[Frontend] Remove frontend pooling multi task support. (#37861)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: Cyrus Leung <cyrus.tl.leung@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>

tests/models/language/pooling/test_all_pooling_plus_chunked_prefill.py

@@ -6,6 +6,7 @@ from transformers import AutoModel
 
 from tests.models.utils import check_embeddings_close
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -21,6 +22,7 @@ def test_embed_models(hf_runner, vllm_runner, model: str):
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=128,
         max_num_batched_tokens=chunk_size,
         enforce_eager=True,

tests/models/language/pooling/test_bge_m3.py

@@ -3,7 +3,6 @@
 import httpx
 import openai
 import pytest
-import pytest_asyncio
 import torch
 
 from ....utils import RemoteOpenAIServer
@@ -25,29 +24,42 @@ sentences_2 = [
 similarity_reference = [[0.6259, 0.3474], [0.3309, 0.6734]]
 lexical_score_reference = [0.19554901123046875, 0.0]
 colbert_score_reference = [0.7797, 0.4620]
+SUPPORTED_TASKS = ["embed", "token_embed", "token_classify"]
+
+
+@pytest.fixture(scope="module", params=SUPPORTED_TASKS)
+def pooling_task(request):
+    yield request.param
 
 
 @pytest.fixture(scope="module")
-def server():
+def server(pooling_task):
     args = [
         "--max-model-len",
         str(MAX_MODEL_LEN),
         "--hf-overrides",
         '{"architectures": ["BgeM3EmbeddingModel"]}',
+        "--pooler-config.task",
+        pooling_task,
     ]
 
     with RemoteOpenAIServer(MODEL_NAME, args) as remote_server:
         yield remote_server
 
 
-@pytest_asyncio.fixture
-async def client(server):
-    async with server.get_async_client() as async_client:
-        yield async_client
-
-
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "embed":
+        with pytest.raises(openai.InternalServerError):
+            await run_client_embeddings(
+                client,
+                MODEL_NAME,
+                sentences_1,
+            )
+        return
+
     embeddings_list_1 = await run_client_embeddings(
         client,
         MODEL_NAME,
@@ -117,7 +129,14 @@ def compute_lexical_matching_score(
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_sparse_embedding(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_classify":
+        with pytest.raises(openai.BadRequestError):
+            await sparse_embeddings(client, sentences_1)
+        return
+
     embeddings_1 = await sparse_embeddings(client, sentences_1)
     embeddings_2 = await sparse_embeddings(client, sentences_2)
 
@@ -137,9 +156,11 @@ async def test_bge_m3_api_server_sparse_embedding(client: openai.AsyncOpenAI):
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_sparse_embedding_corner_case(
-    client: openai.AsyncOpenAI,
-):
+async def test_bge_m3_api_server_sparse_embedding_corner_case(server, pooling_task):
+    if pooling_task != "token_classify":
+        return
+
+    client = server.get_async_client()
     embeddings = await sparse_embeddings(client, ["Hi"])
     assert len(embeddings) == 1
     assert 2673 in embeddings[0]
@@ -155,7 +176,18 @@ def colbert_score(q_reps: torch.Tensor, p_reps: torch.Tensor) -> torch.Tensor:
 
 
 @pytest.mark.asyncio
-async def test_bge_m3_api_server_multi_vector(client: openai.AsyncOpenAI):
+async def test_bge_m3_api_server_multi_vector(server, pooling_task):
+    client = server.get_async_client()
+
+    if pooling_task != "token_embed":
+        with pytest.raises(openai.BadRequestError):
+            await client.post(
+                "../pooling",
+                body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},
+                cast_to=httpx.Response,
+            )
+        return
+
     result_1 = await client.post(
         "../pooling",
         body={"model": MODEL_NAME, "input": sentences_1, "task": "token_embed"},

tests/models/language/pooling/test_extract_hidden_states.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 
 from vllm import TokensPrompt
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -20,6 +21,7 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
         max_model_len=128,
         enforce_eager=True,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         enable_prefix_caching=True,
     ) as vllm_model:
         pooling_outputs = vllm_model.llm.encode(
@@ -44,14 +46,3 @@ def test_extract_hidden_states(hf_runner, vllm_runner, model: str):
             assert len(output.prompt_token_ids) == n
             assert len(output.outputs.data) == n
             assert output.num_cached_tokens == 0
-
-        # skip_reading_prefix_cache can still write to cache
-        # to accelerate following requests
-        pooling_outputs = vllm_model.llm.encode(
-            [TokensPrompt(prompt_token_ids=t) for t in token_prompts],
-            pooling_task="embed",
-        )
-
-        for n, output in zip(n_prompt_tokens, pooling_outputs):
-            assert len(output.prompt_token_ids) == n
-            assert output.num_cached_tokens > 0

tests/models/language/pooling/test_multi_vector_retrieval.py

@@ -5,6 +5,7 @@ import torch
 from transformers import AutoModel
 
 from tests.models.utils import check_embeddings_close
+from vllm.config import PoolerConfig
 
 
 @pytest.mark.parametrize(
@@ -17,6 +18,7 @@ def test_embed_models(hf_runner, vllm_runner, example_prompts, model: str, dtype
     with vllm_runner(
         model,
         runner="pooling",
+        pooler_config=PoolerConfig(task="token_embed"),
         max_model_len=None,
     ) as vllm_model:
         vllm_outputs = vllm_model.token_embed(example_prompts)

tests/models/language/pooling/test_pooler_config_init_behaviour.py

@@ -146,7 +146,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=False),
+        pooler_config=PoolerConfig(use_activation=False, task="token_embed"),
     ) as vllm_model:
         wo_normalize = vllm_model.token_embed(example_prompts)
 
@@ -154,7 +154,7 @@ def test_multi_vector_retrieval_models_using_normalize(
         model,
         max_model_len=512,
         dtype=dtype,
-        pooler_config=PoolerConfig(use_activation=True),
+        pooler_config=PoolerConfig(use_activation=True, task="token_embed"),
     ) as vllm_model:
         w_normalize = vllm_model.token_embed(example_prompts)