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6fd3e72da1
* bench command * bench command * bench command * bench command * bench command --------- Co-authored-by: K11OntheBoat <your_email@example.com>
486 lines
17 KiB
Python
486 lines
17 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 argparse
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import unittest
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from unittest.mock import MagicMock, patch
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try:
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import torch
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TORCH_AVAILABLE = True
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except (ImportError, NameError, AttributeError, OSError):
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TORCH_AVAILABLE = False
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from fastdeploy.benchmarks.datasets import SampleRequest
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from fastdeploy.benchmarks.throughput import (
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EngineArgs,
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add_cli_args,
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get_requests,
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main,
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run_fd,
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run_fd_chat,
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run_hf,
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validate_args,
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)
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class TestThroughput(unittest.TestCase):
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@patch("fastdeploy.LLM")
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def test_run_fd(self, mock_llm):
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mock_llm_instance = MagicMock()
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mock_llm.return_value = mock_llm_instance
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mock_llm_instance.generate.return_value = ["output1", "output2"]
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# Mock cfg.max_model_len
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mock_cfg = MagicMock()
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mock_cfg.max_model_len = 2048
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mock_llm_instance.llm_engine.cfg = mock_cfg
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requests = [
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SampleRequest(
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no=1, prompt="test prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
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)
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]
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engine_args = EngineArgs(model="test_model")
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elapsed_time, outputs = run_fd(requests, n=1, engine_args=engine_args)
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self.assertIsInstance(elapsed_time, float)
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self.assertEqual(len(outputs), 2)
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@patch("fastdeploy.LLM")
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def test_run_fd_chat(self, mock_llm):
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mock_llm_instance = MagicMock()
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mock_llm.return_value = mock_llm_instance
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mock_llm_instance.chat.return_value = ["chat output1", "chat output2"]
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# Mock cfg.max_model_len
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mock_cfg = MagicMock()
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mock_cfg.max_model_len = 2048
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mock_llm_instance.llm_engine.cfg = mock_cfg
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requests = [
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SampleRequest(
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no=1, prompt="test chat prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
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)
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]
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engine_args = EngineArgs(model="test_model")
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elapsed_time, outputs = run_fd_chat(requests, n=1, engine_args=engine_args)
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self.assertIsInstance(elapsed_time, float)
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self.assertEqual(len(outputs), 2)
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@unittest.skipIf(not TORCH_AVAILABLE, "PyTorch is not available")
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@patch("transformers.AutoModelForCausalLM.from_pretrained")
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@patch("transformers.AutoTokenizer.from_pretrained")
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def test_run_hf(self, mock_tokenizer, mock_model):
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mock_model_instance = MagicMock()
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mock_model.return_value = mock_model_instance
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mock_model_instance.generate.return_value = torch.tensor([[1, 2, 3]]) if TORCH_AVAILABLE else None
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mock_tokenizer_instance = MagicMock()
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mock_tokenizer.return_value = mock_tokenizer_instance
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mock_tokenizer_instance.pad_token = "pad"
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requests = [
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SampleRequest(
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no=1, prompt="test hf prompt", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None
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)
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]
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elapsed_time = run_hf(
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requests,
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model="test_model",
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tokenizer=mock_tokenizer_instance,
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n=1,
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max_batch_size=4,
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trust_remote_code=True,
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)
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self.assertIsInstance(elapsed_time, float)
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@patch("fastdeploy.benchmarks.datasets.RandomDataset")
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def test_get_requests(self, mock_dataset):
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mock_dataset_instance = MagicMock()
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mock_dataset.return_value = mock_dataset_instance
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mock_dataset_instance.sample.return_value = [
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SampleRequest(no=1, prompt="test1", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None),
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SampleRequest(no=2, prompt="test2", prompt_len=15, expected_output_len=25, history_QA=[], json_data=None),
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]
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args = argparse.Namespace(
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dataset_name="random",
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dataset_path=None,
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seed=42,
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input_len=10,
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output_len=20,
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num_prompts=2,
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hf_max_batch_size=4,
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lora_path=None,
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random_range_ratio=0.0,
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prefix_len=0,
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)
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tokenizer = MagicMock()
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tokenizer.vocab_size = 10000 # 设置合理的词汇表大小
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tokenizer.num_special_tokens_to_add.return_value = 0 # 设置特殊token数量
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requests = get_requests(args, tokenizer)
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self.assertEqual(len(requests), 2)
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def test_validate_args(self):
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# Test basic validation
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args = argparse.Namespace(
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backend="fastdeploy",
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dataset_name="random",
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dataset=None,
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dataset_path=None,
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input_len=10,
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output_len=20,
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tokenizer=None,
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model="test_model",
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hf_max_batch_size=None,
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trust_remote_code=False,
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quantization=None,
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)
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validate_args(args)
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self.assertEqual(args.tokenizer, "test_model")
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def test_add_cli_args(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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self.assertEqual(args.backend, "fastdeploy")
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self.assertEqual(args.dataset_name, "random")
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@patch("fastdeploy.benchmarks.throughput.run_fd")
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@patch("fastdeploy.benchmarks.throughput.get_requests")
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@patch("transformers.AutoTokenizer.from_pretrained")
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def test_main_fastdeploy(self, mock_tokenizer, mock_get_requests, mock_run_fd):
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mock_get_requests.return_value = [
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SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
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]
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mock_run_fd.return_value = (1.0, ["output1", "output2"])
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.dataset_path = None
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args.seed = 42
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args.input_len = 10
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args.output_len = 20
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args.num_prompts = 1
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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args.n = 1
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args.hf_max_batch_size = None
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args.trust_remote_code = False
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args.output_json = None
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args.disable_detokenize = False
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args.tensor_parallel_size = 1
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with patch("builtins.print") as mock_print:
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main(args)
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mock_print.assert_called()
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@unittest.skipIf(not TORCH_AVAILABLE, "PyTorch is not available")
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@patch("fastdeploy.benchmarks.throughput.run_hf")
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@patch("fastdeploy.benchmarks.throughput.get_requests")
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@patch("transformers.AutoTokenizer.from_pretrained")
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@patch("transformers.AutoModelForCausalLM.from_pretrained")
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def test_main_hf(self, mock_model, mock_tokenizer, mock_get_requests, mock_run_hf):
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mock_get_requests.return_value = [
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SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
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]
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mock_run_hf.return_value = 1.0
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "hf"
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args.dataset_name = "random"
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args.dataset_path = None
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args.seed = 42
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args.input_len = 10
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args.output_len = 20
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args.num_prompts = 1
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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args.n = 1
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args.hf_max_batch_size = 4
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args.trust_remote_code = True
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args.output_json = None
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args.disable_detokenize = False
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args.tensor_parallel_size = 1
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with patch("builtins.print") as mock_print:
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main(args)
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mock_print.assert_called()
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@patch("fastdeploy.benchmarks.throughput.run_fd_chat")
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@patch("fastdeploy.benchmarks.throughput.get_requests")
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@patch("transformers.AutoTokenizer.from_pretrained")
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def test_main_fastdeploy_chat(self, mock_tokenizer, mock_get_requests, mock_run_fd_chat):
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mock_get_requests.return_value = [
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SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
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]
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mock_run_fd_chat.return_value = (1.0, ["output1", "output2"])
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy-chat"
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args.dataset_name = "random"
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args.dataset_path = None
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args.seed = 42
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args.input_len = 10
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args.output_len = 20
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args.num_prompts = 1
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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args.n = 1
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args.hf_max_batch_size = None
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args.trust_remote_code = False
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args.output_json = None
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args.disable_detokenize = False
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args.tensor_parallel_size = 1
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with patch("builtins.print") as mock_print:
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main(args)
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mock_print.assert_called()
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@patch("builtins.open")
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@patch("json.dump")
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@patch("fastdeploy.benchmarks.throughput.run_fd")
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@patch("fastdeploy.benchmarks.throughput.get_requests")
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def test_main_with_output_json(self, mock_get_requests, mock_run_fd, mock_json_dump, mock_open):
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mock_get_requests.return_value = [
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SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
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]
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mock_run_fd.return_value = (1.0, ["output1", "output2"])
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.dataset_path = None
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args.seed = 42
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args.input_len = 10
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args.output_len = 20
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args.num_prompts = 1
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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args.n = 1
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args.hf_max_batch_size = None
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args.trust_remote_code = False
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args.output_json = "output.json"
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args.disable_detokenize = False
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args.tensor_parallel_size = 1
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main(args)
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mock_json_dump.assert_called()
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# 新增测试用例覆盖缺失的行
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def test_validate_args_with_lora(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy" # LoRA只支持vLLM后端
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args.dataset_name = "random"
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args.enable_lora = True
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args.lora_path = "/path/to/lora"
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args.input_len = 10
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args.output_len = 20
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_validate_args_with_hf_backend(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "hf"
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args.dataset_name = "random"
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args.hf_max_batch_size = 4
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args.input_len = 10
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args.output_len = 20
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_validate_args_with_quantization(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.quantization = "w4a8"
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args.input_len = 10
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args.output_len = 20
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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@patch("fastdeploy.benchmarks.throughput.write_to_json")
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@patch("fastdeploy.benchmarks.throughput.convert_to_pytorch_benchmark_format")
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def test_save_to_pytorch_benchmark_format(self, mock_convert, mock_write):
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args = argparse.Namespace(
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output_json="test.json",
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model="test_model",
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input_len=10,
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output_len=20,
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backend="fastdeploy",
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)
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results = {
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"elapsed_time": 1.0,
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"num_requests": 10,
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"total_num_tokens": 100,
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"requests_per_second": 10.0,
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"tokens_per_second": 100.0,
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}
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mock_convert.return_value = [{"metrics": {"requests_per_second": 10.0}}]
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from fastdeploy.benchmarks.throughput import save_to_pytorch_benchmark_format
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save_to_pytorch_benchmark_format(args, results)
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mock_write.assert_called()
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@patch("fastdeploy.benchmarks.throughput.run_fd")
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@patch("fastdeploy.benchmarks.throughput.get_requests")
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def test_main_with_disable_detokenize(self, mock_get_requests, mock_run_fd):
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mock_get_requests.return_value = [
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SampleRequest(no=1, prompt="test", prompt_len=10, expected_output_len=20, history_QA=[], json_data=None)
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]
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mock_run_fd.return_value = (1.0, ["output1", "output2"])
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.dataset_path = None
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args.seed = 42
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args.input_len = 10
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args.output_len = 20
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args.num_prompts = 1
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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args.n = 1
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args.hf_max_batch_size = None
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args.trust_remote_code = False
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args.output_json = None
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args.disable_detokenize = True
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args.tensor_parallel_size = 1
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with patch("builtins.print") as mock_print:
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main(args)
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mock_print.assert_called()
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def test_validate_args_with_random_range_ratio(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.random_range_ratio = 0.5
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args.input_len = 10
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args.output_len = 20
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_validate_args_with_prefix_len(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "random"
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args.prefix_len = 5
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args.input_len = 10
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args.output_len = 20
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_validate_args_with_eb_dataset(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy"
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args.dataset_name = "EB"
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args.dataset_path = "/path/to/eb"
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_validate_args_with_ebchat_dataset(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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args = parser.parse_args([])
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args.backend = "fastdeploy-chat"
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args.dataset_name = "EBChat"
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args.dataset_path = "/path/to/ebchat"
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args.tokenizer = "test_tokenizer"
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args.model = "test_model"
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validate_args(args)
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def test_add_cli_args_with_all_options(self):
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parser = argparse.ArgumentParser()
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add_cli_args(parser)
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# 使用parse_known_args避免未识别参数导致的SystemExit
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args, _ = parser.parse_known_args(
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[
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"--backend",
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"fastdeploy-chat",
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"--dataset-name",
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"EBChat",
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"--dataset-path",
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"/path/to/dataset",
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"--input-len",
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"10",
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"--output-len",
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"20",
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"--n",
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"2",
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"--num-prompts",
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"50",
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"--hf-max-batch-size",
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"4",
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"--output-json",
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"output.json",
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"--disable-detokenize",
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"--lora-path",
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"/path/to/lora",
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"--prefix-len",
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"5",
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"--random-range-ratio",
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"0.5",
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]
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)
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self.assertEqual(args.backend, "fastdeploy-chat")
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self.assertEqual(args.dataset_name, "EBChat")
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self.assertEqual(args.dataset_path, "/path/to/dataset")
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self.assertEqual(args.input_len, 10)
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self.assertEqual(args.output_len, 20)
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self.assertEqual(args.n, 2)
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self.assertEqual(args.num_prompts, 50)
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self.assertEqual(args.hf_max_batch_size, 4)
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self.assertEqual(args.output_json, "output.json")
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self.assertTrue(args.disable_detokenize)
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self.assertEqual(args.lora_path, "/path/to/lora")
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self.assertEqual(args.prefix_len, 5)
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self.assertEqual(args.random_range_ratio, 0.5)
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|
|
|
|
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if __name__ == "__main__":
|
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unittest.main()
|