import asyncio
from collections import defaultdict
from typing import Dict, List, Tuple
import ray
from ray.util.placement_group import placement_group, placement_group_table
from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
from trinity.common.config import Config, InferenceModelConfig
from trinity.common.constants import DEBUG_NAMESPACE
from trinity.common.models.model import InferenceModel, ModelWrapper
from trinity.utils.log import get_logger
class _BundleAllocator:
"""An allocator for bundles."""
def __init__(self, num_gpus: int) -> None:
self.logger = get_logger(__name__, in_ray_actor=True)
bundles = [{"GPU": 1} for _ in range(num_gpus)]
self.pg = placement_group(bundles, strategy="PACK")
ray.get(self.pg.ready())
# to address https://github.com/ray-project/ray/issues/51117
# aggregate bundles belonging to the same node
bundle_node_map = placement_group_table(self.pg)["bundles_to_node_id"]
node_bundle_map = defaultdict(list)
for bundle_id, node_id in bundle_node_map.items():
node_bundle_map[node_id].append(bundle_id)
self.node_bundle_list = [value for value in node_bundle_map.values()]
self.node_list = [key for key in node_bundle_map.keys()]
self.nid = 0
self.bid = 0
def allocate(self, num: int) -> list:
# allocate num bundles from current node
if self.bid + num > len(self.node_bundle_list[self.nid]):
raise ValueError(
"Bundle allocation error, a tensor parallel group"
" is allocated across multiple nodes."
)
bundle_list = self.node_bundle_list[self.nid][self.bid : self.bid + num]
self.logger.info(f"Allocate bundles {bundle_list} on node {self.node_list[self.nid]}.")
self.bid += num
if self.bid == len(self.node_bundle_list[self.nid]):
self.bid = 0
self.nid += 1
return bundle_list
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def create_explorer_models(
config: Config,
) -> Tuple[List, List[List]]:
"""Create rollout_models and auxiliary_models.
Args:
config: The trinity configuration.
Returns:
Tuple[List, List[List]]: The rollout_models and auxiliary_models.
"""
logger = get_logger(__name__)
rollout_engines = []
if config.explorer.rollout_model.engine_type.startswith("vllm"):
from trinity.common.models.vllm_model import vLLMRolloutModel
engine_cls = vLLMRolloutModel
elif config.explorer.rollout_model.engine_type == "tinker":
from trinity.common.models.tinker_model import TinkerModel
engine_cls = TinkerModel
namespace = config.ray_namespace
rollout_engines = [
ray.remote(engine_cls)
.options(
name=f"{config.explorer.name}_rollout_model_{i}",
namespace=namespace,
)
.remote(
config=config.explorer.rollout_model,
)
for i in range(config.explorer.rollout_model.engine_num)
]
auxiliary_engines = [
[
ray.remote(engine_cls)
.options(
name=f"{config.explorer.name}_auxiliary_model_{model_config.name or i}_{j}",
namespace=namespace,
)
.remote(
config=config.explorer.auxiliary_models[i],
)
for j in range(model_config.engine_num)
]
for i, model_config in enumerate(config.explorer.auxiliary_models)
]
return rollout_engines, auxiliary_engines
else:
raise ValueError(f"Unknown engine type: {config.explorer.rollout_model.engine_type}")
if config.mode == "colocate":
rollout_engine = (
ray.remote(vLLMRolloutModel)
.options(
name=f"{config.explorer.name}_rollout_model_0",
num_cpus=0,
num_gpus=0,
namespace=config.ray_namespace,
)
.remote(
config=config.explorer.rollout_model,
)
)
return [rollout_engine], []
num_gpus = (
config.explorer.rollout_model.engine_num
* config.explorer.rollout_model.tensor_parallel_size
+ sum(
[
model.engine_num * model.tensor_parallel_size
for model in config.explorer.auxiliary_models
]
)
)
allocator = _BundleAllocator(num_gpus=num_gpus)
# create rollout models
rollout_engines = create_vllm_inference_models(
config=config.explorer.rollout_model,
allocator=allocator,
actor_name=f"{config.explorer.name}_rollout_model",
)
if config.explorer.rollout_model.enable_history:
logger.info(
"Model History recording is enabled. Please periodically extract "
"history via `extract_experience_from_history` to avoid out-of-memory issues."
)
# create auxiliary models
auxiliary_engines = []
for i, model_config in enumerate(config.explorer.auxiliary_models):
engines = create_vllm_inference_models(
config=model_config,
allocator=allocator,
actor_name=f"{config.explorer.name}_auxiliary_model_{model_config.name or i}",
)
auxiliary_engines.append(engines)
return rollout_engines, auxiliary_engines
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def create_vllm_inference_models(
config: InferenceModelConfig,
allocator: _BundleAllocator,
actor_name: str,
) -> List:
from trinity.common.models.vllm_model import vLLMRolloutModel
models = []
for i in range(config.engine_num):
bundles_for_engine = allocator.allocate(config.tensor_parallel_size)
config.bundle_indices = ",".join([str(bid) for bid in bundles_for_engine])
models.append(
ray.remote(vLLMRolloutModel)
.options(
name=f"{actor_name}_{i}",
num_cpus=0,
num_gpus=0 if config.tensor_parallel_size > 1 else 1,
namespace=config.ray_namespace,
scheduling_strategy=PlacementGroupSchedulingStrategy(
placement_group=allocator.pg,
placement_group_capture_child_tasks=True,
placement_group_bundle_index=bundles_for_engine[0],
),
)
.remote(
config=config,
)
)
return models
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async def create_debug_explorer_model(config: Config) -> None:
"""Create explorer inference models for debugging."""
logger = get_logger(__name__)
logger.info("Creating inference models for debugging...")
# only create one engine for each model
config.explorer.rollout_model.engine_num = 1
for model in config.explorer.auxiliary_models:
model.engine_num = 1
rollout_models, auxiliary_models = create_explorer_models(config)
# make sure models are started
prepare_refs = [m.prepare.remote() for m in rollout_models]
prepare_refs.extend(m.prepare.remote() for models in auxiliary_models for m in models)
await asyncio.gather(*prepare_refs)
logger.info(
"----------------------------------------------------\n"
"Inference models started successfully for debugging.\n"
"Press Ctrl+C to exit.\n"
"----------------------------------------------------"
)
try:
while True:
await asyncio.sleep(1)
except KeyboardInterrupt:
logger.info("Exiting...")
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def get_debug_explorer_model(config: Config) -> Tuple[InferenceModel, List[InferenceModel]]:
"""Get the inference models for debugging.
The models must be created by `create_debug_explorer_model` in another process first.
"""
import ray
try:
rollout_model = ray.get_actor(
f"{config.explorer.name}_rollout_model_0", namespace=DEBUG_NAMESPACE
)
auxiliary_models = []
for i, model_config in enumerate(config.explorer.auxiliary_models):
model = ray.get_actor(
f"{config.explorer.name}_auxiliary_model_{model_config.name or i}_0",
namespace=DEBUG_NAMESPACE,
)
auxiliary_models.append(model)
except ValueError:
raise RuntimeError(
"Debug explorer models not found. Please start the models first by running "
"`trinity debug --module inference_model --config config_path` in another process."
) from None
return rollout_model, auxiliary_models
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async def get_auxiliary_model_wrappers(config: Config) -> Dict[str | int, List[ModelWrapper]]:
"""Get auxiliary models.
Returns:
Dict[str| int, List[ModelWrapper]]: A dictionary mapping auxiliary model
index to a list of auxiliary model actor handlers.
"""
if not config.explorer.auxiliary_models:
# if no auxiliary model is configured
# return an empty dict to avoid unnecessary ray calls
return {}
auxiliary_models: Dict[str | int, List[ModelWrapper]] = defaultdict(list)
keys: List[str | int] = []
fetch_tasks = []
for i, model_config in enumerate(config.explorer.auxiliary_models):
key = model_config.name or i
for j in range(model_config.engine_num):
keys.append(key)
fetch_tasks.append(
asyncio.to_thread(
ray.get_actor,
f"{config.explorer.name}_auxiliary_model_{key}_{j}",
namespace=config.ray_namespace,
)
)
# Run lookups off the event loop to avoid blocking when many models exist.
actors = await asyncio.gather(*fetch_tasks)
for key, actor in zip(keys, actors):
auxiliary_models[key].append(ModelWrapper(actor))
# call prepare on all auxiliary models to make sure they are ready before returning
prepare_tasks = []
for model_wrappers in auxiliary_models.values():
for model_wrapper in model_wrappers:
prepare_tasks.append(model_wrapper.prepare())
await asyncio.gather(*prepare_tasks)
return dict(auxiliary_models)
