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[CI]Fix broken CI (#1773)

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This PR fixed the broken CI. It require
https://github.com/vllm-project/vllm/pull/20900 merged first.

- vLLM version: v0.9.2
- vLLM main:
e8cc53af5e

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
This commit is contained in:
wangxiyuan
2025-07-15 00:54:20 +08:00
committed by GitHub
parent afcfe91dfa
commit 494b0f474f
6 changed files with 22 additions and 392 deletions
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7
tests/e2e/singlecard/core/ascend_scheduler/test_ascend_scheduler.py
View File

@@ -16,6 +16,7 @@ from vllm.v1.request import Request, RequestStatus
from vllm.v1.structured_output import StructuredOutputManager
from vllm_ascend.core.scheduler import AscendScheduler
from vllm_ascend.utils import vllm_version_is
EOS_TOKEN_ID = 50256
@@ -303,6 +304,8 @@ def test_stop_via_update_from_output():
req.num_computed_tokens = req.num_tokens
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
@@ -355,6 +358,8 @@ def test_stop_via_update_from_output():
req.num_computed_tokens = req.num_tokens
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
@@ -405,6 +410,8 @@ def test_stop_via_update_from_output():
req.num_computed_tokens = req.num_tokens
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],

259
tests/e2e/singlecard/test_prompt_embedding.py
View File

@@ -1,259 +0,0 @@
#
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
# Copyright 2023 The vLLM team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# This file is a part of the vllm-ascend project.
# Adapted from vllm/tests/entrypoints/openai/test_completion_with_prompt_embeds.py
#
import base64
import io
import os
import openai # use the official client for correctness check
import pytest
import pytest_asyncio
import torch
from modelscope import snapshot_download # type: ignore
from openai import BadRequestError
from transformers import AutoConfig
from vllm.engine.arg_utils import EngineArgs
from tests.utils import RemoteOpenAIServer
if not hasattr(EngineArgs, "enable_prompt_embeds"):
pytest.skip("Not supported vllm version", allow_module_level=True)
# any model with a chat template should work here
MODEL_NAME = snapshot_download("LLM-Research/Llama-3.2-1B-Instruct")
CONFIG = AutoConfig.from_pretrained(MODEL_NAME)
@pytest.fixture(scope="module")
def default_server_args() -> list[str]:
return [
# use half precision for speed and memory savings in CI environment
"--dtype",
"bfloat16",
"--max-model-len",
"8192",
"--max-num-seqs",
"128",
"--enforce-eager",
# Prompt Embeds server args
"--enable-prompt-embeds",
"--no-enable-chunked-prefill",
]
@pytest.fixture(scope="module",
params=["", "--disable-frontend-multiprocessing"])
def server_with_prompt_embeds(default_server_args, request):
if request.param:
default_server_args.append(request.param)
with RemoteOpenAIServer(MODEL_NAME, default_server_args) as remote_server:
yield remote_server
@pytest_asyncio.fixture
async def client_with_prompt_embeds(server_with_prompt_embeds):
async with server_with_prompt_embeds.get_async_client() as async_client:
yield async_client
def create_dummy_embeds(num_tokens: int = 5) -> str:
"""Create dummy embeddings and return them as base64 encoded string."""
dummy_embeds = torch.randn(num_tokens, CONFIG.hidden_size)
buffer = io.BytesIO()
torch.save(dummy_embeds, buffer)
return base64.b64encode(buffer.getvalue()).decode('utf-8')
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.skipif(
os.getenv("VLLM_USE_V1") == "1",
reason="Enable embedding input will fallback to v0, skip it")
async def test_completions_with_prompt_embeds(
client_with_prompt_embeds: openai.AsyncOpenAI, model_name: str):
# Test case: Single prompt embeds input
encoded_embeds = create_dummy_embeds()
completion = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": encoded_embeds})
assert len(completion.choices[0].text) >= 1
assert completion.choices[0].prompt_logprobs is None
# Test case: batch completion with prompt_embeds
encoded_embeds2 = create_dummy_embeds()
completion = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": [encoded_embeds, encoded_embeds2]})
assert len(completion.choices) == 2
assert len(completion.choices[0].text) >= 1
assert len(completion.choices[1].text) >= 1
# Test case: streaming with prompt_embeds
encoded_embeds = create_dummy_embeds()
single_completion = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": encoded_embeds})
single_output = single_completion.choices[0].text
stream = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
stream=True,
extra_body={"prompt_embeds": encoded_embeds})
chunks = []
finish_reason_count = 0
async for chunk in stream:
chunks.append(chunk.choices[0].text)
if chunk.choices[0].finish_reason is not None:
finish_reason_count += 1
assert finish_reason_count == 1
assert chunk.choices[0].finish_reason == "length"
assert chunk.choices[0].text
assert "".join(chunks) == single_output
# Test case: batch streaming with prompt_embeds
encoded_embeds2 = create_dummy_embeds()
stream = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
stream=True,
extra_body={"prompt_embeds": [encoded_embeds, encoded_embeds2]})
chunks_stream_embeds: list[list[str]] = [[], []]
finish_reason_count = 0
async for chunk in stream:
chunks_stream_embeds[chunk.choices[0].index].append(
chunk.choices[0].text)
if chunk.choices[0].finish_reason is not None:
finish_reason_count += 1
assert finish_reason_count == 2
assert chunk.choices[0].finish_reason == "length"
assert chunk.choices[0].text
assert len(chunks_stream_embeds[0]) > 0
assert len(chunks_stream_embeds[1]) > 0
# Test case: mixed text and prompt_embeds
encoded_embeds = create_dummy_embeds()
completion_mixed = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="This is a prompt",
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": encoded_embeds})
assert len(completion.choices) == 2
completion_text_only = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="This is a prompt",
max_tokens=5,
temperature=0.0,
)
completion_embeds_only = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="",
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": encoded_embeds})
# Embeddings responses should be handled first
assert completion_mixed.choices[0].text == completion_embeds_only.choices[
0].text
assert completion_mixed.choices[1].text == completion_text_only.choices[
0].text
@pytest.mark.asyncio
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.skipif(
os.getenv("VLLM_USE_V1") == "1",
reason="Enable embedding input will fallback to v0, skip it")
async def test_completions_errors_with_prompt_embeds(
client_with_prompt_embeds: openai.AsyncOpenAI, model_name: str):
# Test error case: invalid prompt_embeds
with pytest.raises(BadRequestError):
await client_with_prompt_embeds.completions.create(
prompt="",
model=model_name,
max_tokens=5,
temperature=0.0,
extra_body={"prompt_embeds": "invalid_base64"})
@pytest.mark.asyncio
@pytest.mark.parametrize("logprobs_arg", [1, 0])
@pytest.mark.parametrize("model_name", [MODEL_NAME])
@pytest.mark.skipif(
os.getenv("VLLM_USE_V1") == "1",
reason="Enable embedding input will fallback to v0, skip it")
async def test_completions_with_logprobs_and_prompt_embeds(
client_with_prompt_embeds: openai.AsyncOpenAI, logprobs_arg: int,
model_name: str):
# Test case: Logprobs using prompt_embeds
encoded_embeds = create_dummy_embeds()
completion = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
echo=False,
logprobs=logprobs_arg,
extra_body={"prompt_embeds": encoded_embeds})
logprobs = completion.choices[0].logprobs
assert logprobs is not None
assert len(logprobs.text_offset) == 5
assert len(logprobs.token_logprobs) == 5
assert len(logprobs.top_logprobs) == 5
for top_logprobs in logprobs.top_logprobs[1:]:
assert max(logprobs_arg, 1) <= len(top_logprobs) <= logprobs_arg + 1
assert len(logprobs.tokens) == 5
# Test case: Log probs with batch completion and prompt_embeds
encoded_embeds2 = create_dummy_embeds()
completion = await client_with_prompt_embeds.completions.create(
model=model_name,
prompt="", # Add empty prompt as required parameter
max_tokens=5,
temperature=0.0,
echo=False,
logprobs=logprobs_arg,
extra_body={"prompt_embeds": [encoded_embeds, encoded_embeds2]})
assert len(completion.choices) == 2
for choice in completion.choices:
logprobs = choice.logprobs
assert logprobs is not None
assert len(logprobs.text_offset) == 5
assert len(logprobs.token_logprobs) == 5
assert len(logprobs.top_logprobs) == 5
for top_logprobs in logprobs.top_logprobs[1:]:
assert max(logprobs_arg,
1) <= len(top_logprobs) <= logprobs_arg + 1
assert len(logprobs.tokens) == 5

7
tests/e2e/singlecard/test_scheduler.py
View File

@@ -31,6 +31,7 @@ from vllm.v1.request import Request, RequestStatus
from vllm.v1.structured_output import StructuredOutputManager
from vllm_ascend.core.scheduler import AscendScheduler
from vllm_ascend.utils import vllm_version_is
EOS_TOKEN_ID = 50256
@@ -213,6 +214,8 @@ def test_stop_via_update_from_output():
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
scheduler.scheduled_req_ids.add(req.request_id)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
@@ -263,6 +266,8 @@ def test_stop_via_update_from_output():
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
scheduler.scheduled_req_ids.add(req.request_id)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],
@@ -311,6 +316,8 @@ def test_stop_via_update_from_output():
scheduler.requests[req.request_id] = req
scheduler.running.append(req)
scheduler.scheduled_req_ids.add(req.request_id)
if not vllm_version_is("0.9.2"):
req.status = RequestStatus.RUNNING
scheduler_output = SchedulerOutput(scheduled_new_reqs=[],
scheduled_cached_reqs=[],