation * set max_num_seqs of test case as 1 * set max_num_seqs and temperature * fix max_num_batched_tokens bug * close cuda graph * success run wint2 * profile run with max_num_batched_tokens * 1.add c++ memchecker 2.success run wint2 * updatee a800 yaml * update docs * 1. delete check 2. fix plas attn test case * default use use_unique_memory_pool * add try-except for warmup * ban mtp, mm, rl * fix test case mock * fix ci bug * fix form_model_get_output_topp0 bug * fix ci bug * refine deepseek ci * refine code * Disable PD * fix sot yaml
87 lines
2.7 KiB
Python
87 lines
2.7 KiB
Python
# 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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import time
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from typing import Any, Union
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import pytest
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from model_loader.utils import clean_ports
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class FDRunner:
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def __init__(
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self,
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model_name_or_path: str,
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tensor_parallel_size: int = 1,
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max_num_seqs: int = 1,
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max_model_len: int = 1024,
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load_choices: str = "default",
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quantization: str = "None",
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**kwargs,
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) -> None:
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from fastdeploy.entrypoints.llm import LLM
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ports_to_clean = []
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if "engine_worker_queue_port" in kwargs:
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ports_to_clean.append(kwargs["engine_worker_queue_port"])
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clean_ports(ports_to_clean)
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time.sleep(5)
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graph_optimization_config = {"use_cudagraph": False}
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self.llm = LLM(
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model=model_name_or_path,
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tensor_parallel_size=tensor_parallel_size,
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max_num_seqs=max_num_seqs,
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max_model_len=max_model_len,
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load_choices=load_choices,
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quantization=quantization,
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max_num_batched_tokens=max_model_len,
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graph_optimization_config=graph_optimization_config,
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**kwargs,
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)
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def generate(
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self,
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prompts: list[str],
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sampling_params,
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**kwargs: Any,
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) -> list[tuple[list[list[int]], list[str]]]:
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req_outputs = self.llm.generate(prompts, sampling_params=sampling_params, **kwargs)
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outputs: list[tuple[list[list[int]], list[str]]] = []
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for output in req_outputs:
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outputs.append((output.outputs.token_ids, output.outputs.text))
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return outputs
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def generate_topp0(
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self,
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prompts: Union[list[str]],
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max_tokens: int,
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**kwargs: Any,
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) -> list[tuple[list[int], str]]:
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from fastdeploy.engine.sampling_params import SamplingParams
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topp_params = SamplingParams(temperature=0.0, top_p=0, max_tokens=max_tokens)
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outputs = self.generate(prompts, topp_params, **kwargs)
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return outputs
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def __enter__(self):
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return self
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def __exit__(self, exc_type, exc_value, traceback):
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del self.llm
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@pytest.fixture(scope="session")
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def fd_runner():
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return FDRunner
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