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https://github.com/PaddlePaddle/FastDeploy.git
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809c1ac7ec
* feat: add post-processing step for pool_output * bugfix * fix: test_serving_embedding * fix test_request_to_batch_dicts * fix: code style
112 lines
4.3 KiB
Python
112 lines
4.3 KiB
Python
import time
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import unittest
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from unittest.mock import AsyncMock, MagicMock
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from fastdeploy.engine.request import (
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PoolingOutput,
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PoolingRequestOutput,
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RequestMetrics,
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)
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from fastdeploy.entrypoints.openai.protocol import (
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EmbeddingChatRequest,
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EmbeddingCompletionRequest,
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EmbeddingRequest,
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EmbeddingResponse,
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)
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from fastdeploy.entrypoints.openai.serving_embedding import OpenAIServingEmbedding
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from fastdeploy.entrypoints.openai.serving_engine import ServeContext
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class TestOpenAIServingEmbedding(unittest.IsolatedAsyncioTestCase):
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def setUp(self):
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self.mock_engine_client = MagicMock()
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self.mock_engine_client.semaphore.acquire = AsyncMock()
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self.mock_engine_client.semaphore.release = MagicMock()
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self.mock_engine_client.check_model_weight_status = AsyncMock(return_value=False)
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mock_dealer = MagicMock()
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mock_response_queue = MagicMock()
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self.response_data: PoolingRequestOutput = PoolingRequestOutput(
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request_id="test_request_id",
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prompt_token_ids=[1, 2, 3],
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finished=True,
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outputs=PoolingOutput(data=[0.1, 0.2, 0.3]),
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metrics=RequestMetrics(arrival_time=time.time()),
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)
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mock_response_queue.get = AsyncMock(
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return_value=[
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self.response_data.to_dict(),
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]
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)
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self.mock_engine_client.connection_manager.get_connection = AsyncMock(
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return_value=(mock_dealer, mock_response_queue)
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)
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self.mock_engine_client.connection_manager.cleanup_request = AsyncMock()
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self.mock_engine_client.format_and_add_data = AsyncMock(return_value=[[1, 2, 3]])
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models = MagicMock()
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models.is_supported_model = MagicMock(return_value=(True, "ERNIE"))
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pid = 123
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ips = ["127.0.0.1"]
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max_waiting_time = 30
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chat_template = MagicMock()
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cfg = MagicMock()
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self.embedding_service = OpenAIServingEmbedding(
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self.mock_engine_client, models, cfg, pid, ips, max_waiting_time, chat_template
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)
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async def test_create_embedding_success(self):
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# Setup
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request = EmbeddingChatRequest(
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model="text-embedding-ada-002",
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messages=[
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{"role": "user", "content": "Hello"},
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{"role": "assistant", "content": "Hi there!"},
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{"role": "user", "content": "How are you?"},
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],
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)
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# Execute
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result: EmbeddingResponse = await self.embedding_service.create_embedding(request)
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# Assert
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self.assertEqual(result.data[0].embedding, self.response_data.outputs.data)
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def test_request_to_batch_dicts(self):
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test_cases = [
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("string input", EmbeddingCompletionRequest(input="hello"), ["hello"], ["req-1_0"]),
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("list of ints", EmbeddingCompletionRequest(input=[1, 2, 3]), [[1, 2, 3]], ["req-1_0"]),
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("list of strings", EmbeddingCompletionRequest(input=["a", "b"]), ["a", "b"], ["req-1_0", "req-1_1"]),
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(
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"list of list of ints",
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EmbeddingCompletionRequest(input=[[1, 2], [3, 4]]),
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[[1, 2], [3, 4]],
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["req-1_0", "req-1_1"],
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),
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]
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for name, request, expected_prompts, expected_ids in test_cases:
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with self.subTest(name=name):
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ctx = ServeContext[EmbeddingRequest](
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request=request,
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model_name="request.model",
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request_id="req-1",
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)
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result = self.embedding_service._request_to_batch_dicts(ctx)
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self.assertEqual(len(result), len(expected_prompts))
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for r, prompt, rid in zip(result, expected_prompts, expected_ids):
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# print(f"assertEqual r:{r} prompt:{prompt} rid:{rid}")
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self.assertEqual(r["prompt"], prompt)
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self.assertEqual(r["request_id"], rid)
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# 测试非 EmbeddingCompletionRequest 输入
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with self.subTest(name="non-embedding request"):
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with self.assertRaises(AttributeError):
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ctx = ServeContext(request={"foo": "bar"}, model_name="request.model", request_id="req-1")
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result = self.embedding_service._request_to_batch_dicts(ctx)
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if __name__ == "__main__":
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unittest.main()
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