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Purge VerlEngine (#7326)

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Signed-off-by: Ata Fatahi <immrata@gmail.com>
This commit is contained in:
Ata Fatahi
2025-06-19 23:47:21 -07:00
committed by GitHub
parent 4df5fc2156
commit 1ab6be1b26
5 changed files with 0 additions and 680 deletions
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3
python/sglang/srt/entrypoints/http_server_engine.py
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@@ -64,11 +64,9 @@ class HttpServerEngineAdapter(EngineBase):
def _make_request(self, endpoint: str, payload: Optional[dict] = None):
"""Make a POST request to the specified endpoint with the given payload.
Args:
endpoint: The API endpoint to call
payload: The JSON payload to send (default: empty dict)
Returns:
The JSON response from the server
"""
@@ -85,7 +83,6 @@ class HttpServerEngineAdapter(EngineBase):
):
"""
Update model weights from tensor data. The HTTP server will only post meta data, and the real weights will be copied directly from GPUs.
Note: The model should be on GPUs rather than CPU for this functionality to work properly.
If you encounter issues, ensure your model is loaded on GPU devices rather than CPU.
"""

179
python/sglang/srt/entrypoints/verl_engine.py
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@@ -1,179 +0,0 @@
# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import os
from typing import Dict, Iterable, List, Literal, Optional, Tuple, Union
import torch
import torch.distributed as dist
from PIL.Image import Image
from torch.distributed.tensor import DeviceMesh, DTensor
from sglang.srt.entrypoints.engine import Engine
from sglang.srt.entrypoints.http_server_engine import HttpServerEngineAdapter
from sglang.srt.model_executor.model_runner import LocalSerializedTensor
from sglang.srt.patch_torch import monkey_patch_torch_reductions
from sglang.srt.utils import MultiprocessingSerializer, broadcast_pyobj
class VerlEngine:
def __init__(
self,
device_mesh_cpu: DeviceMesh,
nnodes: int = 1,
backend: Literal["engine", "server"] = "engine",
**kwargs,
):
monkey_patch_torch_reductions()
self._device_mesh_cpu = device_mesh_cpu
self._tp_rank = device_mesh_cpu.get_local_rank()
self._rank = device_mesh_cpu.get_rank()
self._tp_size = device_mesh_cpu.size()
tp_size_per_node = self._tp_size // nnodes
node_rank = self._tp_rank // tp_size_per_node
first_rank_in_node = self._tp_rank % tp_size_per_node == 0
# Common engine keyword arguments
engine_kwargs = dict(
**kwargs, tp_size=self._tp_size, node_rank=node_rank, nnodes=nnodes
)
if backend == "engine":
if first_rank_in_node:
os.environ["SGLANG_BLOCK_NONZERO_RANK_CHILDREN"] = "0"
self._engine = Engine(**engine_kwargs)
else:
self._engine = None
elif backend == "server":
if self._tp_rank == 0:
self._engine = HttpServerEngineAdapter(**engine_kwargs)
else:
self._engine = None
else:
raise ValueError(f"Unsupported backend: {backend}")
dist.barrier(group=self._device_mesh_cpu.get_group())
def generate(
self,
# The input prompt. It can be a single prompt or a batch of prompts.
prompt: Optional[Union[List[str], str]] = None,
sampling_params: Optional[Union[List[Dict], Dict]] = None,
# The token ids for text; one can either specify text or input_ids.
input_ids: Optional[Union[List[List[int]], List[int]]] = None,
# The image input. It can be an image instance, file name, URL, or base64 encoded string.
# Can be formatted as:
# - Single image for a single request
# - List of images (one per request in a batch)
# - List of lists of images (multiple images per request)
# See also python/sglang/srt/utils.py:load_image for more details.
image_data: Optional[
Union[
List[List[Union[Image, str]]],
List[Union[Image, str]],
Union[Image, str],
]
] = None,
return_logprob: Optional[Union[List[bool], bool]] = False,
logprob_start_len: Optional[Union[List[int], int]] = None,
top_logprobs_num: Optional[Union[List[int], int]] = None,
token_ids_logprob: Optional[Union[List[List[int]], List[int]]] = None,
lora_path: Optional[List[Optional[str]]] = None,
custom_logit_processor: Optional[Union[List[str], str]] = None,
) -> Dict:
"""
The arguments of this function is the same as `sglang/srt/managers/io_struct.py::GenerateReqInput`.
Please refer to `GenerateReqInput` for the documentation.
"""
if self._tp_rank == 0:
output = self._engine.generate(
prompt=prompt,
sampling_params=sampling_params,
input_ids=input_ids,
image_data=image_data,
return_logprob=return_logprob,
logprob_start_len=logprob_start_len,
top_logprobs_num=top_logprobs_num,
token_ids_logprob=token_ids_logprob,
lora_path=lora_path,
custom_logit_processor=custom_logit_processor,
)
else:
output = None
# Most naive implementation, can extract tensor and send via gloo if too slow
[output] = broadcast_pyobj(
data=[output],
rank=self._rank,
dist_group=self._device_mesh_cpu.get_group(),
src=self._device_mesh_cpu.mesh[0].item(),
force_cpu_device=False,
)
return output
def update_weights_from_tensor(
self,
named_tensors: Iterable[Tuple[str, torch.Tensor]],
load_format: Optional[str] = None,
):
# Most naive implementation, can optimize a lot if it is bottleneck
for tensor_index, (name, tensor) in enumerate(named_tensors):
serialized_tensor = MultiprocessingSerializer.serialize(
_preprocess_tensor_for_update_weights(tensor)
)
if self._tp_rank == 0:
gathered_serialized_tensors = [None for _ in range(self._tp_size)]
else:
gathered_serialized_tensors = None
dist.gather_object(
obj=serialized_tensor,
object_gather_list=gathered_serialized_tensors,
dst=self._device_mesh_cpu.mesh.tolist()[0],
group=self._device_mesh_cpu.get_group(),
)
if self._tp_rank == 0:
self._engine.update_weights_from_tensor(
named_tensors=[
(
name,
LocalSerializedTensor(values=gathered_serialized_tensors),
)
],
load_format=load_format,
flush_cache=False,
)
if self._tp_rank == 0:
self._engine.flush_cache()
def release_memory_occupation(self):
if self._tp_rank == 0:
self._engine.release_memory_occupation()
def resume_memory_occupation(self):
if self._tp_rank == 0:
self._engine.resume_memory_occupation()
def shutdown(self):
if self._engine is not None:
self._engine.shutdown()
def _preprocess_tensor_for_update_weights(tensor: torch.Tensor):
if isinstance(tensor, DTensor):
return tensor.full_tensor()
return tensor