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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 15, 21:00 UTC

Fix entrypoint tests for embedding models (#14052)

Harry Mellor committed Feb 28, 2025 at 16:56 UTC 4be4b26cb728f0746cb0f9d7900b8a7f2988a647
1 file changed +11 -11
tests/entrypoints/openai/test_embedding.py
+11 -11
@@ -27,7 +27,7 @@ def server():
27 "bfloat16",
28 "--enforce-eager",
29 "--max-model-len",
30 - "8192",
30 + "512",
31 "--chat-template",
32 DUMMY_CHAT_TEMPLATE,
33 ]
@@ -60,10 +60,10 @@ async def test_single_embedding(client: openai.AsyncOpenAI, model_name: str):
60
61 assert embeddings.id is not None
62 assert len(embeddings.data) == 1
63 - assert len(embeddings.data[0].embedding) == 4096
63 + assert len(embeddings.data[0].embedding) == 384
64 assert embeddings.usage.completion_tokens == 0
65 - assert embeddings.usage.prompt_tokens == 9
66 - assert embeddings.usage.total_tokens == 9
65 + assert embeddings.usage.prompt_tokens == 11
66 + assert embeddings.usage.total_tokens == 11
67
68 # test using token IDs
69 input_tokens = [1, 1, 1, 1, 1]
@@ -77,7 +77,7 @@ async def test_single_embedding(client: openai.AsyncOpenAI, model_name: str):
77
78 assert embeddings.id is not None
79 assert len(embeddings.data) == 1
80 - assert len(embeddings.data[0].embedding) == 4096
80 + assert len(embeddings.data[0].embedding) == 384
81 assert embeddings.usage.completion_tokens == 0
82 assert embeddings.usage.prompt_tokens == 5
83 assert embeddings.usage.total_tokens == 5
@@ -101,10 +101,10 @@ async def test_batch_embedding(client: openai.AsyncOpenAI, model_name: str):
101
102 assert embeddings.id is not None
103 assert len(embeddings.data) == 3
104 - assert len(embeddings.data[0].embedding) == 4096
104 + assert len(embeddings.data[0].embedding) == 384
105 assert embeddings.usage.completion_tokens == 0
106 - assert embeddings.usage.prompt_tokens == 32
107 - assert embeddings.usage.total_tokens == 32
106 + assert embeddings.usage.prompt_tokens == 33
107 + assert embeddings.usage.total_tokens == 33
108
109 # test List[List[int]]
110 input_tokens = [[4, 5, 7, 9, 20], [15, 29, 499], [24, 24, 24, 24, 24],
@@ -119,7 +119,7 @@ async def test_batch_embedding(client: openai.AsyncOpenAI, model_name: str):
119
120 assert embeddings.id is not None
121 assert len(embeddings.data) == 4
122 - assert len(embeddings.data[0].embedding) == 4096
122 + assert len(embeddings.data[0].embedding) == 384
123 assert embeddings.usage.completion_tokens == 0
124 assert embeddings.usage.prompt_tokens == 17
125 assert embeddings.usage.total_tokens == 17
@@ -234,7 +234,7 @@ async def test_single_embedding_truncation(client: openai.AsyncOpenAI,
234
235 assert embeddings.id is not None
236 assert len(embeddings.data) == 1
237 - assert len(embeddings.data[0].embedding) == 4096
237 + assert len(embeddings.data[0].embedding) == 384
238 assert embeddings.usage.completion_tokens == 0
239 assert embeddings.usage.prompt_tokens == 10
240 assert embeddings.usage.total_tokens == 10
@@ -252,7 +252,7 @@ async def test_single_embedding_truncation(client: openai.AsyncOpenAI,
252
253 assert embeddings.id is not None
254 assert len(embeddings.data) == 1
255 - assert len(embeddings.data[0].embedding) == 4096
255 + assert len(embeddings.data[0].embedding) == 384
256 assert embeddings.usage.completion_tokens == 0
257 assert embeddings.usage.prompt_tokens == 10
258 assert embeddings.usage.total_tokens == 10