dynamo/benchmarks/profiler/utils/config_modifiers/vllm.py

306 lines
11 KiB
Python

# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
import logging
from typing import Literal
import yaml
from benchmarks.profiler.utils.config import (
Config,
append_argument,
break_arguments,
get_service_name_by_type,
get_worker_service_from_config,
set_argument_value,
setup_worker_service_resources,
update_image,
validate_and_get_worker_args,
)
from benchmarks.profiler.utils.defaults import (
DEFAULT_MODEL_NAME,
DYNAMO_RUN_DEFAULT_PORT,
)
from dynamo.planner.defaults import SubComponentType
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
console_handler = logging.StreamHandler()
console_handler.setLevel(logging.INFO)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s", "%Y-%m-%d %H:%M:%S"
)
console_handler.setFormatter(formatter)
logger.addHandler(console_handler)
DEFAULT_VLLM_CONFIG_PATH = "examples/backends/vllm/deploy/disagg.yaml"
class VllmV1ConfigModifier:
@classmethod
def load_default_config(cls) -> dict:
with open(DEFAULT_VLLM_CONFIG_PATH, "r") as f:
return yaml.safe_load(f)
@classmethod
def update_model(cls, config, model_name: str) -> dict:
# change the model to serve
cfg = Config.model_validate(config)
# Update model for both prefill and decode workers
for sub_component_type in [SubComponentType.PREFILL, SubComponentType.DECODE]:
try:
worker_service = get_worker_service_from_config(
cfg, backend="vllm", sub_component_type=sub_component_type
)
args = validate_and_get_worker_args(worker_service, backend="vllm")
args = break_arguments(args)
# Update --model (vllm uses --model instead of --model-path and --served-model-name)
args = set_argument_value(args, "--model", model_name)
worker_service.extraPodSpec.mainContainer.args = args
except (ValueError, KeyError):
# Service might not exist (e.g., in aggregated mode)
logger.debug(
f"Skipping {sub_component_type} service as it doesn't exist"
)
continue
return cfg.model_dump()
@classmethod
def update_image(cls, config, image: str) -> dict:
"""Update container image for all DGD services (frontend, planner, workers)."""
return update_image(config, image)
@classmethod
def convert_config(
cls,
config: dict,
target: Literal["prefill", "decode"],
is_moe_model: bool = False,
) -> dict:
if is_moe_model:
raise NotImplementedError(
"MoE model support is not implemented for VLLM backend"
)
cfg = Config.model_validate(config)
# set metadata name
cfg.metadata.name = "vllm-agg"
# disable planner
if "Planner" in cfg.spec.services:
del cfg.spec.services["Planner"]
if target == "prefill":
# Get service names by inferring from subComponentType first
prefill_service_name = get_service_name_by_type(
cfg, "vllm", SubComponentType.PREFILL
)
decode_service_name = get_service_name_by_type(
cfg, "vllm", SubComponentType.DECODE
)
# convert prefill worker into decode worker
cfg.spec.services[decode_service_name] = cfg.spec.services[
prefill_service_name
]
del cfg.spec.services[prefill_service_name]
# Set subComponentType for aggregated mode (using decode worker for prefill-only)
cfg.spec.services[decode_service_name].subComponentType = "decode"
worker_service = get_worker_service_from_config(
cfg,
backend="vllm",
sub_component_type=SubComponentType.DECODE,
)
args = validate_and_get_worker_args(worker_service, backend="vllm")
args = break_arguments(args)
# remove --is-prefill-worker flag
args.remove("--is-prefill-worker")
# disable prefix caching
if "--enable-prefix-caching" in args:
args.remove("--enable-prefix-caching")
if "--no-enable-prefix-caching" not in args:
args = append_argument(args, "--no-enable-prefix-caching")
worker_service.extraPodSpec.mainContainer.args = args
elif target == "decode":
# Get service names by inferring from subComponentType first
prefill_service_name = get_service_name_by_type(
cfg, "vllm", SubComponentType.PREFILL
)
decode_service_name = get_service_name_by_type(
cfg, "vllm", SubComponentType.DECODE
)
# delete prefill worker
del cfg.spec.services[prefill_service_name]
# Set subComponentType for aggregated decode-only mode
cfg.spec.services[decode_service_name].subComponentType = "decode"
worker_service = get_worker_service_from_config(
cfg,
backend="vllm",
sub_component_type=SubComponentType.DECODE,
)
args = validate_and_get_worker_args(worker_service, backend="vllm")
args = break_arguments(args)
# enable prefix caching
if "--enable-prefix-caching" not in args:
args = append_argument(args, "--enable-prefix-caching")
if "--no-enable-prefix-caching" in args:
args.remove("--no-enable-prefix-caching")
worker_service.extraPodSpec.mainContainer.args = args
# set num workers to 1
# Use the inferred decode service name
final_decode_service_name = get_service_name_by_type(
cfg, "vllm", SubComponentType.DECODE
)
decode_worker_config = cfg.spec.services[final_decode_service_name]
decode_worker_config.replicas = 1
return cfg.model_dump()
@classmethod
def set_config_tp_size(
cls,
config: dict,
tp_size: int,
component_type: SubComponentType = SubComponentType.DECODE,
):
cfg = Config.model_validate(config)
worker_service = get_worker_service_from_config(
cfg, backend="vllm", sub_component_type=component_type
)
# Set up resources
setup_worker_service_resources(worker_service, tp_size)
# Get and validate args
args = validate_and_get_worker_args(worker_service, backend="vllm")
args = break_arguments(args)
try:
idx = args.index("--tensor-parallel-size")
args[idx + 1] = str(tp_size)
except ValueError:
args = append_argument(args, ["--tensor-parallel-size", str(tp_size)])
worker_service.extraPodSpec.mainContainer.args = args
return cfg.model_dump()
@classmethod
def set_config_tep_size(
cls,
config: dict,
tep_size: int,
num_gpus_per_node: int,
component_type: SubComponentType = SubComponentType.DECODE,
):
raise NotImplementedError(
"TEP (Tensor Expert Parallelism) is not implemented for VLLM backend"
)
@classmethod
def set_config_dep_size(
cls,
config: dict,
dep_size: int,
num_gpus_per_node: int,
component_type: SubComponentType = SubComponentType.DECODE,
):
raise NotImplementedError(
"DEP (Data Expert Parallelism) is not implemented for VLLM backend"
)
@classmethod
def get_model_name(cls, config: dict) -> str:
cfg = Config.model_validate(config)
try:
worker_service = get_worker_service_from_config(cfg, backend="vllm")
args = validate_and_get_worker_args(worker_service, backend="vllm")
except (ValueError, KeyError):
logger.warning(
f"Worker service missing or invalid, using default model name: {DEFAULT_MODEL_NAME}"
)
return DEFAULT_MODEL_NAME
args = break_arguments(args)
for i, arg in enumerate(args):
if arg == "--model" and i + 1 < len(args):
return args[i + 1]
logger.warning(
f"Model name not found in configuration args, using default model name: {DEFAULT_MODEL_NAME}"
)
return DEFAULT_MODEL_NAME
@classmethod
def get_port(cls, config: dict) -> int:
cfg = Config.model_validate(config)
frontend_service = cfg.spec.services.get("Frontend")
if (
not frontend_service
or not frontend_service.extraPodSpec
or not frontend_service.extraPodSpec.mainContainer
):
logger.warning(
f"Frontend service or container not found, using default port: {DYNAMO_RUN_DEFAULT_PORT}"
)
return DYNAMO_RUN_DEFAULT_PORT
args = frontend_service.extraPodSpec.mainContainer.args
if not args:
logger.warning(
f"No args found in Frontend configuration, using default port: {DYNAMO_RUN_DEFAULT_PORT}"
)
return DYNAMO_RUN_DEFAULT_PORT
args = break_arguments(args)
try:
idx = args.index("--http-port")
return int(args[idx + 1])
except (ValueError, IndexError):
logger.warning(
f"Port not found in configuration args, using default port: {DYNAMO_RUN_DEFAULT_PORT}"
)
return DYNAMO_RUN_DEFAULT_PORT
@classmethod
def get_kv_cache_size_from_dynamo_log(
cls, dynamo_log_fn: str, attention_dp_size: int = 1
) -> int:
try:
with open(dynamo_log_fn, "r") as f:
for line in f:
if "Maximum concurrency for" in line:
line = line.strip().split("Maximum concurrency for ")[1]
token_count = int(
line.split(" tokens per request: ")[0].replace(",", "")
)
concurrency = float(line.split(" tokens per request: ")[1][:-1])
logger.info(
f"Found KV cache info: {token_count} x {concurrency} = {int(token_count * concurrency)}"
)
return int(token_count * concurrency)
except Exception as e:
logger.warning(
f"Failed to parse KV cache size from line: {line}. Error: {e}"
)
return 0