From c961323ec4d989a52c78e5b42fc32f665367949f Mon Sep 17 00:00:00 2001 From: TaoZex <2633363995@qq.com> Date: Mon, 30 Mar 2026 13:12:42 +0800 Subject: [PATCH 1/8] feat: support model training in IPv6-only environment (#1072) * feat: support model training in IPv6-only environment --------- Co-authored-by: bingyechen Co-authored-by: root --- areal/engine/fsdp_engine.py | 7 +- areal/engine/megatron_engine.py | 7 +- areal/engine/sglang_remote.py | 3 +- areal/engine/vllm_remote.py | 3 +- .../agent_service/worker/__main__.py | 6 +- .../experimental/engine/archon_weight_sync.py | 7 +- .../controller/controller.py | 26 ++++-- areal/infra/controller/rollout_controller.py | 12 ++- areal/infra/launcher/sglang_server.py | 10 +- areal/infra/launcher/vllm_server.py | 10 +- areal/infra/remote_inf_engine.py | 35 +++++-- areal/infra/rpc/rpc_server.py | 23 +++-- areal/infra/rpc/rtensor.py | 19 +++- areal/infra/scheduler/local.py | 24 ++--- areal/infra/scheduler/slurm.py | 29 +++--- areal/infra/utils/http.py | 7 +- areal/utils/name_resolve.py | 4 +- areal/utils/network.py | 93 ++++++++++++++++--- tests/conftest.py | 59 ++++++++++++ tests/test_fsdp_engine_nccl.py | 6 +- tests/test_network_ipv6_utils.py | 88 ++++++++++++++++++ 21 files changed, 386 insertions(+), 92 deletions(-) create mode 100644 tests/conftest.py create mode 100644 tests/test_network_ipv6_utils.py diff --git a/areal/engine/fsdp_engine.py b/areal/engine/fsdp_engine.py index 40b8e545ad..ba404fa6b5 100644 --- a/areal/engine/fsdp_engine.py +++ b/areal/engine/fsdp_engine.py @@ -119,7 +119,7 @@ ) from areal.utils.functional import gather_logprobs, gather_logprobs_entropy from areal.utils.hf_utils import load_hf_processor_and_tokenizer, load_hf_tokenizer -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_host_for_url, gethostip from areal.utils.offload import is_tms_enabled, torch_memory_saver from areal.utils.perf_tracer import trace_perf, trace_scope from areal.utils.save_load import get_state_dict_from_repo_id_or_path @@ -1097,15 +1097,16 @@ def _init_weight_update_from_distributed(self, meta: WeightUpdateMeta): fut = self.rollout_engine.init_weights_update_group(meta) gen_world_size = meta.gen_allocation.parallel.world_size + init_method = f"tcp://{format_host_for_url(meta.nccl_master_address)}:{meta.nccl_master_port}" self.logger.info( f"Initializing weight update group: type={meta.type} " - f"init_method=tcp://{meta.nccl_master_address}:{meta.nccl_master_port} " + f"init_method={init_method} " f"group={meta.nccl_group_name}" ) self.weight_update_group = init_custom_process_group( backend=current_platform.communication_backend, world_size=gen_world_size + 1, - init_method=f"tcp://{meta.nccl_master_address}:{meta.nccl_master_port}", + init_method=init_method, rank=0, group_name=meta.nccl_group_name, timeout=DIST_GROUP_DEFAULT_TIMEOUT, diff --git a/areal/engine/megatron_engine.py b/areal/engine/megatron_engine.py index b5e2a03360..8f66d509ff 100644 --- a/areal/engine/megatron_engine.py +++ b/areal/engine/megatron_engine.py @@ -98,7 +98,7 @@ from areal.utils.functional import gather_logprobs, gather_logprobs_entropy from areal.utils.hf_utils import load_hf_tokenizer from areal.utils.lock import DistributedLock -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_host_for_url, gethostip from areal.utils.offload import is_tms_enabled, torch_memory_saver from areal.utils.perf_tracer import trace_perf, trace_scope from areal.utils.seeding import get_seed @@ -1254,15 +1254,16 @@ def _init_weight_update_from_distributed(self, meta: WeightUpdateMeta) -> None: fut = self.rollout_engine.init_weights_update_group(meta) gen_world_size = meta.gen_allocation.parallel.world_size + init_method = f"tcp://{format_host_for_url(meta.nccl_master_address)}:{meta.nccl_master_port}" self.logger.info( f"Initializing weight update group: type={meta.type} " - f"init_method=tcp://{meta.nccl_master_address}:{meta.nccl_master_port} " + f"init_method={init_method} " f"group={self.weight_update_group_name}" ) self.weight_update_group = init_custom_process_group( backend=current_platform.communication_backend, world_size=gen_world_size + 1, - init_method=f"tcp://{meta.nccl_master_address}:{meta.nccl_master_port}", + init_method=init_method, rank=0, group_name=self.weight_update_group_name, timeout=DIST_GROUP_DEFAULT_TIMEOUT, diff --git a/areal/engine/sglang_remote.py b/areal/engine/sglang_remote.py index 46a5e559ba..9804d08c3f 100644 --- a/areal/engine/sglang_remote.py +++ b/areal/engine/sglang_remote.py @@ -31,6 +31,7 @@ from areal.infra.platforms import current_platform from areal.infra.utils.launcher import TRITON_CACHE_PATH from areal.utils import perf_tracer, stats_tracker +from areal.utils.network import format_host_for_url class SGLangBackend: @@ -194,7 +195,7 @@ def build_init_weights_group_request( ) rank_offset = 1 + server_idx * gen_parallel.tp_size payload = { - "master_address": meta.nccl_master_address, + "master_address": format_host_for_url(meta.nccl_master_address), "master_port": str(meta.nccl_master_port), "rank_offset": rank_offset, "world_size": gen_parallel.world_size + 1, diff --git a/areal/engine/vllm_remote.py b/areal/engine/vllm_remote.py index 1131dc19eb..580f953450 100644 --- a/areal/engine/vllm_remote.py +++ b/areal/engine/vllm_remote.py @@ -29,6 +29,7 @@ from areal.infra.platforms import current_platform from areal.infra.utils.launcher import TRITON_CACHE_PATH from areal.utils import logging, perf_tracer, stats_tracker +from areal.utils.network import format_host_for_url logger = logging.getLogger("vLLMEngine") @@ -198,7 +199,7 @@ def build_init_weights_group_request( gen_parallel = meta.gen_allocation.parallel rank_offset = 1 + server_idx * gen_parallel.tp_size * gen_parallel.pp_size payload = { - "master_address": meta.nccl_master_address, + "master_address": format_host_for_url(meta.nccl_master_address), "master_port": str(meta.nccl_master_port), "rank_offset": rank_offset, "world_size": gen_parallel.world_size + 1, diff --git a/areal/experimental/agent_service/worker/__main__.py b/areal/experimental/agent_service/worker/__main__.py index 1d748ef0e9..c0ab7b27e0 100644 --- a/areal/experimental/agent_service/worker/__main__.py +++ b/areal/experimental/agent_service/worker/__main__.py @@ -7,6 +7,8 @@ import httpx import uvicorn +from areal.utils.network import format_hostport + from .app import create_worker_app @@ -22,8 +24,8 @@ def main() -> None: parser.add_argument("--admin-key", default="areal-agent-admin") args = parser.parse_args() - worker_addr = f"http://{args.host}:{args.worker_port}" - proxy_addr = f"http://{args.host}:{args.proxy_port}" + worker_addr = f"http://{format_hostport(args.host, args.worker_port)}" + proxy_addr = f"http://{format_hostport(args.host, args.proxy_port)}" worker_app = create_worker_app(args.agent) proxy_app = create_data_proxy_app(worker_addr=worker_addr) diff --git a/areal/experimental/engine/archon_weight_sync.py b/areal/experimental/engine/archon_weight_sync.py index 934ef542be..31b16a2fb1 100644 --- a/areal/experimental/engine/archon_weight_sync.py +++ b/areal/experimental/engine/archon_weight_sync.py @@ -17,7 +17,7 @@ from areal.utils import name_resolve, names from areal.utils.constants import DIST_GROUP_DEFAULT_TIMEOUT from areal.utils.lock import DistributedLock -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_host_for_url, gethostip from areal.utils.perf_tracer import trace_perf if TYPE_CHECKING: @@ -70,15 +70,16 @@ def init_weight_update_group( fut = engine.rollout_engine.init_weights_update_group(meta) gen_world_size = meta.gen_allocation.parallel.world_size + init_method = f"tcp://{format_host_for_url(meta.nccl_master_address)}:{meta.nccl_master_port}" engine.logger.info( f"Initializing weight update group: type={meta.type}, " - f"init_method=tcp://{meta.nccl_master_address}:{meta.nccl_master_port}, " + f"init_method={init_method}, " f"group={meta.nccl_group_name}" ) state.group = init_custom_process_group( backend=current_platform.communication_backend, world_size=gen_world_size + 1, - init_method=f"tcp://{meta.nccl_master_address}:{meta.nccl_master_port}", + init_method=init_method, rank=0, group_name=meta.nccl_group_name, timeout=DIST_GROUP_DEFAULT_TIMEOUT, diff --git a/areal/experimental/inference_service/controller/controller.py b/areal/experimental/inference_service/controller/controller.py index 930378b320..87cbee0cbe 100644 --- a/areal/experimental/inference_service/controller/controller.py +++ b/areal/experimental/inference_service/controller/controller.py @@ -25,6 +25,7 @@ GatewayControllerConfig, ) from areal.utils import logging +from areal.utils.network import format_hostport logger = logging.getLogger("GatewayInferenceController") @@ -214,7 +215,8 @@ async def _async_initialize( # Pre-existing servers — just record their addresses self.server_infos = server_infos self._inf_addrs = [ - f"http://{info.host}:{info.port}" for info in server_infos + f"http://{format_hostport(info.host, info.port)}" + for info in server_infos ] logger.info( "Using %d pre-existing server_infos, skipping inference server fork", @@ -261,7 +263,9 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: # For each RPCGuard worker: alloc port, build cmd, fork server for rank, worker in enumerate(inf_workers): - guard_addr = f"http://{worker.ip}:{worker.worker_ports[0]}" + guard_addr = ( + f"http://{format_hostport(worker.ip, int(worker.worker_ports[0]))}" + ) resp = requests.post( f"{guard_addr}/alloc_ports", @@ -286,7 +290,7 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: ) resp.raise_for_status() - addr = f"http://{inf_host}:{inf_port}" + addr = f"http://{format_hostport(inf_host, inf_port)}" self._inf_addrs.append(addr) self.server_infos.append( LocalInfServerInfo( @@ -320,14 +324,14 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: cfg.log_level, ] - guard_addr_0 = f"http://{self.workers[0].ip}:{self.workers[0].worker_ports[0]}" + guard_addr_0 = f"http://{format_hostport(self.workers[0].ip, int(self.workers[0].worker_ports[0]))}" router_host, router_port = self._fork_on_guard( guard_addr=guard_addr_0, role="router", worker_index=0, raw_cmd=router_cmd, ) - self._router_addr = f"http://{router_host}:{router_port}" + self._router_addr = f"http://{format_hostport(router_host, router_port)}" logger.info("Router: %s", self._router_addr) # ================================================================== @@ -348,7 +352,9 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: ] for rank, worker in enumerate(inf_workers): - guard_addr = f"http://{worker.ip}:{worker.worker_ports[0]}" + guard_addr = ( + f"http://{format_hostport(worker.ip, int(worker.worker_ports[0]))}" + ) # Each data proxy connects to its corresponding inference server data_proxy_cmd = data_proxy_base_cmd + [ "--backend-addr", @@ -360,7 +366,9 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: worker_index=rank, raw_cmd=data_proxy_cmd, ) - self._data_proxy_addrs.append(f"http://{data_proxy_host}:{data_proxy_port}") + self._data_proxy_addrs.append( + f"http://{format_hostport(data_proxy_host, data_proxy_port)}" + ) logger.info("Data proxies: %s", self._data_proxy_addrs) @@ -387,7 +395,7 @@ def _build_launch_cmd(host: str, port: int) -> list[str]: worker_index=0, raw_cmd=gw_cmd, ) - self._gateway_addr = f"http://{gw_host}:{gw_port}" + self._gateway_addr = f"http://{format_hostport(gw_host, gw_port)}" logger.info("Gateway: %s", self._gateway_addr) # -- Service health checks & registration ------------------------------ @@ -992,7 +1000,7 @@ def _fork_on_guard( self._forked_services.append((guard_addr, role, worker_index)) - addr = f"http://{host}:{port}" + addr = f"http://{format_hostport(host, port)}" self._wait_for_service(f"{addr}{health_path}", role) return host, port diff --git a/areal/infra/controller/rollout_controller.py b/areal/infra/controller/rollout_controller.py index 6725510316..8fde8e5fdf 100644 --- a/areal/infra/controller/rollout_controller.py +++ b/areal/infra/controller/rollout_controller.py @@ -38,7 +38,7 @@ from areal.utils import logging, perf_tracer from areal.utils.data import cycle_dataloader from areal.utils.dynamic_import import import_from_string -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_hostport, gethostip from areal.utils.perf_tracer import trace_perf from ..staleness_manager import StalenessManager @@ -402,7 +402,9 @@ async def _async_start_proxy(self) -> None: addr=f"{server_info.host}:{server_info.port}", ) ) - self.proxy_addrs.append(f"http://{worker.ip}:{worker.worker_ports[0]}") + self.proxy_addrs.append( + f"http://{format_hostport(worker.ip, int(worker.worker_ports[0]))}" + ) await asyncio.gather(*init_tasks) logger.info(f"Proxy servers initialized. Addresses: {self.proxy_addrs}") @@ -515,7 +517,7 @@ def proxy_gateway_addr(self) -> str: """Single URL for external users.""" if self._proxy_gateway_host is None: raise RuntimeError("Proxy gateway not started") - return f"http://{self._proxy_gateway_host}:{self._proxy_gateway_port}" + return f"http://{format_hostport(self._proxy_gateway_host, self._proxy_gateway_port)}" def _stop_proxy_gateway(self) -> None: """Stop the proxy gateway server if running.""" @@ -621,7 +623,7 @@ def serve_forever(): # Signal that the loop is ready self._callback_loop_ready.set() logger.info( - f"Callback server started on {self._callback_host}:{self._callback_port}" + f"Callback server started on {format_hostport(self._callback_host, self._callback_port)}" ) self._callback_server.serve_forever() @@ -652,7 +654,7 @@ def callback_addr(self) -> str: """Return callback server address as 'host:port'.""" if self._callback_host is None or self._callback_port is None: raise RuntimeError("Callback server not started") - return f"{self._callback_host}:{self._callback_port}" + return format_hostport(self._callback_host, self._callback_port) def _resolve_task_future(self, task_id: int): """Resolve a pending future with the task result.""" diff --git a/areal/infra/launcher/sglang_server.py b/areal/infra/launcher/sglang_server.py index 348cb2a937..90ce8a10b8 100644 --- a/areal/infra/launcher/sglang_server.py +++ b/areal/infra/launcher/sglang_server.py @@ -26,7 +26,7 @@ ) from areal.infra.utils.proc import kill_process_tree from areal.utils import logging, name_resolve, names -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_hostport, gethostip logger = logging.getLogger("SGLangWrapper") @@ -187,7 +187,7 @@ def run(self): node_rank=node_rank, ) launch_server_args.append((cmd, host_ip, server_port, node_rank)) - server_addresses.append(f"http://{host_ip}:{server_port}") + server_addresses.append(f"http://{format_hostport(host_ip, server_port)}") with ThreadPoolExecutor(max_workers=n_servers_per_proc) as executor: server_iterator = executor.map( @@ -201,11 +201,13 @@ def run(self): def launch_one_server(self, cmd, host_ip, server_port, node_rank): server_process = launch_server_cmd(cmd) - wait_for_server(f"http://{host_ip}:{server_port}") + wait_for_server(f"http://{format_hostport(host_ip, server_port)}") if node_rank == 0: name = names.gen_servers(self.experiment_name, self.trial_name) name_resolve.add_subentry(name, f"{host_ip}:{server_port}") - logger.info(f"SGLang server launched at: http://{host_ip}:{server_port}") + logger.info( + f"SGLang server launched at: http://{format_hostport(host_ip, server_port)}" + ) return server_process diff --git a/areal/infra/launcher/vllm_server.py b/areal/infra/launcher/vllm_server.py index f55d5cb71c..fc2074bad2 100644 --- a/areal/infra/launcher/vllm_server.py +++ b/areal/infra/launcher/vllm_server.py @@ -24,7 +24,7 @@ from areal.infra.utils.launcher import TRITON_CACHE_PATH, get_scheduling_spec from areal.infra.utils.proc import kill_process_tree from areal.utils import logging, name_resolve, names -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import find_free_ports, format_hostport, gethostip logger = logging.getLogger("VLLMWrapper") @@ -196,7 +196,7 @@ def run(self): dist_init_addr=dist_init_addr, ) launch_server_args.append((cmd, host_ip, server_port, custom_env)) - server_addresses.append(f"http://{host_ip}:{server_port}") + server_addresses.append(f"http://{format_hostport(host_ip, server_port)}") try: with ThreadPoolExecutor(max_workers=n_servers_per_proc) as executor: @@ -240,10 +240,12 @@ def launch_one_server( custom_env: dict[str, str] | None = None, ): server_process = launch_server_cmd(cmd, custom_env=custom_env) - wait_for_server(f"http://{host_ip}:{server_port}") + wait_for_server(f"http://{format_hostport(host_ip, server_port)}") name = names.gen_servers(self.experiment_name, self.trial_name) name_resolve.add_subentry(name, f"{host_ip}:{server_port}") - logger.info(f"vllm server launched at: http://{host_ip}:{server_port}") + logger.info( + f"vllm server launched at: http://{format_hostport(host_ip, server_port)}" + ) return server_process diff --git a/areal/infra/remote_inf_engine.py b/areal/infra/remote_inf_engine.py index c465c2c8d3..835537cfe7 100644 --- a/areal/infra/remote_inf_engine.py +++ b/areal/infra/remote_inf_engine.py @@ -47,7 +47,12 @@ from areal.utils import logging, name_resolve, names from areal.utils.data import concat_padded_tensors from areal.utils.dynamic_import import import_from_string -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import ( + find_free_ports, + format_hostport, + gethostip, + split_hostport, +) from areal.utils.perf_tracer import trace_perf from .workflow_executor import WorkflowExecutor @@ -357,9 +362,13 @@ def __init__( self._proxy_gateway_addr: str | None = None self.local_server_processes: list[LocalInfServerInfo] = [] - def _wait_for_server(self, address): + def _wait_for_server(self, address: str, process: subprocess.Popen | None = None): """Wait for a server to become healthy.""" - base_url = f"http://{address}" + try: + host, port = split_hostport(address) + base_url = f"http://{format_hostport(host, port)}" + except ValueError: + base_url = f"http://{address}" tik = time.time() while time.time() - tik < self.config.setup_timeout: if self.check_health(base_url): @@ -408,7 +417,9 @@ def initialize( self.addresses = addr if isinstance(addr, list) else [addr] self.logger.info("Get server addresses from the `addr` argument.") elif len(self.local_server_processes) > 0: - self.addresses = [f"{s.host}:{s.port}" for s in self.local_server_processes] + self.addresses = [ + format_hostport(s.host, s.port) for s in self.local_server_processes + ] self.logger.info("Get server addresses from the local subprocess.") elif ( self.config.experiment_name is not None @@ -905,6 +916,16 @@ def init_weights_update_group( ) def callback(fut): + if fut.cancelled(): + return + if fut.exception() is not None: + self.logger.error( + "Failed to initialize %s group for distributed weight update for %s: %s", + current_platform.communication_backend.upper(), + meta.nccl_group_name, + repr(fut.exception()), + ) + return self.logger.info( f"Initialized {current_platform.communication_backend.upper()} group " f"for distributed weight update for {meta.nccl_group_name}." @@ -1237,14 +1258,14 @@ def launch_server(self, server_args: dict[str, Any]) -> LocalInfServerInfo: server_args["host"] = gethostip() server_args["port"] = find_free_ports(1)[0] process = self.backend.launch_server(server_args) - address = f"{server_args['host']}:{server_args['port']}" + address = format_hostport(server_args["host"], server_args["port"]) server_info = LocalInfServerInfo( host=server_args["host"], port=server_args["port"], process=process, ) try: - self._wait_for_server(address) + self._wait_for_server(address, process=process) self.local_server_processes.append(server_info) if ray.is_initialized(): # do not return with process for ray as it is not picklable @@ -1262,7 +1283,7 @@ def launch_server(self, server_args: dict[str, Any]) -> LocalInfServerInfo: raise def _shutdown_one_server(self, server_info: LocalInfServerInfo): - addr = f"{server_info.host}:{server_info.port}" + addr = format_hostport(server_info.host, server_info.port) if addr in self.addresses: self.addresses.remove(addr) if server_info.process.poll() is not None: diff --git a/areal/infra/rpc/rpc_server.py b/areal/infra/rpc/rpc_server.py index a9b03ec375..41a3d12223 100644 --- a/areal/infra/rpc/rpc_server.py +++ b/areal/infra/rpc/rpc_server.py @@ -34,7 +34,11 @@ tensor_container_to, ) from areal.utils.dynamic_import import import_from_string -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import ( + find_free_ports, + format_hostport, + gethostip, +) logger = logging.getLogger("SyncRPCServer") @@ -182,7 +186,7 @@ def _wait_for_worker_ready(host: str, port: int, timeout: float = 60) -> bool: Returns: True if the worker is ready, False if timeout is reached. """ - url = f"http://{host}:{port}/health" + url = f"http://{format_hostport(host, port)}/health" deadline = time.time() + timeout while time.time() < deadline: @@ -1035,9 +1039,16 @@ def main(): _nfs_record_root, \ _etcd3_addr, \ _fileroot - _server_host = args.host - if _server_host == "0.0.0.0": + bind_host = args.host + if bind_host == "0.0.0.0": + host_ip = gethostip() + if ":" in host_ip: + bind_host = "::" + _server_host = host_ip + elif bind_host == "::": _server_host = gethostip() + else: + _server_host = bind_host _role = args.role # Set global config for fork endpoint @@ -1059,7 +1070,7 @@ def main(): worker_id = f"{worker_role}/{worker_index}" # Make a flask server - server = make_server(args.host, args.port, app, threaded=True) + server = make_server(bind_host, args.port, app, threaded=True) _server_port = server.socket.getsockname()[1] name_resolve.reconfigure( @@ -1072,7 +1083,7 @@ def main(): key = names.worker_discovery( args.experiment_name, args.trial_name, args.role, worker_index ) - name_resolve.add(key, f"{_server_host}:{_server_port}", replace=True) + name_resolve.add(key, format_hostport(_server_host, _server_port), replace=True) global _allocated_ports _allocated_ports.add(_server_port) diff --git a/areal/infra/rpc/rtensor.py b/areal/infra/rpc/rtensor.py index fc06eda1b1..620de400db 100644 --- a/areal/infra/rpc/rtensor.py +++ b/areal/infra/rpc/rtensor.py @@ -111,8 +111,14 @@ async def _fetch_tensor( ) -> torch.Tensor: # Avoid circular import from areal.infra.rpc.serialization import deserialize_value - - url = f"http://{node_addr}/data/{shard_id}" + from areal.utils.network import format_hostport, split_hostport + + try: + host, port = split_hostport(node_addr) + base = format_hostport(host, port) + except ValueError: + base = node_addr + url = f"http://{base}/data/{shard_id}" last_exception = None for attempt in range(max_retries): @@ -246,9 +252,16 @@ def store(self, tensor: torch.Tensor) -> str: async def delete(self, node_addr: str, shard_ids: list[str]) -> None: """Delete shards via HTTP DELETE request.""" + from areal.utils.network import format_hostport, split_hostport + + try: + host, port = split_hostport(node_addr) + base = format_hostport(host, port) + except ValueError: + base = node_addr async with self._create_session() as session: async with session.delete( - f"http://{node_addr}/data/clear", json={"shard_ids": shard_ids} + f"http://{base}/data/clear", json={"shard_ids": shard_ids} ) as resp: if resp.status == 200: await resp.json() diff --git a/areal/infra/scheduler/local.py b/areal/infra/scheduler/local.py index 0dd1f54749..53aa73d3a4 100644 --- a/areal/infra/scheduler/local.py +++ b/areal/infra/scheduler/local.py @@ -45,7 +45,11 @@ from areal.infra.utils.proc import kill_process_tree, run_with_streaming_logs from areal.utils import logging, name_resolve, names from areal.utils.fs import validate_shared_path -from areal.utils.network import find_free_ports, gethostip +from areal.utils.network import ( + find_free_ports, + format_hostport, + gethostip, +) logger = logging.getLogger("LocalScheduler") @@ -268,9 +272,7 @@ async def _fork_single_worker( If specified, the forked process runs this module. """ worker_id = f"{role}/{idx}" - target_url = ( - f"http://{target_wi.worker.ip}:{target_wi.worker.worker_ports[0]}/fork" - ) + target_url = f"http://{format_hostport(target_wi.worker.ip, int(target_wi.worker.worker_ports[0]))}/fork" try: payload = {"role": role, "worker_index": idx} @@ -341,7 +343,7 @@ async def _kill_forked_worker( target_wi: WorkerInfo, ) -> None: """Kill a single forked worker via its parent's RPC server.""" - target_url = f"http://{target_wi.worker.ip}:{target_wi.worker.worker_ports[0]}/kill_forked_worker" + target_url = f"http://{format_hostport(target_wi.worker.ip, int(target_wi.worker.worker_ports[0]))}/kill_forked_worker" try: payload = {"role": role, "worker_index": idx} @@ -830,7 +832,7 @@ def get_workers(self, role: str, timeout: float | None = None) -> list[Worker]: def _is_worker_ready(self, worker_info: WorkerInfo) -> bool: port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/health" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/health" try: response = requests.get(url, timeout=2.0) @@ -844,7 +846,7 @@ def _configure_worker(self, worker_info: WorkerInfo, worker_rank: int): worker_id = worker_info.worker.id port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/configure" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/configure" try: response = requests.post( @@ -1000,7 +1002,7 @@ async def set_worker_env(self, worker_id: str, env: dict[str, str]) -> None: payload = {"env": env} port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/set_env" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/set_env" try: timeout = aiohttp.ClientTimeout(total=30.0) @@ -1091,7 +1093,7 @@ async def create_engine( # Send HTTP request to create engine port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/create_engine" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/create_engine" try: logger.debug( @@ -1226,7 +1228,7 @@ def call_engine( # Retry logic with exponential backoff port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/call" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/call" last_error = None for attempt in range(1, max_retries + 1): @@ -1342,7 +1344,7 @@ async def async_call_engine( # Route to different endpoint based on method port = int(worker_info.worker.worker_ports[0]) # Standard engine method call - url = f"http://{worker_info.worker.ip}:{port}/call" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/call" # Serialize args and kwargs serialized_args = serialize_value(list(args)) serialized_kwargs = serialize_value(kwargs) diff --git a/areal/infra/scheduler/slurm.py b/areal/infra/scheduler/slurm.py index e2d1b296d4..46d7141e6b 100644 --- a/areal/infra/scheduler/slurm.py +++ b/areal/infra/scheduler/slurm.py @@ -47,6 +47,7 @@ ) from areal.utils import logging, name_resolve, names from areal.utils.fs import validate_shared_path +from areal.utils.network import format_hostport, split_hostport from areal.utils.offload import get_tms_env_vars logger = logging.getLogger("SlurmScheduler") @@ -272,7 +273,7 @@ def _is_worker_ready(self, worker_info: SlurmWorkerInfo) -> bool: return False port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/health" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/health" try: response = requests.get(url, timeout=2.0) @@ -287,7 +288,7 @@ def _configure_worker(self, worker_info: SlurmWorkerInfo, worker_rank: int) -> N worker_id = worker_info.worker.id port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/configure" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/configure" try: response = requests.post( @@ -354,18 +355,18 @@ def _discover_worker_network(self, role: str) -> None: addr = name_resolve.get(key) except name_resolve.NameEntryNotFoundError: continue - ip, ports_str = addr.split(":") + ip, port = split_hostport(addr) worker_info.worker.ip = ip - worker_info.discovered = True - worker_ports = [ports_str] + worker_ports = [str(port)] worker_info.worker.worker_ports = worker_ports + worker_info.discovered = True self._wait_worker_ready(worker_info) # Allocate new ports from the worker if worker_info.spec.port_count > 1: resp = requests.post( - f"http://{addr}/alloc_ports", + f"http://{format_hostport(ip, port)}/alloc_ports", json=dict(count=worker_info.spec.port_count - 1), ) resp.raise_for_status() @@ -450,9 +451,7 @@ async def _fork_single_worker( If specified, the forked process runs this module. """ worker_id = f"{role}/{idx}" - target_url = ( - f"http://{target_wi.worker.ip}:{target_wi.worker.worker_ports[0]}/fork" - ) + target_url = f"http://{format_hostport(target_wi.worker.ip, int(target_wi.worker.worker_ports[0]))}/fork" try: payload = {"role": role, "worker_index": idx} @@ -504,7 +503,7 @@ async def _fork_single_worker( port_cnt = len(self._workers[target_role][0].worker.worker_ports) if port_cnt > 1: async with session.post( - f"http://{forked_host}:{forked_port}/alloc_ports", + f"http://{format_hostport(forked_host, forked_port)}/alloc_ports", json=dict(count=port_cnt - 1), ) as response: if response.status != 200: @@ -535,7 +534,7 @@ async def _kill_forked_worker( target_wi: SlurmWorkerInfo, ) -> None: """Kill a single forked worker via its parent's RPC server.""" - target_url = f"http://{target_wi.worker.ip}:{target_wi.worker.worker_ports[0]}/kill_forked_worker" + target_url = f"http://{format_hostport(target_wi.worker.ip, int(target_wi.worker.worker_ports[0]))}/kill_forked_worker" try: payload = {"role": role, "worker_index": idx} @@ -1218,7 +1217,7 @@ async def set_worker_env(self, worker_id: str, env: dict[str, str]) -> None: payload = {"env": env} port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/set_env" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/set_env" try: timeout = aiohttp.ClientTimeout(total=30.0) @@ -1303,7 +1302,7 @@ async def create_engine( } port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/create_engine" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/create_engine" try: logger.debug( @@ -1422,7 +1421,7 @@ def call_engine( } port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/call" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/call" last_error = None for attempt in range(1, max_retries + 1): @@ -1553,7 +1552,7 @@ async def async_call_engine( } port = int(worker_info.worker.worker_ports[0]) - url = f"http://{worker_info.worker.ip}:{port}/call" + url = f"http://{format_hostport(worker_info.worker.ip, port)}/call" last_error = None for attempt in range(1, max_retries + 1): diff --git a/areal/infra/utils/http.py b/areal/infra/utils/http.py index 9b619facfa..aa76abb835 100644 --- a/areal/infra/utils/http.py +++ b/areal/infra/utils/http.py @@ -5,6 +5,7 @@ import aiohttp from areal.utils import logging +from areal.utils.network import format_hostport, split_hostport DEFAULT_RETRIES = 1 DEFAULT_REQUEST_TIMEOUT = 3600 @@ -32,7 +33,11 @@ async def arequest_with_retry( timeout = DEFAULT_REQUEST_TIMEOUT last_exception = None max_retries = max_retries or DEFAULT_RETRIES - base_url = f"http://{addr}" + try: + host, port = split_hostport(addr) + base_url = f"http://{format_hostport(host, port)}" + except ValueError: + base_url = f"http://{addr}" url = f"{base_url}{endpoint}" timeo = aiohttp.ClientTimeout( diff --git a/areal/utils/name_resolve.py b/areal/utils/name_resolve.py index e67aa40391..90f627a793 100644 --- a/areal/utils/name_resolve.py +++ b/areal/utils/name_resolve.py @@ -1212,7 +1212,9 @@ def make_repository(args: "NameResolveConfig"): os.makedirs(repo.record_root, exist_ok=True) return repo elif args.type == "etcd3": - host, port = args.etcd3_addr.split(":") + from areal.utils.network import split_hostport + + host, port = split_hostport(args.etcd3_addr) return Etcd3NameRecordRepository(host=host, port=int(port)) elif args.type == "ray": return RayNameResolveRepository(actor_name=args.ray_actor_name) diff --git a/areal/utils/network.py b/areal/utils/network.py index 7f1effd7c7..cb7b1ff791 100644 --- a/areal/utils/network.py +++ b/areal/utils/network.py @@ -1,5 +1,6 @@ import random import socket +from ipaddress import ip_address def gethostname(): @@ -8,25 +9,30 @@ def gethostname(): def gethostip(probe_host: str = "8.8.8.8", probe_port: int = 80) -> str: """ - Find the local IPv4 address for outbound route to `probe_host:probe_port` (typically - a LAN/private IP). Use hostname resolution first; if it fails or returns loopback (127.*), - fall back to a UDP connect. + Find the local IP address for outbound route to `probe_host:probe_port`. Args: - probe_host: Remote IPv4 address used to trigger route selection, default to Google - Public DNS IP. + probe_host: Remote address used to trigger route selection. probe_port: Remote port used for the UDP probe. Returns: - The selected local IPv4 address as a string + The selected local IP address as a string. Supports both IPv4 and IPv6. Raises: - RuntimeError: If no suitable IPv4 address can be determined + RuntimeError: If no suitable address can be determined """ try: - ip = socket.gethostbyname(socket.gethostname()) - if ip and not ip.startswith("127."): - return ip + hostname = socket.gethostname() + infos = socket.getaddrinfo(hostname, None, socket.AF_UNSPEC, socket.SOCK_DGRAM) + for family, _, _, _, sockaddr in infos: + if family == socket.AF_INET: + ip = sockaddr[0] + if ip and not ip.startswith("127."): + return ip + elif family == socket.AF_INET6: + ip = sockaddr[0] + if ip and ip != "::1": + return ip except socket.gaierror: pass @@ -35,7 +41,72 @@ def gethostip(probe_host: str = "8.8.8.8", probe_port: int = 80) -> str: sock.connect((probe_host, probe_port)) return sock.getsockname()[0] except OSError as e: - raise RuntimeError("Could not determine host IP") from e + try: + with socket.socket(socket.AF_INET6, socket.SOCK_DGRAM) as sock: + sock.connect(("2001:4860:4860::8888", probe_port)) + ip6 = sock.getsockname()[0] + if ip6 and ip6 != "::1": + return ip6 + except OSError: + raise RuntimeError("Could not determine host IP") from e + + +def get_loopback_ip() -> str: + try: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: + sock.bind(("127.0.0.1", 0)) + return "127.0.0.1" + except OSError: + pass + if socket.has_ipv6: + try: + with socket.socket(socket.AF_INET6, socket.SOCK_STREAM) as sock: + sock.bind(("::1", 0)) + return "::1" + except OSError: + pass + raise RuntimeError("Could not determine loopback IP") + + +def format_host_for_url(host: str) -> str: + if host.startswith("[") and host.endswith("]"): + return host + if ":" in host: + return f"[{host}]" + return host + + +def format_hostport(host: str, port: int) -> str: + return f"{format_host_for_url(host)}:{port}" + + +def split_hostport(addr: str) -> tuple[str, int]: + if addr.startswith("["): + end = addr.find("]") + if end == -1: + raise ValueError(f"Invalid bracketed address: {addr}") + host = addr[1:end] + rest = addr[end + 1 :] + if not rest.startswith(":"): + raise ValueError(f"Invalid bracketed address: {addr}") + host_for_validate = host.split("%", 1)[0] + try: + ip_address(host_for_validate) + except ValueError as e: + raise ValueError(f"Invalid bracketed address: {addr}") from e + return host, int(rest[1:]) + + if addr.count(":") == 1: + host, port_s = addr.split(":", 1) + return host, int(port_s) + + host, port_s = addr.rsplit(":", 1) + host_for_validate = host.split("%", 1)[0] + try: + ip_address(host_for_validate) + except ValueError as e: + raise ValueError(f"Invalid host:port address: {addr}") from e + return host, int(port_s) def find_free_ports( diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000000..4f567127e9 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,59 @@ +import socket + +import pytest + + +def _can_bind_ipv4_loopback() -> bool: + try: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: + s.bind(("127.0.0.1", 0)) + return True + except OSError: + return False + + +def _can_bind_ipv6_loopback() -> bool: + if not socket.has_ipv6: + return False + try: + with socket.socket(socket.AF_INET6, socket.SOCK_STREAM) as s: + s.bind(("::1", 0)) + return True + except OSError: + return False + + +def _has_ipv4_route() -> bool: + try: + with socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s: + s.connect(("8.8.8.8", 80)) + ip = s.getsockname()[0] + return bool(ip) and not ip.startswith("127.") + except OSError: + return False + + +def _has_ipv6_route() -> bool: + if not socket.has_ipv6: + return False + try: + with socket.socket(socket.AF_INET6, socket.SOCK_DGRAM) as s: + s.connect(("2001:4860:4860::8888", 80)) + ip = s.getsockname()[0] + return bool(ip) and ip != "::1" + except OSError: + return False + + +@pytest.fixture(scope="session") +def ip_stack() -> dict[str, bool]: + ipv4_loopback = _can_bind_ipv4_loopback() + ipv6_loopback = _can_bind_ipv6_loopback() + ipv4_route = _has_ipv4_route() + ipv6_route = _has_ipv6_route() + return { + "ipv4_loopback": ipv4_loopback, + "ipv6_loopback": ipv6_loopback, + "ipv4_route": ipv4_route, + "ipv6_route": ipv6_route, + } diff --git a/tests/test_fsdp_engine_nccl.py b/tests/test_fsdp_engine_nccl.py index 7948a1c563..d284b1ad91 100644 --- a/tests/test_fsdp_engine_nccl.py +++ b/tests/test_fsdp_engine_nccl.py @@ -37,7 +37,7 @@ def sglang_server(): ), tp_size=1, base_gpu_id=1, - dist_init_addr=f"{host}:{dist_port}", + dist_init_addr=network.format_hostport(host, dist_port), ) # Create engine instance for server management @@ -91,7 +91,9 @@ def test_fsdpengine_nccl_weight_update_to_remote(tmp_path_factory, sglang_server backend="sglang:d1", experiment_name=EXPR_NAME, trial_name=TRIAL_NAME ) remote_engine = RemoteSGLangEngine(config) - remote_engine.initialize(addr=f"{sglang_server.host}:{sglang_server.port}") + remote_engine.initialize( + addr=network.format_hostport(sglang_server.host, sglang_server.port) + ) # Get WeightUpdateMeta meta = WeightUpdateMeta.from_fsdp_xccl( diff --git a/tests/test_network_ipv6_utils.py b/tests/test_network_ipv6_utils.py new file mode 100644 index 0000000000..273105d957 --- /dev/null +++ b/tests/test_network_ipv6_utils.py @@ -0,0 +1,88 @@ +import importlib.util +from pathlib import Path + +import pytest + + +def _load_network_module(): + root = Path(__file__).resolve().parents[1] + path = root / "areal" / "utils" / "network.py" + spec = importlib.util.spec_from_file_location("areal_utils_network", str(path)) + mod = importlib.util.module_from_spec(spec) + assert spec is not None and spec.loader is not None + spec.loader.exec_module(mod) + return mod + + +def test_ipv4_only_environment_executes_correct_branch(ip_stack): + if not (ip_stack["ipv4_route"] and not ip_stack["ipv6_route"]): + pytest.skip("Not an IPv4-only host (route-level)") + network = _load_network_module() + assert ":" not in network.gethostip() + + +def test_ipv6_only_environment_executes_correct_branch(ip_stack): + if not (ip_stack["ipv6_route"] and not ip_stack["ipv4_route"]): + pytest.skip("Not an IPv6-only host (route-level)") + network = _load_network_module() + assert ":" in network.gethostip() + + +def test_dual_stack_environment_executes_correct_branch(ip_stack): + if not (ip_stack["ipv4_route"] and ip_stack["ipv6_route"]): + pytest.skip("Not a dual-stack host (route-level)") + network = _load_network_module() + ip = network.gethostip() + assert ip not in {"127.0.0.1", "::1"} + + +def test_split_hostport_accepts_unbracketed_ipv6(): + network = _load_network_module() + host, port = network.split_hostport("2001:db8::1:8000") + assert host == "2001:db8::1" + assert port == 8000 + + +def test_split_hostport_accepts_ipv4(): + network = _load_network_module() + host, port = network.split_hostport("127.0.0.1:8000") + assert host == "127.0.0.1" + assert port == 8000 + + +def test_split_hostport_accepts_bracketed_ipv6(): + network = _load_network_module() + host, port = network.split_hostport("[2001:db8::1]:8000") + assert host == "2001:db8::1" + assert port == 8000 + + +def test_split_hostport_accepts_ipv6_with_zone_id_unbracketed(): + network = _load_network_module() + host, port = network.split_hostport("fe80::1%eth0:8000") + assert host == "fe80::1%eth0" + assert port == 8000 + + +def test_split_hostport_accepts_ipv6_with_zone_id_bracketed(): + network = _load_network_module() + host, port = network.split_hostport("[fe80::1%eth0]:8000") + assert host == "fe80::1%eth0" + assert port == 8000 + + +def test_format_hostport_brackets_ipv6(): + network = _load_network_module() + assert network.format_hostport("2001:db8::1", 8000) == "[2001:db8::1]:8000" + + +def test_format_host_for_url_idempotent(): + network = _load_network_module() + assert network.format_host_for_url("[2001:db8::1]") == "[2001:db8::1]" + assert network.format_host_for_url("127.0.0.1") == "127.0.0.1" + + +def test_get_loopback_ip_returns_bindable_address(): + network = _load_network_module() + ip = network.get_loopback_ip() + assert ip in {"127.0.0.1", "::1"} From be621da55a8c442634456435e05c978eda2837cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Nguy=E1=BB=85n=20Ph=C3=BA=20Tr=C6=B0=E1=BB=9Dng?= Date: Mon, 30 Mar 2026 12:19:03 +0700 Subject: [PATCH 2/8] fix: fix pad_packed_tensor_dict (#1104) Co-authored-by: truongnp5 --- areal/utils/data.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/areal/utils/data.py b/areal/utils/data.py index 392f7d2466..834ea23e97 100644 --- a/areal/utils/data.py +++ b/areal/utils/data.py @@ -917,6 +917,13 @@ def pad_packed_tensor_dict( raise ValueError( f"pad_to_length {pad_to_length} is smaller than total length {total_length}." ) + elif pad_length == 0: + return ( + data, + pad_length, + old_cu_seqlens, + align_to_length, + ) new_cu_seqlens = F.pad(cu_seqlens, (0, 1), value=pad_to_length) new_max_seqlen = max(max_seqlen, pad_length) padded_data = {} From 722e235a37e4a9f3e288e54629179befa494156b Mon Sep 17 00:00:00 2001 From: Gursimran Date: Sun, 29 Mar 2026 22:24:36 -0700 Subject: [PATCH 3/8] feat: megatron bridge adaptation (#1056) * feat: megatron-bridge-adaptation and dependency conficts resolution - tested TP,PP>1 megatron-bridge integration with mbridge backward compatibility - darwin with x86_64 needs special handling as torch >2.9.1 stops support - some packages conflicts due to megatron-bridge are overridden to previous versions * chore: added docs for the megatron-bridge feature * fix: handing case where load/ save in megatron-bridge does not support critic --------- Co-authored-by: Wei Fu <36355462+garrett4wade@users.noreply.github.com> --- areal/api/cli_args.py | 9 + areal/engine/megatron_engine.py | 142 +- areal/models/mcore/registry.py | 89 +- areal/tools/validate_docker_installation.py | 1 + areal/tools/validation_base.py | 1 + docs/en/_toc.yml | 1 + docs/en/reference/bridge_backend.md | 37 + docs/en/reference/tree_training.md | 13 +- docs/en/tutorial/archon.md | 24 +- docs/en/tutorial/megatron.md | 16 + docs/zh/_toc.yml | 1 + docs/zh/reference/bridge_backend.md | 36 + docs/zh/reference/tree_training.md | 12 +- docs/zh/tutorial/archon.md | 24 +- docs/zh/tutorial/megatron.md | 14 + pyproject.toml | 16 +- uv.lock | 2245 ++++++++----------- 17 files changed, 1276 insertions(+), 1405 deletions(-) create mode 100644 docs/en/reference/bridge_backend.md create mode 100644 docs/zh/reference/bridge_backend.md diff --git a/areal/api/cli_args.py b/areal/api/cli_args.py index c5786a0351..483aa9fbac 100644 --- a/areal/api/cli_args.py +++ b/areal/api/cli_args.py @@ -808,6 +808,15 @@ class MegatronEngineConfig: # FP8 Training Configuration fp8_config: FP8EngineConfig | None = None + # Bridge backend used for HF<->Megatron conversion/model creation. + bridge_type: str = field( + default="mbridge", + metadata={ + "help": "Bridge backend for MegatronEngine. Choices: 'mbridge' or 'megatron-bridge'.", + "choices": ["mbridge", "megatron-bridge"], + }, + ) + class SchedulingStrategyType(str, Enum): separation = "separation" diff --git a/areal/engine/megatron_engine.py b/areal/engine/megatron_engine.py index 8f66d509ff..b972d240a7 100644 --- a/areal/engine/megatron_engine.py +++ b/areal/engine/megatron_engine.py @@ -14,6 +14,7 @@ import mbridge import torch import torch.distributed as dist +from megatron.bridge import AutoBridge as MegatronBridgeAutoBridge from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core.distributed import DistributedDataParallel as DDP @@ -167,6 +168,7 @@ def __init__(self, config: TrainEngineConfig): self.fp8_config.direct_convert if self.enable_fp8 else False ) self.quantization_config: dict[str, int | str | list[str]] | None = None + self.bridge_cls: str = getattr(self.mcore_config, "bridge_type", "mbridge") def create_process_group(self, parallel_strategy: ParallelStrategy | None = None): if parallel_strategy is None: @@ -238,33 +240,21 @@ def initialize(self, addr: str | None, ft_spec: FinetuneSpec, *args, **kwargs): self.tokenizer = load_hf_tokenizer(self.config.path) - with patch_bridge_for_tree_training(self.enable_tree_training): - self.bridge = mbridge.AutoBridge.from_pretrained( - self.config.path, trust_remote_code=True - ) - self.bridge.dtype = self.dtype - # Set gradient checkpointing options - if self.config.gradient_checkpointing: - self.bridge.set_extra_args( - recompute_granularity=self.mcore_config.recompute_granularity, - recompute_method=self.mcore_config.recompute_method, - recompute_num_layers=self.mcore_config.recompute_num_layers, - distribute_saved_activations=self.mcore_config.distribute_saved_activations, - recompute_modules=self.mcore_config.recompute_modules, - ) - - self.logger.info( - "Using mbridge to create models and hf model save/load in MegatronEngine." - ) + with patch_bridge_for_tree_training( + self.enable_tree_training and self.bridge_cls == "mbridge" + ): + self.bridge = self._build_hf_mcore_bridge() self.hf_config, self.tf_config = make_hf_and_mcore_config( - self.config.path, dtype=self.dtype, bridge=self.bridge + self.config.path, + dtype=self.dtype, + bridge=self.bridge, + bridge_type=self.bridge_cls, ) self.tf_config = configure_pipeline_layer_splits( self.parallel_strategy, self.hf_config, self.tf_config ) - # Get quantization_config from hf_config if available (for FP8 weight updates) self.quantization_config = getattr( self.hf_config, "quantization_config", None ) @@ -272,15 +262,15 @@ def initialize(self, addr: str | None, ft_spec: FinetuneSpec, *args, **kwargs): self._check_and_apply_fp8_config() self._validate_fp8_consistency() - # initialize mcore (DDP Wrapped) GPTModel - with self.device: - models = make_mcore_model( - hf_config=self.hf_config, - tf_config=self.tf_config, - mcore_config=self.mcore_config, - bridge=self.bridge, - is_critic=self.config.is_critic, - ) + with self.device: + models = make_mcore_model( + hf_config=self.hf_config, + tf_config=self.tf_config, + mcore_config=self.mcore_config, + bridge=self.bridge, + bridge_type=self.bridge_cls, + is_critic=self.config.is_critic, + ) self.model = _MegatronModelList(models) @@ -326,6 +316,7 @@ def initialize(self, addr: str | None, ft_spec: FinetuneSpec, *args, **kwargs): primary_model = self.model[0] model_config = get_model_config(primary_model) + # NOTE: It is recommended to set this option to True for RL training on MoE models for stability. if self.mcore_config.use_deterministic_algorithms: set_deterministic_algorithms(model_config) @@ -360,6 +351,44 @@ def initialize(self, addr: str | None, ft_spec: FinetuneSpec, *args, **kwargs): self._create_optimizer(ft_spec) self._initialized = True + def _build_hf_mcore_bridge(self): + if self.bridge_cls == "mbridge": + self.bridge = mbridge.AutoBridge.from_pretrained(self.config.path, trust_remote_code=True) + self.bridge.dtype = self.dtype + if self.config.gradient_checkpointing: + self.bridge.set_extra_args( + recompute_granularity=self.mcore_config.recompute_granularity, + recompute_method=self.mcore_config.recompute_method, + recompute_num_layers=self.mcore_config.recompute_num_layers, + distribute_saved_activations=self.mcore_config.distribute_saved_activations, + recompute_modules=self.mcore_config.recompute_modules, + ) + self.logger.info( + "Using mbridge to create models and hf model save/load in MegatronEngine." + ) + + elif self.bridge_cls == "megatron-bridge": + if self.enable_tree_training: + raise NotImplementedError( + "Tree training is not supported with bridge_type='megatron-bridge'." + ) + self.bridge = MegatronBridgeAutoBridge.from_hf_pretrained( + self.config.path, + trust_remote_code=True, + dtype=self.config.dtype, + trust_remote_code=True, + ) + self.logger.info( + "Using megatron-bridge to create models and hf model save/load in MegatronEngine." + ) + + else: + self.logger.info( + "Not using bridge to create models and hf model save/load in MegatronEngine." + ) + self.bridge = None + return self.bridge + @property def initialized(self) -> bool: return self._initialized @@ -1371,16 +1400,27 @@ def _save_model_to_hf( assert self.model is not None, "Model is not initialized." os.makedirs(path, exist_ok=True) - save_weights_to_hf_with_mbridge_fast( - bridge=self.bridge, - models=self.model, - weights_path=path, - base_model_path=base_model_path, - max_shard_size_byte=int(3e9), - max_workers=None, - is_critic=self.config.is_critic, - fp8_direct_convert=self.fp8_direct_convert, - ) + if self.bridge_cls == "megatron-bridge": + if self.config.is_critic: + raise ValueError( + "Saving critic model is not supported with megatron-bridge." + ) + self.bridge.save_hf_pretrained( + self.model, + path, + source_path=base_model_path, + ) + else: + save_weights_to_hf_with_mbridge_fast( + bridge=self.bridge, + models=self.model, + weights_path=path, + base_model_path=base_model_path, + max_shard_size_byte=int(3e9), + max_workers=None, + is_critic=self.config.is_critic, + fp8_direct_convert=self.fp8_direct_convert, + ) if dist.get_rank() == 0: if tokenizer is not None: @@ -1393,14 +1433,22 @@ def _save_model_to_hf( def _load_model_from_hf(self, path: str) -> None: assert self.model is not None, "Model is not initialized." - load_weights_from_hf_with_mbridge_fast( - bridge=self.bridge, - models=self.model, - weights_path=path, - max_workers=None, - is_critic=self.config.is_critic, - fp8_direct_convert=self.fp8_direct_convert, - ) + + if self.bridge_cls == "megatron-bridge": + if self.config.is_critic: + raise ValueError( + "Loading critic model is not supported with megatron-bridge." + ) + self.bridge.load_hf_weights(self.model, hf_path=path) + else: + load_weights_from_hf_with_mbridge_fast( + bridge=self.bridge, + models=self.model, + weights_path=path, + max_workers=None, + is_critic=self.config.is_critic, + fp8_direct_convert=self.fp8_direct_convert, + ) def _prepare_mb_list(self, input_: dict[str, Any]) -> MicroBatchList: assert "attention_mask" in input_ and "input_ids" in input_ diff --git a/areal/models/mcore/registry.py b/areal/models/mcore/registry.py index 39140ce844..ad189b6f84 100644 --- a/areal/models/mcore/registry.py +++ b/areal/models/mcore/registry.py @@ -1,7 +1,9 @@ import dataclasses +from typing import Any import torch from mbridge.core.bridge import Bridge +from megatron.core import parallel_state as mpu from megatron.core import tensor_parallel from megatron.core.distributed import DistributedDataParallel as DDP from megatron.core.distributed import DistributedDataParallelConfig as MCoreDDPConfig @@ -100,12 +102,20 @@ def unwrap_to_gpt_model(model: torch.nn.Module) -> GPTModel: # Model registry for different architectures def make_hf_and_mcore_config( - hf_path: str, dtype: torch.dtype, bridge=None + hf_path: str, + dtype: torch.dtype, + bridge=None, + bridge_type: str = "mbridge", ) -> tuple[PretrainedConfig, TransformerConfig]: - if bridge is not None: + if bridge is not None and bridge_type == "mbridge": hf_config = bridge.hf_config hf_config._name_or_path = hf_path return hf_config, bridge.config + elif bridge is not None and bridge_type == "megatron-bridge": + hf_config = getattr(bridge.hf_pretrained, "config", bridge.hf_pretrained) + if hasattr(hf_config, "_name_or_path"): + hf_config._name_or_path = hf_path + return hf_config, bridge.transformer_config else: hf_config: PretrainedConfig = AutoConfig.from_pretrained( pretrained_model_name_or_path=hf_path, @@ -148,10 +158,11 @@ def make_mcore_model( hf_config: PretrainedConfig, tf_config: TransformerConfig, mcore_config: MegatronEngineConfig | None = None, - bridge: Bridge | None = None, + bridge: Bridge | Any | None = None, + bridge_type: str = "mbridge", is_critic: bool = False, ) -> list[GPTModel | DDP]: - if bridge is not None: + if bridge is not None and bridge_type == "mbridge": models = bridge.get_model( # TODO: Add DDP options when supporting training wrap_with_ddp=mcore_config.wrap_with_ddp, @@ -172,6 +183,76 @@ def make_mcore_model( _replace_output_layer_with_value_head(_model, tf_config) return models + + if bridge is not None and bridge_type == "megatron-bridge": + provider = bridge.to_megatron_provider(load_weights=False) + vpp_size = mcore_config.virtual_pipeline_parallel_size or 0 + + provider.tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size() + provider.pipeline_model_parallel_size = ( + mpu.get_pipeline_model_parallel_world_size() + ) + provider.virtual_pipeline_model_parallel_size = ( + vpp_size if vpp_size > 1 else None + ) + provider.context_parallel_size = mpu.get_context_parallel_world_size() + provider.expert_model_parallel_size = mpu.get_expert_model_parallel_world_size() + provider.expert_tensor_parallel_size = ( + mpu.get_expert_tensor_parallel_world_size() + ) + provider.sequence_parallel = mpu.get_tensor_model_parallel_world_size() > 1 + provider.pipeline_dtype = tf_config.params_dtype + + provider.recompute_granularity = mcore_config.recompute_granularity + provider.recompute_method = mcore_config.recompute_method + provider.recompute_num_layers = mcore_config.recompute_num_layers + provider.distribute_saved_activations = ( + mcore_config.distribute_saved_activations + ) + provider.recompute_modules = mcore_config.recompute_modules + + provider.account_for_embedding_in_pipeline_split = False + provider.account_for_loss_in_pipeline_split = False + + # Keep these four flags aligned with mbridge base defaults. + provider.variable_seq_lengths = True + logger.warning( + "Ignoring mcore_config.moe_token_dispatcher_type=%s for bridge_type='megatron-bridge'; " + "using 'alltoall' and variable_seq_lengths=True.", + mcore_config.moe_token_dispatcher_type, + ) + provider.moe_token_dispatcher_type = "alltoall" + provider.batch_p2p_comm = False + provider.overlap_p2p_comm = ( + vpp_size > 1 and provider.pipeline_model_parallel_size > 1 + ) + + # Aligning tf config settings with provider for consistency. + tf_config.variable_seq_lengths = provider.variable_seq_lengths + tf_config.moe_token_dispatcher_type = provider.moe_token_dispatcher_type + tf_config.batch_p2p_comm = provider.batch_p2p_comm + tf_config.overlap_p2p_comm = provider.overlap_p2p_comm + + provider.finalize() + + models = provider.provide_distributed_model( + ddp_config=MCoreDDPConfig(**dataclasses.asdict(mcore_config.ddp)), + fp16=tf_config.fp16, + bf16=tf_config.bf16, + use_megatron_fsdp=mcore_config.use_custom_fsdp, + use_torch_fsdp2=mcore_config.use_torch_fsdp2, + wrap_with_ddp=mcore_config.wrap_with_ddp, + overlap_param_gather_with_optimizer_step=mcore_config.overlap_param_gather_with_optimizer_step, + ) + models = list(models) + + if is_critic: + for model in models: + _model = unwrap_to_gpt_model(model) + _replace_output_layer_with_value_head(_model, tf_config) + + return models + else: if ( mcore_config is not None diff --git a/areal/tools/validate_docker_installation.py b/areal/tools/validate_docker_installation.py index cbb03bdc9a..840ee1b53f 100755 --- a/areal/tools/validate_docker_installation.py +++ b/areal/tools/validate_docker_installation.py @@ -54,6 +54,7 @@ class DockerInstallationValidator(BaseInstallationValidator): "flash_attn_3", "megatron-core", "mbridge", + "megatron-bridge", "causal_conv1d", } diff --git a/areal/tools/validation_base.py b/areal/tools/validation_base.py index 363651e27b..2e39dd6e57 100644 --- a/areal/tools/validation_base.py +++ b/areal/tools/validation_base.py @@ -73,6 +73,7 @@ class BaseInstallationValidator: # and should be validated dynamically by subclasses "megatron-core", "mbridge", + "megatron-bridge", "ray", "datasets", "hydra-core", diff --git a/docs/en/_toc.yml b/docs/en/_toc.yml index 79d7d5894b..f9c24c4c3b 100644 --- a/docs/en/_toc.yml +++ b/docs/en/_toc.yml @@ -42,6 +42,7 @@ parts: - file: reference/checkpointing - file: reference/metrics_tracking - file: reference/alloc_mode + - file: reference/bridge_backend - file: reference/tree_training - file: reference/rollout_workflow - file: reference/agent_workflow diff --git a/docs/en/reference/bridge_backend.md b/docs/en/reference/bridge_backend.md new file mode 100644 index 0000000000..94af93b0cf --- /dev/null +++ b/docs/en/reference/bridge_backend.md @@ -0,0 +1,37 @@ +# Megatron-HF Bridge Backend + +AReaL currently supports two bridge backends for `MegatronEngine`: + +- `mbridge` (default) +- `megatron-bridge` + +Set the backend with: + +```yaml +actor: + megatron: + bridge_type: mbridge +``` + +- Use `bridge_type=megatron-bridge` to enable the new path. +- `mbridge` is the default choice if this argument is not present + +## Why this feature exists + +- `mbridge` is being deprecated and does not provide PEFT/LoRA support. +- `megatron-bridge` supports more/ newer model architectures. +- `megatron-bridge` provides built-in PEFT/LoRA implementations. + +## Recommendation + +- For new GPU training workflows, prefer `megatron-bridge`. +- Keep `mbridge` for backward compatibility and environments that still depend on it. +- Prefer `mbridge` when using disk-based weight broadcast as it has optimized HF + load/save path. +- If you use XCCL for weight broadcast, load/save time is less important. + +## Current limitation + +- Tree-attention training in `MegatronEngine` currently supports only `mbridge`. +- The `megatron-bridge` backend is not supported in the tree-attention path yet. +- `megatron-bridge` does support faster/optimized HF model load/save implementations. diff --git a/docs/en/reference/tree_training.md b/docs/en/reference/tree_training.md index d18fd4aca1..4ce5587e18 100644 --- a/docs/en/reference/tree_training.md +++ b/docs/en/reference/tree_training.md @@ -22,11 +22,11 @@ FLOPs by up to **10x** and achieves up to **7x** acceleration. ### Supported Backends -| Backend | Status | Notes | -| -------- | --------- | ---------------------------------- | -| FSDP | Supported | Via FlexAttention with block masks | -| Megatron | Supported | Via PytorchFlexAttention module | -| Archon | Supported | Via TreeAttentionWrapper | +| Backend | Status | Notes | +| -------- | --------- | ------------------------------------------------------- | +| FSDP | Supported | Via FlexAttention with block masks | +| Megatron | Supported | `mbridge` only (`megatron-bridge` is not supported yet) | +| Archon | Supported | Via TreeAttentionWrapper | ## Configuration @@ -170,6 +170,9 @@ The dict keys are `tree_block_mask` or `tree_triton_data` depending on the backe **Key file:** `areal/engine/megatron_engine.py` +> **Current limitation**: Tree training in `MegatronEngine` currently supports only the +> `mbridge` backend. `megatron-bridge` is not supported in this path yet. + Megatron uses `patch_bridge_for_tree_training` context manager during model creation: ```python diff --git a/docs/en/tutorial/archon.md b/docs/en/tutorial/archon.md index a707d1b885..89fd0ee8cf 100644 --- a/docs/en/tutorial/archon.md +++ b/docs/en/tutorial/archon.md @@ -18,18 +18,18 @@ excellent work in making distributed training accessible through pure PyTorch AP ## Engine Comparison -| Feature | FSDPEngine | MegatronEngine | ArchonEngine | -| ----------------- | ------------------- | --------------- | --------------------------- | -| Backend | HuggingFace + FSDP2 | Megatron-Core | PyTorch-native | -| Model Source | Any HF model | Megatron models | Custom Archon models | -| torch.compile | Limited | No | Yes (default) | -| Data Parallel | FSDP2 | Megatron DP | FSDP2 | -| Tensor Parallel | PyTorch DTensor | Megatron TP | PyTorch DTensor | -| Pipeline Parallel | No | Yes (VPP) | Yes (1F1B, I1F1B, IZB, ZBV) | -| Expert Parallel | No | Full EP/ETP | Full EP/ETP | -| Context Parallel | Ulysses SP | Megatron CP | Ulysses SP | -| Supported Models | Any HF | Via mbridge | Built-in + User-defined | -| Status | Production | Production | Experimental | +| Feature | FSDPEngine | MegatronEngine | ArchonEngine | +| ----------------- | ------------------- | ------------------------ | --------------------------- | +| Backend | HuggingFace + FSDP2 | Megatron-Core | PyTorch-native | +| Model Source | Any HF model | Megatron models | Custom Archon models | +| torch.compile | Limited | No | Yes (default) | +| Data Parallel | FSDP2 | Megatron DP | FSDP2 | +| Tensor Parallel | PyTorch DTensor | Megatron TP | PyTorch DTensor | +| Pipeline Parallel | No | Yes (VPP) | Yes (1F1B, I1F1B, IZB, ZBV) | +| Expert Parallel | No | Full EP/ETP | Full EP/ETP | +| Context Parallel | Ulysses SP | Megatron CP | Ulysses SP | +| Supported Models | Any HF | mbridge/ megatron-bridge | Built-in + User-defined | +| Status | Production | Production | Experimental | ## Key Features diff --git a/docs/en/tutorial/megatron.md b/docs/en/tutorial/megatron.md index 33646a82dc..4e4e192d10 100644 --- a/docs/en/tutorial/megatron.md +++ b/docs/en/tutorial/megatron.md @@ -13,6 +13,22 @@ Shifting from FSDP to Megatron requires only a single line of change: the For a complete guide on allocation mode syntax, parallelism dimensions, and GPU calculations, see the [Allocation Mode Reference](../reference/alloc_mode.md). +## Bridge Backend Selection + +`MegatronEngine` supports two bridge backends configured by +`actor.megatron.bridge_type`: + +```yaml +actor: + megatron: + bridge_type: mbridge # default (backward compatible) +``` + +Set `bridge_type: megatron-bridge` to use the new backend. + +For trade-offs and migration guidance, see the +[Megatron Bridge Backend Reference](../reference/bridge_backend.md). + ## MoE Parallel Strategy For MoE models, Megatron supports separate parallelism for attention and FFN modules diff --git a/docs/zh/_toc.yml b/docs/zh/_toc.yml index d8a055a5bb..995e0b4f18 100644 --- a/docs/zh/_toc.yml +++ b/docs/zh/_toc.yml @@ -42,6 +42,7 @@ parts: - file: reference/checkpointing - file: reference/metrics_tracking - file: reference/alloc_mode + - file: reference/bridge_backend - file: reference/tree_training - file: reference/rollout_workflow - file: reference/agent_workflow diff --git a/docs/zh/reference/bridge_backend.md b/docs/zh/reference/bridge_backend.md new file mode 100644 index 0000000000..e8a69815a2 --- /dev/null +++ b/docs/zh/reference/bridge_backend.md @@ -0,0 +1,36 @@ +# Megatron Bridge 后端 + +AReaL 目前为 `MegatronEngine` 支持两种 bridge 后端： + +- `mbridge`（默认） +- `megatron-bridge` + +可通过以下配置选择后端： + +```yaml +actor: + megatron: + bridge_type: mbridge +``` + +- 使用 `bridge_type=megatron-bridge` 启用新路径。 +- 未显式配置时，默认使用 `mbridge`。 + +## 为什么需要这个功能 + +- `mbridge` 正在被弃用，且不支持 PEFT/LoRA。 +- `megatron-bridge` 支持更多（更新）模型架构。 +- `megatron-bridge` 提供内置 PEFT/LoRA 实现。 + +## 建议 + +- 对新的 GPU 训练工作流，优先使用 `megatron-bridge`。 +- 为兼容现有流程和依赖环境，短期内继续保留 `mbridge`。 +- 若使用磁盘进行权重广播，建议使用 `mbridge`，其 HF 模型加载/保存实现更快、更优化。 +- 若使用 XCCL 进行权重广播，加载/保存耗时影响较小。 + +## 当前限制 + +`MegatronEngine` 的 tree-attention 训练路径目前仅支持 `mbridge`，暂不支持 `megatron-bridge`。 + +由于 `mbridge` 在 HF 模型加载/保存上更快，在基于磁盘的工作流中仍是实用选择。 diff --git a/docs/zh/reference/tree_training.md b/docs/zh/reference/tree_training.md index 6814b9d9e3..5b052d8322 100644 --- a/docs/zh/reference/tree_training.md +++ b/docs/zh/reference/tree_training.md @@ -17,11 +17,11 @@ ### 支持的后端 -| 后端 | 状态 | 备注 | -| -------- | ---- | ------------------------------ | -| FSDP | 支持 | 通过 FlexAttention 和块掩码 | -| Megatron | 支持 | 通过 PytorchFlexAttention 模块 | -| Archon | 支持 | 通过 TreeAttentionWrapper | +| 后端 | 状态 | 备注 | +| -------- | ---- | ------------------------------------------ | +| FSDP | 支持 | 通过 FlexAttention 和块掩码 | +| Megatron | 支持 | 仅 `mbridge`（暂不支持 `megatron-bridge`） | +| Archon | 支持 | 通过 TreeAttentionWrapper | ## 配置 @@ -162,6 +162,8 @@ if self.enable_tree_training and ctx.trie_node is not None: **关键文件：** `areal/engine/megatron_engine.py` +> **当前限制**：`MegatronEngine` 的树训练路径目前仅支持 `mbridge` 后端， 暂不支持 `megatron-bridge`。 + Megatron 在模型创建期间使用 `patch_bridge_for_tree_training` 上下文管理器： ```python diff --git a/docs/zh/tutorial/archon.md b/docs/zh/tutorial/archon.md index 06130f02a8..66e89fdb51 100644 --- a/docs/zh/tutorial/archon.md +++ b/docs/zh/tutorial/archon.md @@ -13,18 +13,18 @@ Archon 的设计和核心实现灵感来自 [torchtitan](https://github.com/pyto ## 引擎对比 -| 特性 | FSDPEngine | MegatronEngine | ArchonEngine | -| ------------- | ------------------- | -------------- | --------------------------- | -| 后端 | HuggingFace + FSDP2 | Megatron-Core | PyTorch 原生 | -| 模型来源 | 任意 HF 模型 | Megatron 模型 | 自定义 Archon 模型 | -| torch.compile | 有限 | 否 | 是（默认） | -| 数据并行 | FSDP2 | Megatron DP | FSDP2 | -| 张量并行 | PyTorch DTensor | Megatron TP | PyTorch DTensor | -| 流水线并行 | 否 | 是（VPP） | 是（1F1B、I1F1B、IZB、ZBV） | -| 专家并行 | 否 | 完整 EP/ETP | 完整 EP/ETP | -| 上下文并行 | Ulysses SP | Megatron CP | Ulysses SP | -| 支持的模型 | 任意 HF | 通过 mbridge | 内置 + 用户自定义 | -| 状态 | 生产就绪 | 生产就绪 | 实验性 | +| 特性 | FSDPEngine | MegatronEngine | ArchonEngine | +| ------------- | ------------------- | ---------------------------------- | --------------------------- | +| 后端 | HuggingFace + FSDP2 | Megatron-Core | PyTorch 原生 | +| 模型来源 | 任意 HF 模型 | Megatron 模型 | 自定义 Archon 模型 | +| torch.compile | 有限 | 否 | 是（默认） | +| 数据并行 | FSDP2 | Megatron DP | FSDP2 | +| 张量并行 | PyTorch DTensor | Megatron TP | PyTorch DTensor | +| 流水线并行 | 否 | 是（VPP） | 是（1F1B、I1F1B、IZB、ZBV） | +| 专家并行 | 否 | 完整 EP/ETP | 完整 EP/ETP | +| 上下文并行 | Ulysses SP | Megatron CP | Ulysses SP | +| 支持的模型 | 任意 HF | 通过 bridge 后端（默认 `mbridge`） | 内置 + 用户自定义 | +| 状态 | 生产就绪 | 生产就绪 | 实验性 | ## 关键特性 diff --git a/docs/zh/tutorial/megatron.md b/docs/zh/tutorial/megatron.md index 2e35c91dae..ca944fe422 100644 --- a/docs/zh/tutorial/megatron.md +++ b/docs/zh/tutorial/megatron.md @@ -9,6 +9,20 @@ 有关分配模式语法、并行维度 和 GPU 计算的完整指南，请参阅[分配模式参考](../reference/alloc_mode.md)。 +## Bridge 后端选择 + +`MegatronEngine` 支持通过 `actor.megatron.bridge_type` 选择两种 bridge 后端： + +```yaml +actor: + megatron: + bridge_type: mbridge # 默认（向后兼容） +``` + +设置 `bridge_type: megatron-bridge` 可启用新后端。 + +关于取舍与迁移建议，请参阅 [Megatron Bridge 后端参考](../reference/bridge_backend.md)。 + ## MoE 并行策略 对于 MoE 模型，Megatron 使用混合语法支持注意力模块和 FFN 模块的独立并行。例如： diff --git a/pyproject.toml b/pyproject.toml index 63fc6b1688..55235960b8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,8 @@ classifiers = [ dependencies = [ # Core ML/AI libraries - "torch>=2.9.1,<2.11", + "torch>=2.9.1,<2.11; sys_platform != 'darwin' or platform_machine != 'x86_64'", + "torch<2.9.1; sys_platform == 'darwin' and platform_machine == 'x86_64'", "torchaudio", "torchvision", "torchdata", @@ -158,6 +159,7 @@ kernels = [ megatron = [ "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", "mbridge==0.15.1; sys_platform == 'linux' and platform_machine == 'x86_64'", + "megatron-bridge==0.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", ] # Convenience extra for CUDA training packages (no inference backend) # Install with --extra sglang or --extra vllm for a complete setup @@ -267,6 +269,18 @@ override-dependencies = [ # - litellm requires soundfile>=0.12.1,<0.13.0, sglang pins soundfile==0.13.1 "openai>=2.8.0", "soundfile>=0.12.1,<0.13.0", + + # Required by megatron-bridge but are optional (as per docs) cuda-based installs + "transformer-engine; sys_platform == 'never'", + "nv-grouped-gemm; sys_platform == 'never'", + "mamba-ssm; sys_platform == 'never'", + "causal-conv1d; sys_platform == 'never'", + "flash-linear-attention; sys_platform == 'never'", + + # Version conflicts with megatron-bridge (resorting to the existing version) + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", # Prevent megatron-bridge from installing megatron-core[dev,mlm] extras + "hydra-core==1.4.0.dev1", + "timm==1.0.16", ] # Static metadata so uv lock resolves flash-attn without downloading or building. diff --git a/uv.lock b/uv.lock index 96895bf9f0..7696872954 100644 --- a/uv.lock +++ b/uv.lock @@ -39,8 +39,16 @@ conflicts = [[ [manifest] overrides = [ + { name = "causal-conv1d", marker = "sys_platform == 'never'" }, + { name = "flash-linear-attention", marker = "sys_platform == 'never'" }, + { name = "hydra-core", specifier = "==1.4.0.dev1" }, + { name = "mamba-ssm", marker = "sys_platform == 'never'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", specifier = "==0.16.0" }, + { name = "nv-grouped-gemm", marker = "sys_platform == 'never'" }, { name = "openai", specifier = ">=2.8.0" }, { name = "soundfile", specifier = ">=0.12.1,<0.13.0" }, + { name = "timm", specifier = "==1.0.16" }, + { name = "transformer-engine", marker = "sys_platform == 'never'" }, ] [[manifest.dependency-metadata]] @@ -48,6 +56,15 @@ name = "flash-attn" version = "2.8.3" requires-dist = ["torch", "einops"] +[[package]] +name = "absl-py" +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" } +wheels = [ + { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" }, +] + [[package]] name = "accelerate" version = "1.13.0" @@ -59,9 +76,10 @@ dependencies = [ { name = "psutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { 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extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra != 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra != 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { name = "tqdm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { name = "transformers", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { name = "uvicorn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, @@ -388,6 +402,7 @@ cuda = [ { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine == 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang'" }, { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra != 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, @@ -399,6 +414,7 @@ cuda-sglang = [ { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, @@ -409,6 +425,7 @@ cuda-sglang = [ cuda-train = [ { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and 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'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, @@ -431,6 +449,7 @@ kernels = [ ] megatron = [ { name = "mbridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra 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mbridge.AutoBridge.from_pretrained(self.config.path, trust_remote_code=True) + self.bridge = mbridge.AutoBridge.from_pretrained( + self.config.path, trust_remote_code=True + ) self.bridge.dtype = self.dtype if self.config.gradient_checkpointing: self.bridge.set_extra_args( @@ -376,7 +378,6 @@ def _build_hf_mcore_bridge(self): self.config.path, trust_remote_code=True, dtype=self.config.dtype, - trust_remote_code=True, ) self.logger.info( "Using megatron-bridge to create models and hf model save/load in MegatronEngine." From 0405b5c30c815044d858f15d04d2033c9a3d7760 Mon Sep 17 00:00:00 2001 From: ag <80872136+Anguo-star@users.noreply.github.com> Date: Mon, 30 Mar 2026 15:15:35 +0800 Subject: [PATCH 5/8] fix(dataloader): prevent data drop and padding during validation for accurate metrics(pre-commit checked) (#1100) --- areal/utils/dataloader.py | 51 ++++++++++++++++++++++++++++++++++++--- 1 file changed, 48 insertions(+), 3 deletions(-) diff --git a/areal/utils/dataloader.py b/areal/utils/dataloader.py index ab0a424154..9b6bba5d72 100644 --- a/areal/utils/dataloader.py +++ b/areal/utils/dataloader.py @@ -1,9 +1,10 @@ from collections.abc import Callable +from datasets import Dataset from torch.utils.data import DistributedSampler from torchdata.stateful_dataloader import StatefulDataLoader -from areal.api.cli_args import _DatasetConfig +from areal.api.cli_args import ValidDatasetConfig, _DatasetConfig def create_dataloader( @@ -26,17 +27,61 @@ def create_dataloader( raise ValueError( f"batch size({dataset_config.batch_size}) must be divisible by world_size({world_size})!" ) + + sampler_cls = DistributedSampler + drop_sampler_last = True + if isinstance(dataset_config, ValidDatasetConfig): + sampler_cls = EvalDistributedSampler + drop_sampler_last = False + return StatefulDataLoader( dataset, batch_size=dataset_config.batch_size // world_size, - sampler=DistributedSampler( + sampler=sampler_cls( dataset, world_size, rank, shuffle=dataset_config.shuffle, - drop_last=True, + drop_last=drop_sampler_last, ), drop_last=dataset_config.drop_last, num_workers=dataset_config.num_workers, collate_fn=collate_fn or (lambda x: x), ) + + +class EvalDistributedSampler(DistributedSampler): + r"""A DistributedSampler specifically designed for evaluation (Validation/Testing). + + Unlike the standard :class:`~torch.utils.data.DistributedSampler`, this sampler + **does not pad** the dataset to make it evenly divisible by the number of replicas. + + In the standard implementation, extra indices are added to ensure every rank has + the exact same `num_samples`. While useful for training (synchronized batch sizes), + this causes some validation samples to be evaluated twice, leading to biased metrics + (e.g., inaccurate accuracy or loss). + + **Key Behaviors:** + 1. **Exact Evaluation:** Ensures every sample in the dataset is evaluated exactly + once across the entire cluster. + 2. **Uneven Split:** Ranks may receive different amounts of data (difference is at most 1). + For example, if N=10 and Replicas=3, ranks get [4, 3, 3] samples respectively, + instead of [4, 4, 4] with padding. + """ + + def __init__( + self, + dataset: Dataset, + num_replicas: int | None = None, + rank: int | None = None, + shuffle: bool = True, + seed: int = 0, + drop_last: bool = False, + ) -> None: + super().__init__(dataset, num_replicas, rank, shuffle, seed, drop_last) + + if not drop_last: + self.total_size = len(dataset) + + if self.rank + (self.num_samples - 1) * self.num_replicas >= self.total_size: + self.num_samples -= 1 From cbe35f5a4b866596d996d5690085eda0577708f5 Mon Sep 17 00:00:00 2001 From: Wentai Zhang Date: Mon, 30 Mar 2026 16:01:04 +0800 Subject: [PATCH 6/8] fix(archon): add missing POST /data/batch endpoint to data proxy (#1105) * fix(archon): add missing POST /data/batch endpoint to data proxy PR #1077 added batch RTensor fetching via POST /data/batch but only implemented the endpoint on the Flask RPC server (rpc_server.py), missing the FastAPI data proxy. This caused RTensor.localize() to fail with HTTP 405 in integration tests that use the data proxy. Refs: #1077 * fix(archon): harden data proxy batch endpoint with Flask-parity error handling Align POST /data/batch error responses, JSON parsing, and exception handling with the Flask rpc_server.py counterpart to ensure identical behavior across both servers. Key changes: - Replace HTTPException with JSONResponse for Flask-compatible error bodies - Add outer try/except with traceback logging matching Flask pattern - Normalise falsy/non-dict JSON payloads via or {} + isinstance guard - Add 12 unit tests for all RTensor data proxy endpoints (no GPU) Refs: #1077, #1105 --- .../inference_service/data_proxy/app.py | 64 +++- .../test_data_proxy_rtensor.py | 346 ++++++++++++++++++ 2 files changed, 409 insertions(+), 1 deletion(-) create mode 100644 tests/experimental/inference_service/test_data_proxy_rtensor.py diff --git a/areal/experimental/inference_service/data_proxy/app.py b/areal/experimental/inference_service/data_proxy/app.py index 04832d5ed8..1c2a1402da 100644 --- a/areal/experimental/inference_service/data_proxy/app.py +++ b/areal/experimental/inference_service/data_proxy/app.py @@ -6,8 +6,8 @@ import orjson from fastapi import FastAPI, HTTPException, Request +from fastapi.responses import JSONResponse, StreamingResponse from fastapi.responses import Response as RawResponse -from fastapi.responses import StreamingResponse from openai.types.chat.completion_create_params import CompletionCreateParams from pydantic import BaseModel @@ -384,6 +384,68 @@ async def export_trajectories( # via HttpRTensorBackend._fetch_tensor(). # ========================================================================= + @app.post("/data/batch") + async def retrieve_data_shard_batch(request: Request): + """Retrieve multiple tensor shards in one request. + + Mirrors the ``POST /data/batch`` endpoint on the Flask RPC server + (``rpc_server.py``) so that ``HttpRTensorBackend._fetch_shard_group`` + works against data-proxy addresses. + """ + try: + try: + payload = (await request.json()) or {} + except Exception: + payload = {} + if not isinstance(payload, dict): + payload = {} + shard_ids = payload.get("shard_ids", []) + if not isinstance(shard_ids, list) or not all( + isinstance(sid, str) for sid in shard_ids + ): + return JSONResponse( + status_code=400, + content={ + "status": "error", + "message": "Expected JSON body with string list field 'shard_ids'", + }, + ) + + data = [] + missing: list[str] = [] + for sid in shard_ids: + try: + data.append(rtensor_storage.fetch(sid)) + except KeyError: + missing.append(sid) + + if missing: + return JSONResponse( + status_code=400, + content={ + "status": "error", + "message": "One or more requested shards were not found", + "missing_shard_ids": missing, + }, + ) + + serialized_data = serialize_value(data) + data_bytes = orjson.dumps(serialized_data) + logger.debug( + "Retrieved %d RTensor shards in batch (size=%d bytes)", + len(shard_ids), + len(data_bytes), + ) + return RawResponse( + content=data_bytes, media_type="application/octet-stream" + ) + except Exception as e: + logger.error("Error retrieving batch shards: %s", e, exc_info=True) + return JSONResponse( + status_code=500, + content={"status": "error", "message": str(e)}, + ) + @app.put("/data/{shard_id}") async def store_data_shard(shard_id: str, request: Request): """Store a tensor shard in local RTensor storage.""" diff --git a/tests/experimental/inference_service/test_data_proxy_rtensor.py b/tests/experimental/inference_service/test_data_proxy_rtensor.py new file mode 100644 index 0000000000..ac3d7051a6 --- /dev/null +++ b/tests/experimental/inference_service/test_data_proxy_rtensor.py @@ -0,0 +1,346 @@ +"""Unit tests for RTensor storage endpoints in the data proxy FastAPI app. + +Covers all 4 RTensor endpoints: + PUT /data/{shard_id} — store a tensor shard + GET /data/{shard_id} — retrieve a tensor shard + POST /data/batch — batch retrieve tensor shards + DELETE /data/clear — clear specified tensor shards +""" + +from __future__ import annotations + +import uuid +from unittest.mock import AsyncMock, MagicMock + +import httpx +import orjson +import pytest +import pytest_asyncio +import torch + +from areal.experimental.inference_service.data_proxy.app import create_app +from areal.experimental.inference_service.data_proxy.config import DataProxyConfig +from areal.experimental.inference_service.data_proxy.session import SessionStore +from areal.infra.rpc import rtensor as rtensor_storage +from areal.infra.rpc.serialization import deserialize_value, serialize_value + +# ============================================================================= +# Fixtures +# ============================================================================= + + +@pytest.fixture(autouse=True) +def clear_rtensor_storage(): + rtensor_storage._storage.clear() + yield + rtensor_storage._storage.clear() + + +@pytest.fixture +def config(): + return DataProxyConfig( + host="127.0.0.1", + port=18082, + backend_addr="http://mock-sglang:30000", + tokenizer_path="mock-tokenizer", + request_timeout=10.0, + ) + + +@pytest_asyncio.fixture +async def client(config): + from areal.experimental.inference_service.data_proxy.pause import PauseState + + app = create_app(config) + pause_state = PauseState() + + app.state.tokenizer = MagicMock() + app.state.inf_bridge = MagicMock() + app.state.inf_bridge.pause = AsyncMock() + app.state.inf_bridge.resume = AsyncMock() + app.state.areal_client = MagicMock() + app.state.pause_state = pause_state + app.state.config = config + app.state.session_store = SessionStore() + app.state.version = 0 + + transport = httpx.ASGITransport(app=app) + async with httpx.AsyncClient(transport=transport, base_url="http://test") as c: + yield c + + +# ============================================================================= +# Helpers +# ============================================================================= + + +def _serialize_tensor_body(tensor: torch.Tensor) -> bytes: + return orjson.dumps(serialize_value(tensor)) + + +def _deserialize_response_bytes(content: bytes) -> torch.Tensor: + return deserialize_value(orjson.loads(content)) + + +def _deserialize_batch_response_bytes(content: bytes) -> list[torch.Tensor]: + return deserialize_value(orjson.loads(content)) + + +# ============================================================================= +# Tests +# ============================================================================= + + +class TestDataProxyRTensor: + @pytest.mark.asyncio + async def test_put_store_shard(self, client): + """PUT /data/{shard_id} stores tensor → 200 with status ok.""" + shard_id = str(uuid.uuid4()) + tensor = torch.tensor([1.0, 2.0, 3.0]) + body = _serialize_tensor_body(tensor) + + resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + + assert resp.status_code == 200 + data = resp.json() + assert data["status"] == "ok" + + @pytest.mark.asyncio + async def test_get_retrieve_shard(self, client): + """PUT a tensor then GET it back → deserialized tensor matches original.""" + shard_id = str(uuid.uuid4()) + tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]]) + body = _serialize_tensor_body(tensor) + + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + get_resp = await client.get(f"/data/{shard_id}") + assert get_resp.status_code == 200 + + retrieved = _deserialize_response_bytes(get_resp.content) + torch.testing.assert_close(retrieved, tensor) + + @pytest.mark.asyncio + async def test_get_unknown_shard_returns_404(self, client): + """GET /data/{random-uuid} for a non-existent shard → 404.""" + shard_id = str(uuid.uuid4()) + resp = await client.get(f"/data/{shard_id}") + assert resp.status_code == 404 + + @pytest.mark.asyncio + async def test_post_batch_retrieve(self, client): + """PUT 3 tensors with different shapes, POST /data/batch → all 3 match originals.""" + tensors = [ + torch.tensor([1.0, 2.0, 3.0]), + torch.zeros(2, 4), + torch.ones(3, 3, 3), + ] + shard_ids = [] + + for tensor in tensors: + shard_id = str(uuid.uuid4()) + shard_ids.append(shard_id) + body = _serialize_tensor_body(tensor) + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + batch_resp = await client.post( + "/data/batch", + json={"shard_ids": shard_ids}, + ) + assert batch_resp.status_code == 200 + + retrieved_list = _deserialize_batch_response_bytes(batch_resp.content) + assert len(retrieved_list) == len(tensors) + for original, retrieved in zip(tensors, retrieved_list): + torch.testing.assert_close(retrieved, original) + + @pytest.mark.asyncio + async def test_post_batch_missing_shard_returns_400(self, client): + """POST /data/batch with a missing shard_id → 400 with error details.""" + shard_id = str(uuid.uuid4()) + tensor = torch.tensor([1.0, 2.0]) + body = _serialize_tensor_body(tensor) + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + nonexistent_id = "nonexistent-id" + batch_resp = await client.post( + "/data/batch", + json={"shard_ids": [shard_id, nonexistent_id]}, + ) + assert batch_resp.status_code == 400 + data = batch_resp.json() + assert data["status"] == "error" + assert data["message"] == "One or more requested shards were not found" + assert data["missing_shard_ids"] == [nonexistent_id] + + @pytest.mark.asyncio + async def test_post_batch_invalid_body_returns_400(self, client): + """POST /data/batch with shard_ids as a non-list → 400 with error message.""" + batch_resp = await client.post( + "/data/batch", + json={"shard_ids": "not-a-list"}, + ) + assert batch_resp.status_code == 400 + data = batch_resp.json() + assert data["status"] == "error" + assert ( + data["message"] == "Expected JSON body with string list field 'shard_ids'" + ) + + @pytest.mark.asyncio + async def test_delete_clear_shards(self, client): + """PUT 2 tensors, DELETE /data/clear → 200 with cleared_count. Then GET → 404.""" + shard_ids = [] + for _ in range(2): + shard_id = str(uuid.uuid4()) + shard_ids.append(shard_id) + tensor = torch.rand(4) + body = _serialize_tensor_body(tensor) + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + delete_resp = await client.request( + "DELETE", + "/data/clear", + json={"shard_ids": shard_ids}, + ) + assert delete_resp.status_code == 200 + data = delete_resp.json() + assert "cleared_count" in data + assert data["cleared_count"] == 2 + + for shard_id in shard_ids: + get_resp = await client.get(f"/data/{shard_id}") + assert get_resp.status_code == 404 + + @pytest.mark.asyncio + async def test_post_batch_malformed_json_returns_200_empty(self, client): + """POST /data/batch with non-JSON body → graceful fallback (empty batch). + + Mirrors Flask's ``get_json(silent=True) or {}`` which silently + returns an empty dict on parse failure, yielding an empty 200. + """ + resp = await client.post( + "/data/batch", + content=b"this is not json", + headers={"Content-Type": "application/json"}, + ) + assert resp.status_code == 200 + assert resp.headers["content-type"] == "application/octet-stream" + batch = _deserialize_batch_response_bytes(resp.content) + assert batch == [] + + @pytest.mark.asyncio + async def test_post_batch_serialization_error_returns_500( + self, client, monkeypatch + ): + """Serialization failure in /data/batch → 500 with Flask-compatible body.""" + shard_id = str(uuid.uuid4()) + tensor = torch.tensor([1.0, 2.0]) + body = _serialize_tensor_body(tensor) + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + from areal.experimental.inference_service.data_proxy import app as app_module + + def _boom(data): + raise RuntimeError("serialization kaboom") + + monkeypatch.setattr(app_module, "serialize_value", _boom) + + batch_resp = await client.post( + "/data/batch", + json={"shard_ids": [shard_id]}, + ) + assert batch_resp.status_code == 500 + data = batch_resp.json() + assert data["status"] == "error" + assert "serialization kaboom" in data["message"] + + @pytest.mark.asyncio + async def test_post_batch_null_json_returns_200_empty(self, client): + """POST /data/batch with ``null`` JSON body → empty batch 200. + + Flask ``get_json(silent=True) or {}`` normalises falsy values to ``{}``. + """ + resp = await client.post( + "/data/batch", + content=b"null", + headers={"Content-Type": "application/json"}, + ) + assert resp.status_code == 200 + assert resp.headers["content-type"] == "application/octet-stream" + batch = _deserialize_batch_response_bytes(resp.content) + assert batch == [] + + @pytest.mark.asyncio + async def test_post_batch_non_dict_json_returns_200_empty(self, client): + """POST /data/batch with a JSON array → empty batch 200. + + Truthy non-dict payloads are normalised to ``{}`` to match Flask. + """ + resp = await client.post( + "/data/batch", + content=b"[1, 2, 3]", + headers={"Content-Type": "application/json"}, + ) + assert resp.status_code == 200 + assert resp.headers["content-type"] == "application/octet-stream" + batch = _deserialize_batch_response_bytes(resp.content) + assert batch == [] + + @pytest.mark.asyncio + async def test_post_batch_fetch_runtime_error_returns_500( + self, client, monkeypatch + ): + """Non-KeyError failure during fetch → outer try/except returns 500.""" + shard_id = str(uuid.uuid4()) + tensor = torch.tensor([1.0]) + body = _serialize_tensor_body(tensor) + put_resp = await client.put( + f"/data/{shard_id}", + content=body, + headers={"Content-Type": "application/octet-stream"}, + ) + assert put_resp.status_code == 200 + + def _broken_fetch(sid): + raise RuntimeError("storage corrupted") + + monkeypatch.setattr(rtensor_storage, "fetch", _broken_fetch) + + batch_resp = await client.post( + "/data/batch", + json={"shard_ids": [shard_id]}, + ) + assert batch_resp.status_code == 500 + data = batch_resp.json() + assert data["status"] == "error" + assert "storage corrupted" in data["message"] From 61281ba8851e6d1cf8c30794a5391359b4e324b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=97=AE=E9=97=AE=E7=A5=9E=E5=A5=87=E6=B5=B7=E8=9E=BA?= Date: Mon, 30 Mar 2026 16:06:12 +0800 Subject: [PATCH 7/8] refactor(engine): abstract CUDA calls via current_platform in PerLayerOptimWrapper (#1108) Replace hardcoded torch.cuda.Stream, torch.cuda.Event, torch.cuda.stream(), torch.cuda.current_stream(), and torch.cuda.empty_cache() with current_platform equivalents to support non-CUDA accelerators. Resolves two TODO comments about platform abstraction. --- areal/engine/fsdp_utils/optimizer.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/areal/engine/fsdp_utils/optimizer.py b/areal/engine/fsdp_utils/optimizer.py index 537712e14a..0b1c77a040 100644 --- a/areal/engine/fsdp_utils/optimizer.py +++ b/areal/engine/fsdp_utils/optimizer.py @@ -11,6 +11,8 @@ from torch.nn import Parameter from torch.optim.adam import adam as _adam_fn +from areal.infra.platforms import current_platform + def to_precision_dtype(dtype_str: str) -> torch.dtype: """ @@ -476,12 +478,11 @@ def refresh_states(self) -> None: def _init_streams_and_events(self) -> None: """Pre-allocate streams and events for pipeline synchronization.""" - # TODO: abstract via current_platform for non-CUDA devices num_groups = len(self._layer_param_groups) - self._h2d_stream = torch.cuda.Stream(device=self.device) - self._d2h_stream = torch.cuda.Stream(device=self.device) - self._compute_end_events = [torch.cuda.Event() for _ in range(num_groups)] - self._h2d_end_events = [torch.cuda.Event() for _ in range(num_groups)] + self._h2d_stream = current_platform.Stream(device=self.device) + self._d2h_stream = current_platform.Stream(device=self.device) + self._compute_end_events = [current_platform.Event() for _ in range(num_groups)] + self._h2d_end_events = [current_platform.Event() for _ in range(num_groups)] # ------------------------------------------------------------------ # Per-layer transfer helpers @@ -583,8 +584,7 @@ def step(self) -> None: """Per-layer optimizer step with async prefetch pipeline.""" h2d_stream = self._h2d_stream d2h_stream = self._d2h_stream - # TODO: abstract via current_platform for non-CUDA devices - compute_stream = torch.cuda.current_stream(self.device) + compute_stream = current_platform.current_stream(self.device) num_groups = len(self._layer_param_groups) layer_states: list[dict[int, ParamTransferState] | None] = [None] * num_groups @@ -593,7 +593,7 @@ def step(self) -> None: # Prefetch initial layers for i in range(min(self.prefetch_layers + 1, num_groups)): - with torch.cuda.stream(h2d_stream): + with current_platform.stream(h2d_stream): layer_states[i] = self._prefetch_layer(i) h2d_stream.record_event(h2d_end_events[i]) @@ -609,13 +609,13 @@ def step(self) -> None: # Prefetch next layer (overlaps with D2H below) next_idx = i + self.prefetch_layers + 1 if next_idx < num_groups: - with torch.cuda.stream(h2d_stream): + with current_platform.stream(h2d_stream): layer_states[next_idx] = self._prefetch_layer(next_idx) h2d_stream.record_event(h2d_end_events[next_idx]) # Offload current layer (waits only for this layer's compute) d2h_stream.wait_event(compute_end_events[i]) - with torch.cuda.stream(d2h_stream): + with current_platform.stream(d2h_stream): cur_states_offload = layer_states[i] assert cur_states_offload is not None, f"Layer {i} already freed" self._offload_layer(cur_states_offload) @@ -628,4 +628,4 @@ def step(self) -> None: # Prevent cross-phase cache pollution: return freed optimizer state # blocks to driver so forward/backward can't repurpose them. - torch.cuda.empty_cache() + current_platform.empty_cache() From 2ddd9595c2b374842068f6547720399f9157c945 Mon Sep 17 00:00:00 2001 From: HT-Yuan <70688525+HT-Yuan@users.noreply.github.com> Date: Mon, 30 Mar 2026 20:04:20 +0800 Subject: [PATCH 8/8] feat(fsdp): pipeline weight sync with single pending bucket (#1074) - Split weight update into async bucket start + explicit wait - Add _PendingWeightUpdateBucket dataclass for async tracking - Overlap bucket N-1 broadcast with bucket N all-gather - Keep training ranks aligned before entering next collective --- areal/engine/fsdp_engine.py | 131 +++++++++++++++++++++++++++--------- 1 file changed, 100 insertions(+), 31 deletions(-) diff --git a/areal/engine/fsdp_engine.py b/areal/engine/fsdp_engine.py index ba404fa6b5..57763e6abc 100644 --- a/areal/engine/fsdp_engine.py +++ b/areal/engine/fsdp_engine.py @@ -160,6 +160,14 @@ def to_dict(self) -> dict[str, Any]: return {f.name: getattr(self, f.name) for f in dataclasses.fields(self)} +@dataclasses.dataclass +class _PendingWeightUpdateBucket: + handles: list[Any] + fut: Future[None] + named_tensors: list[tuple[str, torch.Tensor]] + stream: torch.cuda.Stream | None = None + + class FSDPEngine(TrainEngine): def __init__(self, config: TrainEngineConfig): self.config = config @@ -1031,14 +1039,15 @@ def _get_full_tensor(self, param: nn.Parameter) -> torch.Tensor: tensor = tensor.to(current_platform.device_type) return tensor - def _update_bucket_weights_from_distributed( + def _update_bucket_weights_from_distributed_async( self, meta: WeightUpdateMeta, named_tensors: list[tuple[str, nn.Parameter | torch.Tensor]], - ): + stream: torch.cuda.Stream | None = None, + ) -> _PendingWeightUpdateBucket | None: # Early exit when chunk size is relatively small if not named_tensors: - return + return None param_specs = [ ParamSpec( @@ -1067,18 +1076,48 @@ def _update_bucket_weights_from_distributed( fut = self.rollout_engine.update_weights_from_distributed(meta, param_specs) handles = [] - for _, tensor in named_tensors: - handles.append( - dist.broadcast( - tensor, src=0, group=self.weight_update_group, async_op=True + if stream is not None: + stream.wait_stream(torch.cuda.current_stream()) + context = torch.cuda.stream(stream) + else: + context = nullcontext() + + with context: + for _, tensor in named_tensors: + handles.append( + dist.broadcast( + tensor, src=0, group=self.weight_update_group, async_op=True + ) ) - ) - for handle in handles: + + return _PendingWeightUpdateBucket( + handles=handles, + fut=fut, + named_tensors=named_tensors, + stream=stream, + ) + + def _wait_pending_weight_update_bucket( + self, pending_bucket: _PendingWeightUpdateBucket | None + ): + if pending_bucket is None: + return + + for handle in pending_bucket.handles: handle.wait() - fut.result() + pending_bucket.fut.result() + pending_bucket.named_tensors.clear() - named_tensors.clear() + def _update_bucket_weights_from_distributed( + self, + meta: WeightUpdateMeta, + named_tensors: list[tuple[str, nn.Parameter | torch.Tensor]], + ): + pending_bucket = self._update_bucket_weights_from_distributed_async( + meta, named_tensors + ) + self._wait_pending_weight_update_bucket(pending_bucket) def _init_weight_update_from_distributed(self, meta: WeightUpdateMeta): assert meta.type == "xccl" @@ -1116,23 +1155,32 @@ def _init_weight_update_from_distributed(self, meta: WeightUpdateMeta): @trace_perf("fsdp_engine.update_weights_from_distributed", category="comm") def _update_weights_from_distributed(self, meta: WeightUpdateMeta): - """Broadcast parameters (chunked) from rank 0 (FSDP2 compatible).""" + """Broadcast parameters with single-pending-bucket pipelining.""" # Reset weight weight meta with local info meta.nccl_master_address = self.weight_update_master_addr meta.nccl_master_port = self.weight_update_master_port meta.nccl_group_name = self.weight_update_group_name - if dist.get_rank() == 0: + main_rank = dist.get_rank() == 0 + if main_rank: self.rollout_engine.pause_generation() dist.barrier(group=self.cpu_group) weight_chunked_mem_size = meta.weight_chunked_mem_mb * 1024 * 1024 - main_rank = dist.get_rank() == 0 + broadcast_stream = None + + if ( + main_rank + and current_platform.device_type == "cuda" + and torch.cuda.is_available() + ): + broadcast_stream = torch.cuda.Stream() buffer_size = 0 named_tensors: list[tuple[str, torch.Tensor]] = [] + pending_bucket: _PendingWeightUpdateBucket | None = None if self.config.use_lora: # For LoRA, only iterate over trainable LoRA parameters @@ -1145,29 +1193,50 @@ def _update_weights_from_distributed(self, meta: WeightUpdateMeta): # For full model, iterate over all parameters param_iterator = self._get_model_name_parameters() - for name, param in param_iterator: - tensor = self._get_full_tensor(param) - - # Ranks other than 0 only help to get the full tensor - if not main_rank: - continue + try: + for name, param in param_iterator: + # Ranks other than 0 only help to get the full tensor + tensor = self._get_full_tensor(param) + if not main_rank: + continue + + tensor_size = tensor.numel() * tensor.element_size() + bucket_overflow = ( + buffer_size > 0 + and tensor_size + buffer_size > weight_chunked_mem_size + ) + if bucket_overflow: + # Only middle buckets need drain+align before the next all-gather. + if pending_bucket is not None: + self._wait_pending_weight_update_bucket(pending_bucket) + pending_bucket = None + + pending_bucket = self._update_bucket_weights_from_distributed_async( + meta, + named_tensors, + stream=broadcast_stream, + ) - tensor_size = tensor.numel() * tensor.element_size() + named_tensors = [] + buffer_size = 0 - if tensor_size + buffer_size > weight_chunked_mem_size: - self._update_bucket_weights_from_distributed(meta, named_tensors) - buffer_size = 0 + buffer_size += tensor_size + named_tensors.append((name, tensor)) - named_tensors.append((name, tensor)) - buffer_size += tensor_size + if pending_bucket: + self._wait_pending_weight_update_bucket(pending_bucket) + pending_bucket = None - # Process remaining parameters - if named_tensors: - self._update_bucket_weights_from_distributed(meta, named_tensors) + # Process remaining parameters + if buffer_size > 0: + self._update_bucket_weights_from_distributed(meta, named_tensors) + finally: + if main_rank and pending_bucket is not None: + self._wait_pending_weight_update_bucket(pending_bucket) + pending_bucket = None dist.barrier(group=self.cpu_group) - - if dist.get_rank() == 0: + if main_rank: self.rollout_engine.continue_generation() current_platform.synchronize()