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Convert formatting to use ruff instead of yapf + isort (#26247)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-10-05 15:06:22 +01:00
committed by GitHub
parent 17edd8a807
commit d6953beb91
1508 changed files with 115244 additions and 94146 deletions
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406
tests/v1/distributed/test_internal_lb_dp.py
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@@ -31,66 +31,71 @@ class MultinodeInternalLBServerManager:
"""Manages multi-node data parallel vLLM server instances for internal
load balancer testing using --headless mode."""
def __init__(self,
model_name: str,
dp_size: int,
api_server_count: int,
base_server_args: list,
dp_per_node: int = 1,
tp_size: int = TP_SIZE):
def __init__(
self,
model_name: str,
dp_size: int,
api_server_count: int,
base_server_args: list,
dp_per_node: int = 1,
tp_size: int = TP_SIZE,
):
self.model_name = model_name
self.dp_size = dp_size
self.dp_per_node = dp_per_node
self.tp_size = tp_size
self.api_server_count = api_server_count
self.base_server_args = base_server_args
self.servers: list[Optional[tuple[RemoteOpenAIServer,
list[str]]]] = [None] * (dp_size //
dp_per_node)
self.servers: list[Optional[tuple[RemoteOpenAIServer, list[str]]]] = [None] * (
dp_size // dp_per_node
)
self.server_threads: list[threading.Thread] = []
def __enter__(self) -> list[tuple[RemoteOpenAIServer, list[str]]]:
"""Start all server instances for multi-node internal LB mode."""
for server_idx, rank in enumerate(
range(0, self.dp_size, self.dp_per_node)):
for server_idx, rank in enumerate(range(0, self.dp_size, self.dp_per_node)):
# Create server args for this specific rank
server_args = self.base_server_args.copy()
if rank == 0:
# Head node - runs API server and first DP rank
server_args.extend([
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_per_node),
"--tensor-parallel-size",
str(self.tp_size),
"--port",
"8000", # Single endpoint for all requests
"--api-server-count",
str(self.api_server_count),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
])
server_args.extend(
[
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_per_node),
"--tensor-parallel-size",
str(self.tp_size),
"--port",
"8000", # Single endpoint for all requests
"--api-server-count",
str(self.api_server_count),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
]
)
else:
# Secondary nodes - run in headless mode
server_args.extend([
"--headless",
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_per_node),
"--data-parallel-start-rank",
str(rank),
"--tensor-parallel-size",
str(self.tp_size),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
])
server_args.extend(
[
"--headless",
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_per_node),
"--data-parallel-start-rank",
str(rank),
"--tensor-parallel-size",
str(self.tp_size),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
]
)
# Use a thread to start each server to allow parallel initialization
def start_server(sidx: int, r: int, sargs: list[str]):
@@ -102,20 +107,19 @@ class MultinodeInternalLBServerManager:
sargs,
auto_port=False,
env_dict={
"VLLM_SERVER_DEV_MODE":
"1",
current_platform.device_control_env_var:
",".join(
str(
current_platform.
device_id_to_physical_device_id(i))
for i in range(r, r + gpus_per_node))
})
"VLLM_SERVER_DEV_MODE": "1",
current_platform.device_control_env_var: ",".join(
str(current_platform.device_id_to_physical_device_id(i))
for i in range(r, r + gpus_per_node)
),
},
)
server.__enter__()
if r == 0:
print(
f"Head node (rank {r}) started successfully with "
f"{self.api_server_count} API servers")
f"{self.api_server_count} API servers"
)
else:
print(f"Headless node (rank {r}) started successfully")
self.servers[sidx] = (server, sargs)
@@ -124,8 +128,9 @@ class MultinodeInternalLBServerManager:
traceback.print_exc()
raise
thread = threading.Thread(target=start_server,
args=(server_idx, rank, server_args))
thread = threading.Thread(
target=start_server, args=(server_idx, rank, server_args)
)
thread.start()
self.server_threads.append(thread)
@@ -157,19 +162,20 @@ class APIOnlyServerManager:
"""Manages API-only server (Node 0) and headless engines server (Node 1)
for testing separated API server and engine configuration."""
def __init__(self,
model_name: str,
dp_size: int,
api_server_count: int,
base_server_args: list,
tp_size: int = TP_SIZE):
def __init__(
self,
model_name: str,
dp_size: int,
api_server_count: int,
base_server_args: list,
tp_size: int = TP_SIZE,
):
self.model_name = model_name
self.dp_size = dp_size
self.tp_size = tp_size
self.api_server_count = api_server_count
self.base_server_args = base_server_args
self.servers: list[Optional[tuple[RemoteOpenAIServer,
list[str]]]] = [None] * 2
self.servers: list[Optional[tuple[RemoteOpenAIServer, list[str]]]] = [None] * 2
self.server_threads: list[threading.Thread] = []
def __enter__(self) -> list[tuple[RemoteOpenAIServer, list[str]]]:
@@ -177,38 +183,42 @@ class APIOnlyServerManager:
# Start API-only server (Node 0) - no engines, only API server
api_server_args = self.base_server_args.copy()
api_server_args.extend([
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
"0", # No engines on this node
"--tensor-parallel-size",
str(self.tp_size),
"--port",
"8000",
"--api-server-count",
str(self.api_server_count),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
])
api_server_args.extend(
[
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
"0", # No engines on this node
"--tensor-parallel-size",
str(self.tp_size),
"--port",
"8000",
"--api-server-count",
str(self.api_server_count),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
]
)
# Start headless engines server (Node 1) - all engines, no API server
engines_server_args = self.base_server_args.copy()
engines_server_args.extend([
"--headless",
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_size), # All engines on this node
"--tensor-parallel-size",
str(self.tp_size),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
])
engines_server_args.extend(
[
"--headless",
"--data-parallel-size",
str(self.dp_size),
"--data-parallel-size-local",
str(self.dp_size), # All engines on this node
"--tensor-parallel-size",
str(self.tp_size),
"--data-parallel-address",
"127.0.0.1",
"--data-parallel-rpc-port",
"13345",
]
)
# Use threads to start both servers in parallel
def start_api_server():
@@ -220,10 +230,13 @@ class APIOnlyServerManager:
env_dict={
"VLLM_SERVER_DEV_MODE": "1",
# No GPUs needed for API-only server
})
},
)
server.__enter__()
print(f"API-only server started successfully with "
f"{self.api_server_count} API servers")
print(
f"API-only server started successfully with "
f"{self.api_server_count} API servers"
)
self.servers[0] = (server, api_server_args)
except Exception as e:
print(f"Failed to start API-only server: {e}")
@@ -236,16 +249,17 @@ class APIOnlyServerManager:
engines_server_args,
auto_port=False,
env_dict={
current_platform.device_control_env_var:
",".join(
str(
current_platform.
device_id_to_physical_device_id(i))
for i in range(self.dp_size * self.tp_size))
})
current_platform.device_control_env_var: ",".join(
str(current_platform.device_id_to_physical_device_id(i))
for i in range(self.dp_size * self.tp_size)
)
},
)
server.__enter__()
print(f"Headless engines server started successfully with "
f"{self.dp_size} engines")
print(
f"Headless engines server started successfully with "
f"{self.dp_size} engines"
)
self.servers[1] = (server, engines_server_args)
except Exception as e:
print(f"Failed to start headless engines server: {e}")
@@ -301,11 +315,14 @@ def default_server_args():
@pytest.fixture(scope="module", params=[1, 4])
def server_manager(request, default_server_args):
api_server_count = request.param
server_manager = MultinodeInternalLBServerManager(MODEL_NAME, DP_SIZE,
api_server_count,
default_server_args,
DP_SIZE // NUM_NODES,
TP_SIZE)
server_manager = MultinodeInternalLBServerManager(
MODEL_NAME,
DP_SIZE,
api_server_count,
default_server_args,
DP_SIZE // NUM_NODES,
TP_SIZE,
)
with server_manager:
yield server_manager
@@ -320,8 +337,9 @@ def servers(server_manager):
def api_only_servers(request, default_server_args):
"""Fixture for API-only server + headless engines configuration."""
api_server_count = request.param
with APIOnlyServerManager(MODEL_NAME, DP_SIZE, api_server_count,
default_server_args, TP_SIZE) as server_list:
with APIOnlyServerManager(
MODEL_NAME, DP_SIZE, api_server_count, default_server_args, TP_SIZE
) as server_list:
yield server_list
@@ -335,8 +353,7 @@ async def client(servers: list[tuple[RemoteOpenAIServer, list[str]]]):
@pytest_asyncio.fixture
async def api_only_client(api_only_servers: list[tuple[RemoteOpenAIServer,
list[str]]]):
async def api_only_client(api_only_servers: list[tuple[RemoteOpenAIServer, list[str]]]):
"""Client fixture for API-only server configuration."""
# Connect to the API-only server (first server in the list)
api_server = api_only_servers[0][0]
@@ -360,16 +377,12 @@ def test_multinode_dp_server_info(server_manager):
# `n_reqs` is set so that there is a good chance each server
# receives at least one request
n_reqs = 2 * api_server_count * api_server_count
parallel_configs = [
_get_parallel_config(head_server) for _ in range(n_reqs)
]
parallel_configs = [_get_parallel_config(head_server) for _ in range(n_reqs)]
api_process_counts = [c["_api_process_count"] for c in parallel_configs]
api_process_ranks = [c["_api_process_rank"] for c in parallel_configs]
assert all(c == api_server_count
for c in api_process_counts), api_process_counts
assert all(0 <= r < api_server_count
for r in api_process_ranks), api_process_ranks
assert all(c == api_server_count for c in api_process_counts), api_process_counts
assert all(0 <= r < api_server_count for r in api_process_ranks), api_process_ranks
@pytest.mark.asyncio
@@ -377,17 +390,15 @@ def test_multinode_dp_server_info(server_manager):
"model_name",
[MODEL_NAME],
)
async def test_multinode_dp_completion(client: openai.AsyncOpenAI,
servers: list[tuple[RemoteOpenAIServer,
list[str]]],
model_name: str) -> None:
async def test_multinode_dp_completion(
client: openai.AsyncOpenAI,
servers: list[tuple[RemoteOpenAIServer, list[str]]],
model_name: str,
) -> None:
async def make_request():
completion = await client.completions.create(
model=model_name,
prompt="Hello, my name is",
max_tokens=5,
temperature=1.0)
model=model_name, prompt="Hello, my name is", max_tokens=5, temperature=1.0
)
assert completion.id is not None
assert completion.choices is not None and len(completion.choices) == 1
@@ -410,9 +421,7 @@ async def test_multinode_dp_completion(client: openai.AsyncOpenAI,
# Test single request
result = await make_request()
assert result is not None
print(
"Multi-node internal LB handled single completion request successfully"
)
print("Multi-node internal LB handled single completion request successfully")
await asyncio.sleep(0.5)
@@ -441,10 +450,14 @@ async def test_multinode_dp_completion(client: openai.AsyncOpenAI,
_, server_args = servers[0]
api_server_count = (
server_args.count('--api-server-count')
and server_args[server_args.index('--api-server-count') + 1] or 1)
print(f"Successfully completed multi-node internal LB test with "
f"{len(servers)} DP ranks (API server count: {api_server_count})")
server_args.count("--api-server-count")
and server_args[server_args.index("--api-server-count") + 1]
or 1
)
print(
f"Successfully completed multi-node internal LB test with "
f"{len(servers)} DP ranks (API server count: {api_server_count})"
)
# Check request balancing via Prometheus metrics
head_server = servers[0][0]
@@ -456,11 +469,11 @@ async def test_multinode_dp_completion(client: openai.AsyncOpenAI,
"model_name",
[MODEL_NAME],
)
async def test_multinode_dp_completion_streaming(client: openai.AsyncOpenAI,
servers: list[
tuple[RemoteOpenAIServer,
list[str]]],
model_name: str) -> None:
async def test_multinode_dp_completion_streaming(
client: openai.AsyncOpenAI,
servers: list[tuple[RemoteOpenAIServer, list[str]]],
model_name: str,
) -> None:
prompt = "What is an LLM?"
async def make_streaming_request():
@@ -474,11 +487,9 @@ async def test_multinode_dp_completion_streaming(client: openai.AsyncOpenAI,
single_output = single_completion.choices[0].text
# Perform the streaming request
stream = await client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True)
stream = await client.completions.create(
model=model_name, prompt=prompt, max_tokens=5, temperature=0.0, stream=True
)
chunks: list[str] = []
finish_reason_count = 0
last_chunk = None
@@ -489,23 +500,21 @@ async def test_multinode_dp_completion_streaming(client: openai.AsyncOpenAI,
last_chunk = chunk # Keep track of the last chunk
# finish reason should only return in the last block for OpenAI API
assert finish_reason_count == 1, (
"Finish reason should appear exactly once.")
assert last_chunk is not None, (
"Stream should have yielded at least one chunk.")
assert last_chunk.choices[
0].finish_reason == "length", "Finish reason should be 'length'."
assert finish_reason_count == 1, "Finish reason should appear exactly once."
assert last_chunk is not None, "Stream should have yielded at least one chunk."
assert last_chunk.choices[0].finish_reason == "length", (
"Finish reason should be 'length'."
)
# Check that the combined text matches the non-streamed version.
assert "".join(
chunks
) == single_output, "Streamed output should match non-streamed output."
assert "".join(chunks) == single_output, (
"Streamed output should match non-streamed output."
)
return True # Indicate success for this request
# Test single streaming request
result = await make_streaming_request()
assert result is not None
print(
"Multi-node internal LB handled single streaming request successfully")
print("Multi-node internal LB handled single streaming request successfully")
await asyncio.sleep(0.5)
@@ -535,10 +544,14 @@ async def test_multinode_dp_completion_streaming(client: openai.AsyncOpenAI,
_, server_args = servers[0]
api_server_count = (
server_args.count('--api-server-count')
and server_args[server_args.index('--api-server-count') + 1] or 1)
print(f"Successfully completed multi-node internal LB streaming test with "
f"{len(servers)} DP ranks (API server count: {api_server_count})")
server_args.count("--api-server-count")
and server_args[server_args.index("--api-server-count") + 1]
or 1
)
print(
f"Successfully completed multi-node internal LB streaming test with "
f"{len(servers)} DP ranks (API server count: {api_server_count})"
)
# Check request balancing via Prometheus metrics
head_server = servers[0][0]
@@ -551,17 +564,16 @@ async def test_multinode_dp_completion_streaming(client: openai.AsyncOpenAI,
[MODEL_NAME],
)
async def test_api_only_multinode_dp_completion(
api_only_client: openai.AsyncOpenAI,
api_only_servers: list[tuple[RemoteOpenAIServer,
list[str]]], model_name: str) -> None:
api_only_client: openai.AsyncOpenAI,
api_only_servers: list[tuple[RemoteOpenAIServer, list[str]]],
model_name: str,
) -> None:
"""Test API-only server with all engines on separate headless server."""
async def make_request():
completion = await api_only_client.completions.create(
model=model_name,
prompt="Hello, my name is",
max_tokens=5,
temperature=1.0)
model=model_name, prompt="Hello, my name is", max_tokens=5, temperature=1.0
)
assert completion.id is not None
assert completion.choices is not None and len(completion.choices) == 1
@@ -614,11 +626,14 @@ async def test_api_only_multinode_dp_completion(
api_server, api_server_args = api_only_servers[0]
api_server_count = (
api_server_args.count('--api-server-count')
and api_server_args[api_server_args.index('--api-server-count') + 1]
or 1)
print(f"Successfully completed API-only multi-node test with {DP_SIZE} "
f"engines on headless server (API server count: {api_server_count})")
api_server_args.count("--api-server-count")
and api_server_args[api_server_args.index("--api-server-count") + 1]
or 1
)
print(
f"Successfully completed API-only multi-node test with {DP_SIZE} "
f"engines on headless server (API server count: {api_server_count})"
)
# Check request balancing via Prometheus metrics
check_request_balancing(api_server, DP_SIZE)
@@ -630,9 +645,10 @@ async def test_api_only_multinode_dp_completion(
[MODEL_NAME],
)
async def test_api_only_multinode_dp_completion_streaming(
api_only_client: openai.AsyncOpenAI,
api_only_servers: list[tuple[RemoteOpenAIServer,
list[str]]], model_name: str) -> None:
api_only_client: openai.AsyncOpenAI,
api_only_servers: list[tuple[RemoteOpenAIServer, list[str]]],
model_name: str,
) -> None:
"""Test API-only server streaming with all engines on separate
headless server."""
prompt = "What is an LLM?"
@@ -648,11 +664,9 @@ async def test_api_only_multinode_dp_completion_streaming(
single_output = single_completion.choices[0].text
# Perform the streaming request
stream = await api_only_client.completions.create(model=model_name,
prompt=prompt,
max_tokens=5,
temperature=0.0,
stream=True)
stream = await api_only_client.completions.create(
model=model_name, prompt=prompt, max_tokens=5, temperature=0.0, stream=True
)
chunks: list[str] = []
finish_reason_count = 0
last_chunk = None
@@ -663,16 +677,15 @@ async def test_api_only_multinode_dp_completion_streaming(
last_chunk = chunk # Keep track of the last chunk
# finish reason should only return in the last block for OpenAI API
assert finish_reason_count == 1, (
"Finish reason should appear exactly once.")
assert last_chunk is not None, (
"Stream should have yielded at least one chunk.")
assert last_chunk.choices[
0].finish_reason == "length", "Finish reason should be 'length'."
assert finish_reason_count == 1, "Finish reason should appear exactly once."
assert last_chunk is not None, "Stream should have yielded at least one chunk."
assert last_chunk.choices[0].finish_reason == "length", (
"Finish reason should be 'length'."
)
# Check that the combined text matches the non-streamed version.
assert "".join(
chunks
) == single_output, "Streamed output should match non-streamed output."
assert "".join(chunks) == single_output, (
"Streamed output should match non-streamed output."
)
return True # Indicate success for this request
# Test single streaming request
@@ -707,11 +720,14 @@ async def test_api_only_multinode_dp_completion_streaming(
_, api_server_args = api_only_servers[0]
api_server_count = (
api_server_args.count('--api-server-count')
and api_server_args[api_server_args.index('--api-server-count') + 1]
or 1)
print(f"Successfully completed API-only streaming test with {DP_SIZE} "
f"engines on headless server (API server count: {api_server_count})")
api_server_args.count("--api-server-count")
and api_server_args[api_server_args.index("--api-server-count") + 1]
or 1
)
print(
f"Successfully completed API-only streaming test with {DP_SIZE} "
f"engines on headless server (API server count: {api_server_count})"
)
# Check request balancing via Prometheus metrics
api_server = api_only_servers[0][0]