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[Diffusion] Add loader-owned host-weight plans for DLO (TP=1) (#6213)

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Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com>
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Hongsheng Liu authored and GitHub committed 2026-08-16 11:02:38 +08:00
1 parent 3875f8ded4
commit 9d2bb23ff6
17 files changed
+2009 -615
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examples/offline_inference/minimax_h3/dlo_dp2_lifecycle.py → examples/offline_inference/minimax_h3/dlo_lifecycle.py
+60 -25
View File
@@ -1,21 +1,32 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
"""Reproduce MiniMax-H3 request-mode and DLO/DP2 lifecycle behavior.
"""Validate MiniMax-H3 request-mode and DLO lifecycle behavior.
The DLO modes accept any positive data-parallel size. With AllGather enabled,
the script submits one complete DP wave and verifies recovery after an invalid
wave. It also checks that every mode shuts down all worker processes cleanly.
Examples:
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
VLLM_OMNI_DLO_DP_WAVE_TIMEOUT=600 \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
--model /path/to/MiniMax-H3/FL2VA --mode dlo --dp-size 4
# Rank-local mmap storage, with no DLO collective or DP wave scheduling.
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
CUDA_VISIBLE_DEVICES=0,1 \
python examples/offline_inference/minimax_h3/dlo_dp2_lifecycle.py \
--model /path/to/MiniMax-H3/FL2VA --mode dlo-dp2
python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
--model /path/to/MiniMax-H3/FL2VA --mode dlo-no-allgather --dp-size 2
VLLM_WORKER_MULTIPROC_METHOD=spawn \
VLLM_OMNI_VIDEO_SYNC_TIMEOUT=14400 \
CUDA_VISIBLE_DEVICES=0,1 \
python examples/offline_inference/minimax_h3/dlo_dp2_lifecycle.py \
--model /path/to/MiniMax-H3/FL2VA --mode request
python examples/offline_inference/minimax_h3/dlo_lifecycle.py \
--model /path/to/MiniMax-H3/FL2VA --mode request --tp-size 2
"""
from __future__ import annotations
@@ -40,14 +51,33 @@ DEFAULT_PROMPTS = (
)
def positive_int(value: str) -> int:
parsed = int(value)
if parsed < 1:
raise argparse.ArgumentTypeError("must be a positive integer")
return parsed
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--model", required=True, help="Path to MiniMax-H3/FL2VA")
parser.add_argument(
"--mode",
choices=("dlo-dp2", "request"),
choices=("dlo", "dlo-no-allgather", "request"),
required=True,
help="DLO AllGather DP2 or ordinary non-DLO TP2 request mode",
help="DLO (with or without AllGather) or ordinary non-DLO request mode",
)
parser.add_argument(
"--dp-size",
type=positive_int,
default=2,
help="DLO data-parallel size (for example 1, 2, 4, or 8; default: 2)",
)
parser.add_argument(
"--tp-size",
type=positive_int,
default=2,
help="Tensor-parallel size used only by request mode (default: 2)",
)
parser.add_argument("--steps", type=int, default=2)
parser.add_argument("--duration", type=float, default=5.0)
@@ -60,10 +90,11 @@ def parse_args() -> argparse.Namespace:
def engine_kwargs(args: argparse.Namespace) -> dict[str, Any]:
is_dlo = args.mode in {"dlo", "dlo-no-allgather"}
common: dict[str, Any] = {
"model": args.model,
"trust_remote_code": True,
"num_gpus": 2,
"num_gpus": args.dp_size if is_dlo else args.tp_size,
"usp": 1,
"ring": 1,
"vae_parallel_mode": "tile",
@@ -74,22 +105,22 @@ def engine_kwargs(args: argparse.Namespace) -> dict[str, Any]:
"stage_init_timeout": args.init_timeout,
"init_timeout": args.init_timeout,
}
if args.mode == "dlo-dp2":
if is_dlo:
common.update(
tensor_parallel_size=1,
data_parallel_size=2,
data_parallel_size=args.dp_size,
text_encoder_tp_size=1,
vae_patch_parallel_size=1,
enable_distributed_layerwise_offload=True,
dlo_use_allgather=True,
dlo_use_allgather=args.mode == "dlo",
dlo_resident_layers=0,
)
else:
common.update(
tensor_parallel_size=2,
tensor_parallel_size=args.tp_size,
data_parallel_size=1,
text_encoder_tp_size=2,
vae_patch_parallel_size=2,
text_encoder_tp_size=args.tp_size,
vae_patch_parallel_size=args.tp_size,
enable_distributed_layerwise_offload=False,
)
return common
@@ -172,7 +203,8 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
"engine_kwargs": engine_kwargs(args),
}
try:
if args.mode == "dlo-dp2":
uses_dp_wave = args.mode == "dlo" and args.dp_size > 1
if uses_dp_wave:
started = time.perf_counter()
asymmetric = await asyncio.gather(
generate_one(
@@ -182,12 +214,15 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
prompt="",
seed=1001,
),
generate_one(
engine,
args,
request_id="invalid-peer",
prompt=DEFAULT_PROMPTS[0],
seed=1002,
*(
generate_one(
engine,
args,
request_id=f"invalid-peer-{index}",
prompt=DEFAULT_PROMPTS[index % len(DEFAULT_PROMPTS)],
seed=1001 + index,
)
for index in range(1, args.dp_size)
),
return_exceptions=True,
)
@@ -195,18 +230,18 @@ async def run(args: argparse.Namespace) -> dict[str, Any]:
summary["asymmetric_errors"] = [
f"{type(result).__name__}: {result}" for result in asymmetric if isinstance(result, BaseException)
]
if len(summary["asymmetric_errors"]) != 2:
raise RuntimeError("Both requests in the asymmetric DP wave must fail before dispatch")
if len(summary["asymmetric_errors"]) != args.dp_size:
raise RuntimeError("Every request in the asymmetric DP wave must fail before dispatch")
started = time.perf_counter()
request_count = 2 if args.mode == "dlo-dp2" else 1
request_count = args.dp_size if uses_dp_wave else 1
outputs = await asyncio.gather(
*(
generate_one(
engine,
args,
request_id=f"recovery-{index}",
prompt=DEFAULT_PROMPTS[index],
prompt=DEFAULT_PROMPTS[index % len(DEFAULT_PROMPTS)],
seed=2000 + index,
)
for index in range(request_count)