mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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129 lines
3.5 KiB
Python
129 lines
3.5 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 os
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import sys
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import pytest
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.abspath(os.path.join(current_dir, ".."))
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if project_root not in sys.path:
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sys.path.insert(0, project_root)
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from tests.model_loader.utils import (
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check_tokens_id_and_text_close,
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form_model_get_output_topp0,
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get_paddle_model_path,
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run_with_timeout,
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)
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FD_ENGINE_QUEUE_PORT = int(os.getenv("FD_ENGINE_QUEUE_PORT", 8313))
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FD_CACHE_QUEUE_PORT = int(os.getenv("FD_CACHE_QUEUE_PORT", 8333))
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prompts = ["解释下“温故而知新", "Hello, how are you?"]
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model_param_map = {
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"ernie-4_5-21b-a3b-bf16-paddle": {
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"tensor_parallel_size": 2,
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"quantizations": [
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{
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"quant_type": "wint4",
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"env": {"FD_ENABLE_MODEL_LOAD_CACHE": "1"},
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}
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],
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}
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}
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params = []
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for model, cfg in model_param_map.items():
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for q in cfg["quantizations"]:
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if isinstance(q, dict):
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quant, backend, env = q["quant_type"], q.get("backend", "default"), q.get("env", {})
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else:
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quant, backend, env = q, "default", {}
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params.append(
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pytest.param(
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model,
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cfg.get("tensor_parallel_size", 1),
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cfg.get("max_model_len", 1024),
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quant,
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cfg.get("max_tokens", 32),
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env,
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marks=[pytest.mark.core_model],
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id=f"{model}.{quant}.{backend}",
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)
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)
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@pytest.mark.parametrize(
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"model_name_or_path,tensor_parallel_size,max_model_len,quantization,max_tokens,env",
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params,
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)
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def test_model_cache(
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fd_runner,
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model_name_or_path: str,
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tensor_parallel_size: int,
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max_model_len: int,
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max_tokens: int,
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quantization: str,
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env,
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monkeypatch,
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) -> None:
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model_path = get_paddle_model_path(model_name_or_path)
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fd_outputs_v1 = run_with_timeout(
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target=form_model_get_output_topp0,
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args=(
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fd_runner,
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model_path,
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tensor_parallel_size,
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max_model_len,
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max_tokens,
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quantization,
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"default_v1",
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FD_ENGINE_QUEUE_PORT,
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prompts,
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FD_CACHE_QUEUE_PORT,
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),
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)
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if env:
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for k, v in env.items():
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monkeypatch.setenv(k, v)
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fd_outputs_v1_with_cache = run_with_timeout(
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target=form_model_get_output_topp0,
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args=(
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fd_runner,
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model_path,
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tensor_parallel_size,
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max_model_len,
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max_tokens,
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quantization,
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"default_v1",
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FD_ENGINE_QUEUE_PORT,
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prompts,
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FD_CACHE_QUEUE_PORT,
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),
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)
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check_tokens_id_and_text_close(
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outputs_0_lst=fd_outputs_v1,
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outputs_1_lst=fd_outputs_v1_with_cache,
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name_0="default_v1 loader",
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name_1="default_v1 loader using cache",
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)
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