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Commit d2b52805 authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.10.2rc1' into v0.10.2rc1-ori

parents 9a521c23 5438967f
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Showing with 665 additions and 198 deletions
tests/v1/engine/test_output_processor.py
View file @ d2b52805
......@@ -52,11 +52,9 @@ def test_incremental_detokenization(request_output_kind: RequestOutputKind,
requests = [
EngineCoreRequest(request_id=f"request-{idx}",
prompt_token_ids=prompt_tokens,
arrival_time=0,
mm_kwargs=None,
mm_hashes=None,
mm_placeholders=None,
mm_features=None,
eos_token_id=None,
arrival_time=0,
lora_request=None,
cache_salt=None,
data_parallel_rank=None,
......@@ -401,11 +399,9 @@ def test_logprobs_processor(request_output_kind: RequestOutputKind,
requests = [
EngineCoreRequest(request_id=request_id_list[idx],
prompt_token_ids=prompt_tokens,
arrival_time=0,
mm_kwargs=None,
mm_hashes=None,
mm_placeholders=None,
mm_features=None,
eos_token_id=None,
arrival_time=0,
lora_request=None,
cache_salt=None,
data_parallel_rank=None,
......@@ -566,11 +562,9 @@ def test_stop_token(include_stop_str_in_output: bool,
request = EngineCoreRequest(
request_id=request_id,
prompt_token_ids=prompt_tokens,
arrival_time=0,
mm_kwargs=None,
mm_hashes=None,
mm_placeholders=None,
mm_features=None,
eos_token_id=eos_token_id,
arrival_time=0,
lora_request=None,
cache_salt=None,
data_parallel_rank=None,
......@@ -665,11 +659,9 @@ def test_stop_string(include_stop_str_in_output: bool,
EngineCoreRequest(
request_id=request_id_list[idx],
prompt_token_ids=prompt_tokens,
arrival_time=0,
mm_kwargs=None,
mm_hashes=None,
mm_placeholders=None,
mm_features=None,
eos_token_id=None,
arrival_time=0,
lora_request=None,
cache_salt=None,
data_parallel_rank=None,
......@@ -781,11 +773,9 @@ def test_iteration_stats(dummy_test_vectors):
EngineCoreRequest(
request_id=f"request-{idx}",
prompt_token_ids=prompt_tokens,
arrival_time=0,
mm_kwargs=None,
mm_hashes=None,
mm_placeholders=None,
mm_features=None,
eos_token_id=None,
arrival_time=0,
lora_request=None,
cache_salt=None,
data_parallel_rank=None,
......
tests/v1/engine/test_processor_multi_modal_uuids.py 0 → 100644
View file @ d2b52805
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import pytest
from vllm.assets.image import ImageAsset
from vllm.assets.video import VideoAsset
from vllm.config import CacheConfig, DeviceConfig, ModelConfig, VllmConfig
from vllm.platforms.interface import UnspecifiedPlatform
from vllm.sampling_params import SamplingParams
from vllm.v1.engine import processor as processor_mod
from vllm.v1.engine.processor import Processor
cherry_pil_image = ImageAsset("cherry_blossom").pil_image
stop_pil_image = ImageAsset("stop_sign").pil_image
baby_reading_np_ndarrays = VideoAsset("baby_reading").np_ndarrays
# Mock processor for testing
def _mk_processor(monkeypatch,
*,
mm_cache_gb: float = 4.0,
enable_prefix_caching: bool = True) -> Processor:
"""
Create a Processor instance with minimal configuration suitable for unit
tests without accessing external resources.
"""
monkeypatch.setattr(ModelConfig,
"try_get_generation_config",
lambda self: {},
raising=True)
monkeypatch.setattr(ModelConfig,
"__post_init__",
lambda self: None,
raising=True)
monkeypatch.setattr(UnspecifiedPlatform,
"is_async_output_supported",
classmethod(lambda cls, enforce_eager: True),
raising=True)
monkeypatch.setattr(
ModelConfig,
"verify_async_output_proc",
lambda self, parallel_config, speculative_config, device_config: None,
raising=True)
monkeypatch.setattr(ModelConfig,
"verify_with_parallel_config",
lambda self, parallel_config: None,
raising=True)
monkeypatch.setattr(processor_mod,
"processor_cache_from_config",
lambda vllm_config, mm_registry: None,
raising=True)
monkeypatch.setattr(VllmConfig,
"__post_init__",
lambda self: None,
raising=True)
model_config = ModelConfig(
skip_tokenizer_init=True,
max_model_len=128,
mm_processor_cache_gb=mm_cache_gb,
generation_config="vllm",
tokenizer="dummy",
)
# Minimal multimodal_config to satisfy references in
# Processor.process_inputs.
class _MockMMConfig:
def __init__(self, gb: float):
self.mm_processor_cache_gb = gb
model_config.multimodal_config = _MockMMConfig(
mm_cache_gb) # type: ignore[attr-defined]
vllm_config = VllmConfig(
model_config=model_config,
cache_config=CacheConfig(enable_prefix_caching=enable_prefix_caching),
device_config=DeviceConfig(device="cpu"),
)
# Pass tokenizer=None; InputPreprocessor handles None when
# skip_tokenizer_init is True.
return Processor(vllm_config, tokenizer=None) # type: ignore[arg-type]
def test_multi_modal_uuids_length_mismatch_raises(monkeypatch):
processor = _mk_processor(monkeypatch)
prompt = {
"prompt": "USER: <image>\nDescribe\nASSISTANT:",
"multi_modal_data": {
"image": [cherry_pil_image, stop_pil_image]
},
# Mismatch: 2 items but only 1 uuid provided
"multi_modal_uuids": {
"image": ["hash_cherry"]
},
}
with pytest.raises(ValueError, match="must have same length as data"):
processor.process_inputs(
request_id="req-1",
prompt=prompt, # type: ignore[arg-type]
params=SamplingParams(),
)
def test_multi_modal_uuids_missing_modality_raises(monkeypatch):
processor = _mk_processor(monkeypatch)
prompt = {
"prompt": "USER: <image><video>\nDescribe\nASSISTANT:",
# Two modalities provided in data
"multi_modal_data": {
"image": [cherry_pil_image],
"video": [baby_reading_np_ndarrays]
},
# Only image uuids provided; video missing should raise
"multi_modal_uuids": {
"image": ["hash_cherry"]
},
}
with pytest.raises(ValueError,
match="must be provided if multi_modal_data"):
processor.process_inputs(
request_id="req-2",
prompt=prompt, # type: ignore[arg-type]
params=SamplingParams(),
)
@pytest.mark.parametrize(
"mm_cache_gb, enable_prefix_caching",
[
(4.0, True), # default behavior
(4.0, False), # prefix caching disabled
(0.0, True), # processor cache disabled
],
)
def test_multi_modal_uuids_accepts_none_and_passes_through(
monkeypatch, mm_cache_gb: float, enable_prefix_caching: bool):
processor = _mk_processor(monkeypatch,
mm_cache_gb=mm_cache_gb,
enable_prefix_caching=enable_prefix_caching)
# Capture the overrides passed to InputPreprocessor.preprocess
captured: dict[str, object] = {}
def fake_preprocess(prompt,
*,
tokenization_kwargs=None,
lora_request=None,
mm_hash_overrides=None):
captured["mm_hash_overrides"] = mm_hash_overrides
# Minimal processed inputs for decoder-only flow
return {"type": "token", "prompt_token_ids": [1]}
# Monkeypatch only the bound preprocess method on this instance
monkeypatch.setattr(processor.input_preprocessor,
"preprocess",
fake_preprocess,
raising=True)
# Use a consistent two-image scenario across all configurations
mm_uuids = {"image": [None, "hash_stop"], "video": None}
prompt = {
"prompt": "USER: <image><image>\nTwo images\nASSISTANT:",
"multi_modal_data": {
"image": [cherry_pil_image, stop_pil_image],
"video": baby_reading_np_ndarrays,
},
"multi_modal_uuids": mm_uuids,
}
processor.process_inputs(
request_id="req-3",
prompt=prompt, # type: ignore[arg-type]
params=SamplingParams(),
)
assert captured["mm_hash_overrides"] == mm_uuids
def test_multi_modal_uuids_ignored_when_caching_disabled(monkeypatch):
# When both processor cache is 0 and prefix caching disabled, the
# processor builds overrides from request id instead of using user UUIDs.
processor = _mk_processor(monkeypatch,
mm_cache_gb=0.0,
enable_prefix_caching=False)
captured: dict[str, object] = {}
def fake_preprocess(prompt,
*,
tokenization_kwargs=None,
lora_request=None,
mm_hash_overrides=None):
captured["mm_hash_overrides"] = mm_hash_overrides
return {"type": "token", "prompt_token_ids": [1]}
monkeypatch.setattr(processor.input_preprocessor,
"preprocess",
fake_preprocess,
raising=True)
request_id = "req-42"
mm_uuids = {"image": ["hash_cherry", "hash_stop"], "video": "hash_video"}
prompt = {
"prompt": "USER: <image><image><video>\nDescribe\nASSISTANT:",
"multi_modal_data": {
"image": [cherry_pil_image, stop_pil_image],
"video": baby_reading_np_ndarrays,
},
"multi_modal_uuids": mm_uuids,
}
processor.process_inputs(
request_id=request_id,
prompt=prompt, # type: ignore[arg-type]
params=SamplingParams(),
)
# Expect request-id-based overrides are passed through
assert captured["mm_hash_overrides"] == {
"image": [f"{request_id}-image-0", f"{request_id}-image-1"],
"video": [f"{request_id}-video-0"],
}
tests/v1/entrypoints/llm/test_struct_output_generate.py
View file @ d2b52805
......@@ -11,9 +11,11 @@ from typing import TYPE_CHECKING, Any
import jsonschema
import pytest
import regex as re
import torch
from pydantic import BaseModel
from tests.reasoning.utils import run_reasoning_extraction
from vllm.distributed import cleanup_dist_env_and_memory
from vllm.entrypoints.llm import LLM
from vllm.outputs import RequestOutput
from vllm.platforms import current_platform
......@@ -39,8 +41,11 @@ EAGLE_SPEC_CONFIG = {
PARAMS_MODELS_BACKENDS_TOKENIZER_MODE = [
("mistralai/Ministral-8B-Instruct-2410", "xgrammar", "auto", None),
("mistralai/Ministral-8B-Instruct-2410", "guidance", "auto", None),
("mistralai/Ministral-8B-Instruct-2410", "lm-format-enforcer", "auto",
None),
("mistralai/Ministral-8B-Instruct-2410", "xgrammar", "mistral", None),
("Qwen/Qwen2.5-1.5B-Instruct", "xgrammar", "auto", None),
("Qwen/Qwen2.5-1.5B-Instruct", "lm-format-enforcer", "auto", None),
("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto", None),
("mistralai/Ministral-8B-Instruct-2410", "outlines", "mistral", None),
("mistralai/Ministral-8B-Instruct-2410", "outlines", "auto",
......@@ -127,13 +132,15 @@ def test_structured_output(
temperature=1.0,
max_tokens=4096,
guided_decoding=GuidedDecodingParams(json=sample_json_schema))
outputs = llm.generate(prompts=[
(f"Give an example JSON for an employee profile that fits this "
f"schema. Make the response as short as possible. Schema: "
f"{sample_json_schema}")
] * 2,
sampling_params=sampling_params,
use_tqdm=True)
prompt = ("Give an example JSON for an employee profile that fits this "
"schema. Make the response as short as possible. Schema: "
f"{sample_json_schema}")
outputs = llm.generate(
[prompt] * 2,
sampling_params=sampling_params,
use_tqdm=True,
)
assert outputs is not None
......@@ -144,7 +151,8 @@ def test_structured_output(
generated_text = output.outputs[0].text
assert generated_text is not None
assert "\n" not in generated_text
if guided_decoding_backend != 'lm-format-enforcer':
assert "\n" not in generated_text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
output_json = json.loads(generated_text)
jsonschema.validate(instance=output_json, schema=sample_json_schema)
......@@ -191,20 +199,24 @@ def test_structured_output(
with pytest.raises(ValueError,
match="The provided JSON schema contains features "
"not supported by xgrammar."):
prompt = (f"Give an example JSON for an employee profile that "
f"fits this schema: {unsupported_json_schema}. "
f"Make the response as short as possible.")
llm.generate(
prompts=[(f"Give an example JSON for an employee profile that "
f"fits this schema: {unsupported_json_schema}. "
f"Make the response as short as possible.")] * 2,
[prompt] * 2,
sampling_params=sampling_params,
use_tqdm=True)
use_tqdm=True,
)
else:
outputs = llm.generate(prompts=(
"Give an example JSON object for a grade "
"that fits this schema: "
f"{unsupported_json_schema}. Make the response as short as "
"possible."),
sampling_params=sampling_params,
use_tqdm=True)
prompt = (f"Give an example JSON object for a grade that "
f"fits this schema: {unsupported_json_schema}. "
f"Make the response as short as possible.")
outputs = llm.generate(
prompt,
sampling_params=sampling_params,
use_tqdm=True,
)
assert outputs is not None
for output in outputs:
assert output is not None
......@@ -217,7 +229,7 @@ def test_structured_output(
parsed_json = json.loads(generated_text)
assert isinstance(parsed_json, dict)
if guided_decoding_backend != "outlines":
if guided_decoding_backend not in ["outlines", "lm-format-enforcer"]:
#
# Test 4: Generate SQL statement using EBNF grammar
#
......@@ -227,10 +239,9 @@ def test_structured_output(
max_tokens=1000,
guided_decoding=GuidedDecodingParams(grammar=sample_sql_ebnf))
outputs = llm.generate(
prompts=(
"Generate a sql statement that selects col_1 from "
"table_1 where it is equal to 1. Make the response as short as "
"possible."),
("Generate a sql statement that selects col_1 from "
"table_1 where it is equal to 1. Make the response as short as "
"possible."),
sampling_params=sampling_params,
use_tqdm=True,
)
......@@ -261,10 +272,9 @@ def test_structured_output(
max_tokens=1000,
guided_decoding=GuidedDecodingParams(grammar=sample_sql_lark))
outputs = llm.generate(
prompts=(
"Generate a sql statement that selects col_1 from "
"table_1 where it is equal to 1. Make the response as short as "
"possible."),
("Generate a sql statement that selects col_1 from "
"table_1 where it is equal to 1. Make the response as short as "
"possible."),
sampling_params=sampling_params,
use_tqdm=True,
)
......@@ -301,7 +311,6 @@ def test_structured_output(
guided_decoding=GuidedDecodingParams(grammar="not a grammar"))
with pytest.raises(ValueError, match="Failed to convert the grammar "):
llm.generate(
prompts=
("Generate a sql statement that selects col_1 from "
"table_1 where it is equal to 1. Make the response as short "
"as possible."),
......@@ -316,11 +325,11 @@ def test_structured_output(
temperature=0.8,
top_p=0.95,
guided_decoding=GuidedDecodingParams(regex=sample_regex))
prompt = (f"Give an example IPv4 address with this regex: {sample_regex}. "
f"Make the response as short as possible.")
outputs = llm.generate(
prompts=[
(f"Give an example IPv4 address with this regex: {sample_regex}. "
f"Make the response as short as possible.")
] * 2,
[prompt] * 2,
sampling_params=sampling_params,
use_tqdm=True,
)
......@@ -343,11 +352,13 @@ def test_structured_output(
temperature=0.8,
top_p=0.95,
guided_decoding=GuidedDecodingParams(choice=sample_guided_choice))
outputs = llm.generate(
prompts=("The best language for type-safe systems programming is "
"(Make the response as short as possible.) "),
("The best language for type-safe systems programming is "
"(Make the response as short as possible.) "),
sampling_params=sampling_params,
use_tqdm=True)
use_tqdm=True,
)
assert outputs is not None
for output in outputs:
assert output is not None
......@@ -367,12 +378,14 @@ def test_structured_output(
temperature=1.0,
max_tokens=1000,
guided_decoding=GuidedDecodingParams(json=json_schema))
outputs = llm.generate(prompts=(
"Generate a JSON with the brand, model and car_type of the most "
"iconic car from the 90's. Make the response as short as "
"possible."),
sampling_params=sampling_params,
use_tqdm=True)
outputs = llm.generate(
("Generate a JSON with the brand, model and car_type of the most "
"iconic car from the 90's. Make the response as short as "
"possible."),
sampling_params=sampling_params,
use_tqdm=True,
)
assert outputs is not None
......@@ -411,10 +424,11 @@ def test_structured_output(
guided_decoding=GuidedDecodingParams(json=json_schema))
outputs = llm.generate(
prompts=("Generate a description of a frog using 50 characters. "
"Make the response as short as possible."),
("Generate a description of a frog using 50 characters. "
"Make the response as short as possible."),
sampling_params=sampling_params,
use_tqdm=True)
use_tqdm=True,
)
assert outputs is not None
......@@ -429,7 +443,7 @@ def test_structured_output(
output_json = json.loads(generated_text)
jsonschema.validate(instance=output_json, schema=json_schema)
if guided_decoding_backend != "outlines":
if guided_decoding_backend not in ["outlines", "lm-format-enforcer"]:
#
# Test 11: Generate structured output using structural_tag format
#
......@@ -498,7 +512,7 @@ Make the response as short as possible.
"""
# Change this once other backends support structural_tag
outputs = llm.generate(prompts=prompt,
outputs = llm.generate(prompt,
sampling_params=sampling_params,
use_tqdm=True)
assert outputs is not None
......@@ -639,15 +653,13 @@ def test_structured_output_auto_mode(
f"{unsupported_json_schema}. Make the response as short as possible.")
# This would fail with the default of "xgrammar", but in "auto"
# we will handle fallback automatically.
outputs = llm.generate(prompts=prompts,
outputs = llm.generate(prompts,
sampling_params=sampling_params,
use_tqdm=True)
# Make sure `auto` backend handling doesn't mess up sampling_params
# and that we can reuse it without error.
outputs.extend(
llm.generate(prompts=prompts,
sampling_params=sampling_params,
use_tqdm=True))
llm.generate(prompts, sampling_params=sampling_params, use_tqdm=True))
assert outputs is not None
for output in outputs:
......@@ -705,7 +717,7 @@ def test_guidance_no_additional_properties(monkeypatch: pytest.MonkeyPatch):
max_tokens=256,
guided_decoding=guided_params)
outputs = llm.generate(prompts=prompt, sampling_params=sampling_params)
outputs = llm.generate(prompt, sampling_params=sampling_params)
assert outputs is not None
generated_text = outputs[0].outputs[0].text
assert generated_text is not None
......@@ -721,3 +733,83 @@ def test_guidance_no_additional_properties(monkeypatch: pytest.MonkeyPatch):
assert "a4" not in generated
assert "a5" not in generated
assert "a6" not in generated
@pytest.mark.parametrize("guided_decoding_backend",
["guidance", "xgrammar", "outlines"])
def test_structured_output_batched_with_non_guided_requests(
monkeypatch: pytest.MonkeyPatch,
sample_json_schema: dict[str, Any],
guided_decoding_backend: str,
):
monkeypatch.setenv("VLLM_USE_V1", "1")
# Don't use eager execution on TPUs because we want to test for no
# recompilation at runtime
enforce_eager = bool(not current_platform.is_tpu())
llm = LLM(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
enforce_eager=enforce_eager,
max_model_len=1024,
guided_decoding_backend=guided_decoding_backend,
guided_decoding_disable_any_whitespace=(guided_decoding_backend
in {"xgrammar", "guidance"}),
)
guided_prompt = (
"Give an example JSON for an employee profile that fits this "
"schema. Make the response as short as possible. Schema: "
f"{sample_json_schema}")
non_guided_prompt = "The diameter of the Earth in kilometers is "
prompts = [guided_prompt, non_guided_prompt]
sampling_params = [
SamplingParams(
temperature=1.0,
max_tokens=400,
guided_decoding=GuidedDecodingParams(json=sample_json_schema)),
# No max tokens, temp=0 to assert on contents
SamplingParams(
seed=42,
temperature=0,
top_p=1.0,
),
]
outputs = llm.generate(prompts=prompts,
sampling_params=sampling_params,
use_tqdm=True)
assert outputs is not None
# Free memory as soon as possible as failed assertions
# will short circuit and not free up memory
del llm
torch.cuda.empty_cache()
cleanup_dist_env_and_memory()
for index, output in enumerate(outputs):
assert output is not None
assert isinstance(output, RequestOutput)
prompt = output.prompt
generated_text = output.outputs[0].text
assert generated_text is not None
print(f"Prompt:\n{prompt!r}\nGenerated text:\n{generated_text!r}")
if index == 0:
# First prompt is guided, expect valid JSON
assert "\n" not in generated_text
output_json = json.loads(generated_text)
jsonschema.validate(instance=output_json,
schema=sample_json_schema)
else:
# Second prompt is not guided, expect valid output
# Cannot assert on exact output, but we can expect it to be factual
assert "12,742" in generated_text
# non-guided requests should not return a valid JSON here
with pytest.raises(ValueError):
output_json = json.loads(generated_text)
tests/v1/entrypoints/openai/responses/test_basic.py
View file @ d2b52805
......@@ -73,3 +73,16 @@ async def test_chat_with_input_type(client: openai.AsyncOpenAI):
], )
print(response)
assert response.status == "completed"
@pytest.mark.asyncio
async def test_logprobs(client: openai.AsyncOpenAI):
response = await client.responses.create(
include=["message.output_text.logprobs"],
input="What is 13 * 24?",
top_logprobs=5,
)
print(response)
outputs = response.output
assert outputs[-1].content[-1].logprobs
assert len(outputs[-1].content[-1].logprobs[0].top_logprobs) == 5
tests/v1/executor/test_executor.py 0 → 100644
View file @ d2b52805
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import os
from typing import Any, Callable, Optional, Union
import pytest
from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
from vllm.sampling_params import SamplingParams
from vllm.v1.engine.async_llm import AsyncLLM
from vllm.v1.engine.llm_engine import LLMEngine
from vllm.v1.executor.multiproc_executor import MultiprocExecutor
class Mock:
...
class CustomMultiprocExecutor(MultiprocExecutor):
def collective_rpc(self,
method: Union[str, Callable],
timeout: Optional[float] = None,
args: tuple = (),
kwargs: Optional[dict] = None,
non_block: bool = False,
unique_reply_rank: Optional[int] = None) -> list[Any]:
# Drop marker to show that this was ran
with open(".marker", "w"):
...
return super().collective_rpc(method, timeout, args, kwargs)
CustomMultiprocExecutorAsync = CustomMultiprocExecutor
MODEL = "Qwen/Qwen3-0.6B"
def test_custom_executor_type_checking():
with pytest.raises(ValueError):
engine_args = EngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=Mock,
)
LLMEngine.from_engine_args(engine_args)
with pytest.raises(ValueError):
engine_args = AsyncEngineArgs(model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=Mock)
AsyncLLM.from_engine_args(engine_args)
@pytest.mark.parametrize("distributed_executor_backend", [
CustomMultiprocExecutor,
"tests.v1.executor.test_executor.CustomMultiprocExecutor"
])
def test_custom_executor(distributed_executor_backend, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
try:
assert not os.path.exists(".marker")
engine_args = EngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True, # reduce test time
)
engine = LLMEngine.from_engine_args(engine_args)
sampling_params = SamplingParams(max_tokens=1)
engine.add_request("0", "foo", sampling_params)
engine.step()
assert os.path.exists(".marker")
finally:
os.chdir(cwd)
@pytest.mark.parametrize("distributed_executor_backend", [
CustomMultiprocExecutorAsync,
"tests.v1.executor.test_executor.CustomMultiprocExecutorAsync"
])
def test_custom_executor_async(distributed_executor_backend, tmp_path):
cwd = os.path.abspath(".")
os.chdir(tmp_path)
try:
assert not os.path.exists(".marker")
engine_args = AsyncEngineArgs(
model=MODEL,
gpu_memory_utilization=0.2,
max_model_len=8192,
distributed_executor_backend=distributed_executor_backend,
enforce_eager=True, # reduce test time
)
engine = AsyncLLM.from_engine_args(engine_args)
sampling_params = SamplingParams(max_tokens=1)
async def t():
stream = engine.generate(request_id="0",
prompt="foo",
sampling_params=sampling_params)
async for x in stream:
...
asyncio.run(t())
assert os.path.exists(".marker")
finally:
os.chdir(cwd)
tests/v1/kv_connector/unit/test_nixl_connector.py
View file @ d2b52805
......@@ -14,6 +14,7 @@ from unittest.mock import patch
import pytest
import ray
import torch
from vllm import LLM
from vllm.config import KVTransferConfig
......@@ -22,6 +23,7 @@ from vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector import (
NixlConnectorWorker)
from vllm.forward_context import ForwardContext
from vllm.sampling_params import SamplingParams
from vllm.v1.attention.backends.flash_attn import FlashAttentionBackend
from .utils import create_request, create_scheduler, create_vllm_config
......@@ -98,7 +100,6 @@ class FakeNixlWrapper:
def set_cycles_before_xfer_done(self, cycles: int):
"""Set the number of cycles before a transfer is considered done."""
self._cycles_before_xfer_done = cycles
@contextlib.contextmanager
......@@ -562,3 +563,86 @@ def _run_abort_timeout_test(llm_kwargs: dict, timeout: int):
sampling_params)
# Request-0 times out and is cleared!
assert '0' not in req_to_blocks
def test_register_kv_caches(dist_init):
"""
Test that register_kv_caches() properly calls nixl_wrapper methods with
correct data.
This test verifies:
1. nixl_wrapper.get_reg_descs() is called with caches_data containing
tensor metadata
2. nixl_wrapper.get_xfer_descs() is called with blocks_data containing
block layout info
"""
vllm_config = create_vllm_config()
# Create test kv cache tensors using proper backend shape
kv_cache_shape = FlashAttentionBackend.get_kv_cache_shape(num_blocks=2,
block_size=16,
num_kv_heads=4,
head_size=64)
shared_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
unique_tensor = torch.zeros(*kv_cache_shape, dtype=torch.float16)
kv_caches = {
"layer0": shared_tensor,
"layer1": unique_tensor,
"layer2": shared_tensor,
}
# Store tensor info for validation
expected_tensor_size = shared_tensor[0].element_size(
) * shared_tensor[0].numel()
expected_base_addrs = [
shared_tensor[0].data_ptr(), shared_tensor[1].data_ptr(),
unique_tensor[0].data_ptr(), unique_tensor[1].data_ptr()
]
with patch("vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector.NixlWrapper") as mock_nixl_wrapper, \
patch("vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector.threading.Event"), \
patch("vllm.distributed.kv_transfer.kv_connector.v1.nixl_connector.threading.Thread"): # noqa: E501
# Create connector
connector = NixlConnector(vllm_config, KVConnectorRole.WORKER)
connector.connector_worker = FakeNixlConnectorWorker(
vllm_config, connector.engine_id, hand_shake_latency=0)
# Get the mock instance
mock_wrapper_instance = mock_nixl_wrapper.return_value
connector.connector_worker.nixl_wrapper = mock_wrapper_instance
# Execute register_kv_caches
connector.register_kv_caches(kv_caches)
# Verify get_reg_descs was called with caches_data
assert mock_wrapper_instance.get_reg_descs.called
caches_data, _ = mock_wrapper_instance.get_reg_descs.call_args[0]
assert len(caches_data) == 4
for i, cache_entry in enumerate(caches_data):
base_addr, size, _tp_rank, _ = cache_entry
assert size == expected_tensor_size, \
f"Entry {i}: Expected tensor size {expected_tensor_size}, " \
f"got {size}"
assert base_addr == expected_base_addrs[i], \
f"Entry {i}: Expected base address {expected_base_addrs[i]}, " \
f"got {base_addr}"
# Verify get_xfer_descs was called with blocks_data
assert mock_wrapper_instance.get_xfer_descs.called
blocks_data, _ = mock_wrapper_instance.get_xfer_descs.call_args[0]
# Validate blocks_data structure and size
expected_blocks_count = 8
assert len(blocks_data) == expected_blocks_count, \
f"Expected {expected_blocks_count} blocks, " \
f"got {len(blocks_data)}"
expected_block_len = expected_tensor_size // 2
for i, block_entry in enumerate(blocks_data):
block_start_addr, block_len, tp_rank = block_entry
assert block_len == expected_block_len, \
f"Block entry {i}: Expected block len {expected_block_len}, " \
f"got {block_len}"
tests/v1/kv_connector/unit/utils.py
View file @ d2b52805
......@@ -162,9 +162,7 @@ def create_request(request_id: int,
prompt_token_ids=prompt_token_ids,
sampling_params=sampling_params,
pooling_params=None,
multi_modal_kwargs=None,
multi_modal_placeholders=None,
multi_modal_hashes=None,
mm_features=None,
eos_token_id=EOS_TOKEN_ID,
block_hasher=get_request_block_hasher(block_size, hash_fn),
)
......@@ -200,7 +198,6 @@ def create_model_runner_output(
req_ids=req_ids,
req_id_to_index=req_id_to_index,
sampled_token_ids=sampled_token_ids,
spec_token_ids=None,
logprobs=None,
prompt_logprobs_dict={},
pooler_output=None,
......
tests/v1/logits_processors/utils.py
View file @ d2b52805
......@@ -8,10 +8,9 @@ from typing import Optional
import torch
from vllm.config import VllmConfig
from vllm.sampling_params import SamplingParams
from vllm.v1.sample.logits_processor import (LOGITSPROCS_GROUP, BatchUpdate,
LogitsProcessor,
MoveDirectionality)
LogitsProcessor)
from vllm.v1.sample.logits_processor.builtin import process_dict_updates
MODEL_NAME = "facebook/opt-125m"
POOLING_MODEL_NAME = "BAAI/bge-base-en-v1.5"
......@@ -45,37 +44,19 @@ class DummyLogitsProcessor(LogitsProcessor):
def __init__(self, vllm_config: "VllmConfig", device: torch.device,
is_pin_memory: bool):
self.req_info: dict[int, SamplingParams] = {}
self.req_info: dict[int, int] = {}
def is_argmax_invariant(self) -> bool:
"""Never impacts greedy sampling"""
return False
def update_state(self, batch_update: Optional[BatchUpdate]):
if not batch_update:
return
# Process added requests.
for index, params, _, _ in batch_update.added:
assert params is not None
if params.extra_args and (target_token :=
params.extra_args.get("target_token")):
self.req_info[index] = target_token
if self.req_info:
# Process removed requests.
for index in batch_update.removed:
self.req_info.pop(index, None)
# Process moved requests, unidirectional move (a->b) and swap
# (a<->b)
for adx, bdx, direct in batch_update.moved:
a_val = self.req_info.pop(adx, None)
b_val = self.req_info.pop(bdx, None)
if a_val is not None:
self.req_info[bdx] = a_val
if direct == MoveDirectionality.SWAP and b_val is not None:
self.req_info[adx] = b_val
process_dict_updates(
self.req_info,
batch_update,
lambda params, _, __: params.extra_args and
(params.extra_args.get("target_token")),
)
def apply(self, logits: torch.Tensor) -> torch.Tensor:
if not self.req_info:
......
tests/v1/sample/test_logprobs.py
View file @ d2b52805
......@@ -456,9 +456,7 @@ def test_all_logprobs(example_prompts, monkeypatch: pytest.MonkeyPatch):
assert len(logprob) == vocab_size
@pytest.mark.parametrize(
"logprobs_mode",
["raw_logprobs", "raw_logits", "processed_logprobs", "processed_logits"])
@pytest.mark.parametrize("logprobs_mode", list(LogprobsMode))
def test_logprobs_mode(logprobs_mode: LogprobsMode,
monkeypatch: pytest.MonkeyPatch):
"""Test with LLM engine with different logprobs_mode.
......@@ -487,12 +485,14 @@ def test_logprobs_mode(logprobs_mode: LogprobsMode,
for logprobs in output.logprobs:
for token_id in logprobs:
logprob = logprobs[token_id]
if "logprobs" in logprobs_mode:
if logprobs_mode in (LogprobsMode.RAW_LOGPROBS,
LogprobsMode.PROCESSED_LOGPROBS):
assert logprob.logprob <= 0
if logprob.logprob > 0:
positive_values = positive_values + 1
total_token_with_logprobs = total_token_with_logprobs + 1
assert total_token_with_logprobs >= len(results[0].outputs)
if "logits" in logprobs_mode:
if logprobs_mode in (LogprobsMode.RAW_LOGITS,
LogprobsMode.PROCESSED_LOGITS):
assert positive_values > 0
del llm
tests/v1/spec_decode/test_tree_attention.py
View file @ d2b52805
......@@ -50,6 +50,7 @@ def forward_attention(
dtype=torch.int32,
)
context_lens = seq_lens - query_lens
max_seq_len = int(seq_lens.max())
max_query_len = q_len
num_actual_tokens = query_start_loc[-1]
......@@ -81,6 +82,7 @@ def forward_attention(
num_reqs=batch_size,
num_actual_tokens=num_actual_tokens,
max_query_len=max_query_len,
max_seq_len=max_seq_len,
block_table_tensor=block_table,
slot_mapping=slot_mapping,
)
......
tests/v1/test_async_llm_dp.py
View file @ d2b52805
......@@ -75,9 +75,10 @@ async def generate(
],
)
@pytest.mark.parametrize("data_parallel_backend", ["mp", "ray"])
@pytest.mark.parametrize("async_scheduling", [True, False])
@pytest.mark.asyncio
async def test_load(output_kind: RequestOutputKind,
data_parallel_backend: str):
async def test_load(output_kind: RequestOutputKind, data_parallel_backend: str,
async_scheduling: bool):
stats_loggers = {}
......@@ -105,6 +106,7 @@ async def test_load(output_kind: RequestOutputKind,
prompt = "This is a test of data parallel"
engine_args.data_parallel_backend = data_parallel_backend
engine_args.async_scheduling = async_scheduling
engine = AsyncLLM.from_engine_args(engine_args,
stat_loggers=[SimpleStatsLogger])
after.callback(engine.shutdown)
......
tests/v1/test_serial_utils.py
View file @ d2b52805
......@@ -11,7 +11,8 @@ import torch
from vllm.multimodal.inputs import (MultiModalBatchedField,
MultiModalFieldElem, MultiModalFlatField,
MultiModalKwargs, MultiModalKwargsItem,
MultiModalKwargsItem,
MultiModalKwargsItems,
MultiModalSharedField, NestedTensors)
from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
......@@ -96,7 +97,7 @@ def test_encode_decode(monkeypatch: pytest.MonkeyPatch):
class MyRequest(msgspec.Struct):
mm: Optional[list[MultiModalKwargs]]
mm: Optional[list[MultiModalKwargsItems]]
def test_multimodal_kwargs():
......@@ -119,7 +120,7 @@ def test_multimodal_kwargs():
audio = MultiModalKwargsItem.from_elems([e1])
video = MultiModalKwargsItem.from_elems([e2])
image = MultiModalKwargsItem.from_elems([e3, e4])
mm = MultiModalKwargs([audio, video, image])
mm = MultiModalKwargsItems.from_seq([audio, video, image])
# pack mm kwargs into a mock request so that it can be decoded properly
req = MyRequest([mm])
......@@ -133,19 +134,22 @@ def test_multimodal_kwargs():
total_len = sum(memoryview(x).cast("B").nbytes for x in encoded)
# expected total encoding length, should be 14255, +-20 for minor changes
assert 14250 <= total_len <= 14300
decoded: MultiModalKwargs = decoder.decode(encoded).mm[0]
# expected total encoding length, should be 14306, +-20 for minor changes
assert 14275 <= total_len <= 14325
decoded = decoder.decode(encoded).mm[0]
assert isinstance(decoded, MultiModalKwargsItems)
# check all modalities were recovered and do some basic sanity checks
assert len(decoded.modalities) == 3
images = decoded.get_items("image")
assert len(decoded) == 3
images = decoded["image"]
assert len(images) == 1
assert len(images[0].items()) == 2
assert list(images[0].keys()) == ["i0", "i1"]
# check the tensor contents and layout in the main dict
assert all(nested_equal(mm[k], decoded[k]) for k in mm)
mm_data = mm.get_data()
decoded_data = decoded.get_data()
assert all(nested_equal(mm_data[k], decoded_data[k]) for k in mm_data)
def nested_equal(a: NestedTensors, b: NestedTensors):
......
tests/v1/tpu/worker/test_tpu_model_runner.py
View file @ d2b52805
......@@ -85,7 +85,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -164,7 +164,7 @@ def test_update_states_request_finished(model_runner):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids={req_id},
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -194,7 +194,7 @@ def test_update_states_request_resumed(model_runner):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -221,7 +221,7 @@ def test_update_states_request_resumed(model_runner):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -252,7 +252,7 @@ def test_update_states_no_changes(model_runner):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -287,7 +287,7 @@ def test_update_states_request_unscheduled(model_runner):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......
tests/v1/worker/test_gpu_input_batch.py
View file @ d2b52805
......@@ -205,6 +205,7 @@ def _construct_cached_request_state(req_id_suffix: int):
pooling_params=None,
mm_kwargs=[],
mm_positions=[],
mm_hashes=[],
block_ids=([], ),
generator=None,
num_computed_tokens=len(output_token_ids),
......
tests/v1/worker/test_gpu_model_runner.py
View file @ d2b52805
......@@ -141,7 +141,7 @@ def _schedule_new_request(*req_ids: str) -> SchedulerOutput:
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -207,7 +207,7 @@ def test_update_states_request_finished(model_runner, dist_init):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids={req_id},
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -239,7 +239,7 @@ def test_update_states_request_resumed(model_runner, dist_init):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -266,7 +266,7 @@ def test_update_states_request_resumed(model_runner, dist_init):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -347,7 +347,7 @@ def test_update_states_no_changes(model_runner, dist_init):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -384,7 +384,7 @@ def test_update_states_request_unscheduled(model_runner, dist_init):
scheduled_encoder_inputs={},
num_common_prefix_blocks=0,
finished_req_ids=set(),
free_encoder_input_ids=[],
free_encoder_mm_hashes=[],
structured_output_request_ids={},
grammar_bitmask=None,
)
......@@ -680,6 +680,7 @@ def test_init_kv_cache_with_kv_sharing_valid():
kv_cache_spec[layer_0].page_size_bytes
runner.initialize_kv_cache(kv_cache_config)
kv_cache_config_after_init = runner.kv_cache_config
layer_0_kv = vllm_ctx[layer_0].kv_cache[0]
layer_1_kv = vllm_ctx[layer_1].kv_cache[0]
......@@ -687,10 +688,12 @@ def test_init_kv_cache_with_kv_sharing_valid():
assert id(layer_1_kv) == id(layer_0_kv)
# check layer 1 added to kv cache group's layer names
assert len(kv_cache_config.kv_cache_groups) == 1
assert len(kv_cache_config.kv_cache_groups[0].layer_names) == 2
assert kv_cache_config.kv_cache_groups[0].layer_names[0] == layer_0
assert kv_cache_config.kv_cache_groups[0].layer_names[1] == layer_1
assert len(kv_cache_config_after_init.kv_cache_groups) == 1
assert len(kv_cache_config_after_init.kv_cache_groups[0].layer_names) == 2
assert kv_cache_config_after_init.kv_cache_groups[0].layer_names[
0] == layer_0
assert kv_cache_config_after_init.kv_cache_groups[0].layer_names[
1] == layer_1
def test_hybrid_attention_mamba_tensor_shapes(monkeypatch):
......
tests/weight_loading/models.txt
View file @ d2b52805
......@@ -26,9 +26,5 @@ compressed-tensors, nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-W8A8-testing
awq, casperhansen/mixtral-instruct-awq, main
awq_marlin, casperhansen/mixtral-instruct-awq, main
fp8, neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV, main
marlin, nm-testing/zephyr-beta-7b-marlin-g128, main
marlin, robertgshaw2/zephyr-7b-beta-channelwise-marlin, main
qqq, HandH1998/QQQ-Llama-3-8b-g128, main
qqq, HandH1998/QQQ-Llama-3-8b, main
hqq, nm-testing/Llama-3.2-1B-Instruct-HQQ, main
None, mgleize/fairseq2-dummy-Llama-3.2-1B, main
\ No newline at end of file
tests/worker/test_model_input.py
View file @ d2b52805
......@@ -9,10 +9,7 @@ from vllm.attention import AttentionMetadata, AttentionMetadataBuilder
from vllm.attention.backends.abstract import AttentionBackend
from vllm.attention.backends.utils import CommonAttentionState
from vllm.model_executor import SamplingMetadata
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.worker.model_runner import ModelInputForGPUWithSamplingMetadata
from vllm.worker.pooling_model_runner import (
ModelInputForGPUWithPoolingMetadata)
class MockAttentionBackend(AttentionBackend):
......@@ -114,54 +111,3 @@ def test_model_runner_input():
assert (received_model_input.sampling_metadata.selected_token_indices ==
sampling_metadata.selected_token_indices)
assert received_model_input.sampling_metadata.seq_groups is None
def test_embedding_model_runner_input():
pooling_metadata = PoolingMetadata(
seq_groups=[[0]],
seq_data={},
prompt_lens=[1],
)
attn_metadata = AttentionMetadata(
num_prefills=1,
num_prefill_tokens=2,
num_decode_tokens=3,
slot_mapping=torch.zeros(1),
multi_modal_placeholder_index_maps=None,
enable_kv_scales_calculation=True,
)
model_input = ModelInputForGPUWithPoolingMetadata(
input_tokens=torch.ones(10),
input_positions=torch.ones(10),
pooling_metadata=pooling_metadata,
attn_metadata=attn_metadata)
assert isinstance(model_input, ModelInputForGPUWithPoolingMetadata)
# Test round trip serialization.
tensor_dict = model_input.as_broadcastable_tensor_dict()
attn_backend = MockAttentionBackend()
received_model_input = (
ModelInputForGPUWithPoolingMetadata.from_broadcasted_tensor_dict(
tensor_dict, attn_backend=attn_backend))
# Check that received copy has correct values.
assert isinstance(received_model_input,
ModelInputForGPUWithPoolingMetadata)
assert received_model_input.input_tokens is not None
assert (
received_model_input.input_tokens == model_input.input_tokens).all()
assert received_model_input.input_positions is not None
assert (received_model_input.input_positions == model_input.input_positions
).all()
assert received_model_input.multi_modal_kwargs is None
assert (received_model_input.multi_modal_kwargs ==
model_input.multi_modal_kwargs)
assert received_model_input.lora_requests is None
assert received_model_input.lora_requests == model_input.lora_requests
assert received_model_input.lora_mapping is None
assert received_model_input.lora_mapping == model_input.lora_mapping
for field in dataclasses.fields(AttentionMetadata):
assert getattr(received_model_input.attn_metadata, field.name,
None) == getattr(attn_metadata, field.name, None)
# Pooling metadata is not broadcast.
assert received_model_input.pooling_metadata is None
tools/check_pickle_imports.py
View file @ d2b52805
......@@ -37,7 +37,7 @@ ALLOWED_FILES = set([
'vllm/distributed/utils.py',
'vllm/distributed/parallel_state.py',
'vllm/engine/multiprocessing/client.py',
'vllm/distributed/device_communicators/custom_all_reduce_utils.py',
'vllm/distributed/device_communicators/all_reduce_utils.py',
'vllm/distributed/device_communicators/shm_broadcast.py',
'vllm/engine/multiprocessing/engine.py',
'benchmarks/kernels/graph_machete_bench.py',
......
tools/ep_kernels/install_python_libraries.sh
View file @ d2b52805
......@@ -77,6 +77,7 @@ clone_repo() {
local repo_url=$1
local dir_name=$2
local key_file=$3
local commit_hash=$4
if [ -d "$dir_name" ]; then
# Check if directory has uncommitted changes (dirty)
......@@ -87,17 +88,27 @@ clone_repo() {
echo "$dir_name directory exists but clone appears incomplete, cleaning up and re-cloning"
rm -rf "$dir_name"
git clone "$repo_url"
if [ -n "$commit_hash" ]; then
cd "$dir_name"
git checkout "$commit_hash"
cd ..
fi
else
echo "$dir_name directory exists and appears complete; manually update if needed"
fi
else
git clone "$repo_url"
if [ -n "$commit_hash" ]; then
cd "$dir_name"
git checkout "$commit_hash"
cd ..
fi
fi
}
# build and install pplx, require pytorch installed
pushd $WORKSPACE
clone_repo "https://github.com/ppl-ai/pplx-kernels" "pplx-kernels" "setup.py"
clone_repo "https://github.com/ppl-ai/pplx-kernels" "pplx-kernels" "setup.py" "c336faf"
cd pplx-kernels
# see https://github.com/pypa/pip/issues/9955#issuecomment-838065925
# PIP_NO_BUILD_ISOLATION=0 disables build isolation
......@@ -106,7 +117,7 @@ popd
# build and install deepep, require pytorch installed
pushd $WORKSPACE
clone_repo "https://github.com/deepseek-ai/DeepEP" "DeepEP" "setup.py"
clone_repo "https://github.com/deepseek-ai/DeepEP" "DeepEP" "setup.py" "e3908bf"
cd DeepEP
export NVSHMEM_DIR=$WORKSPACE/nvshmem_install
PIP_NO_BUILD_ISOLATION=0 pip install -vvv -e .
......
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