[Feature] Support loading weights from ckpt engine connector by stmatengss · Pull Request #10667 · sgl-project/sglang

stmatengss · 2025-09-19T17:17:42Z

Motivation

Motivated by #8215, we aim to integrate ckpt-engineinto SGLang to accelerate model loading and weight synchronization.

A proposal is in #10464, and this PR can support both co-locate/disaggregation deployment and TP.

Usage:

pip install 'checkpoint-engine[p2p]'  # install checkpoint engine

~~Fake sglang server (only occupying model weights).~~

python3 -m sglang.launch_server --model [model-name] --tp 2 --page-size 1 --mem-fraction-static 0 --enable-ckpt-engine

~~New sglang instance~~

python3 -m sglang.launch_server --model [model-name]  --tp 2 --mem-fraction-static 0.4 --load-format ckpt_engine --port [port_num]

Running Methods:

sglang

python3 -m sglang.launch_server --model /opt/models/Qwen/Qwen3-8b --tp 8 --load-format ckpt_engine --port 30001

checkpoint engine

torchrun --nproc-per-node 8 ckptengine_update.py --update-method all --checkpoint-path /opt/models/Qwen/Qwen3-8b/

Co-author: @XucSh @zxpdemonio @BraveY
Thanks to @weixiao-huang for help.

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

Format your code according to the Format code with pre-commit.
Add unit tests according to the Run and add unit tests.
Update documentation according to Write documentations.
Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.

gemini-code-assist · 2025-09-19T17:18:08Z

Summary of Changes

Hello @stmatengss, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request integrates the checkpoint-engine library into SGLang, providing a new mechanism for loading and updating large language model weights. The primary goal is to enhance efficiency in model loading and weight synchronization across distributed environments, supporting both co-located/disaggregated deployments and Tensor Parallelism. This involves adding a new load format, a dedicated connector, and an API endpoint for dynamic weight updates, streamlining the management of model parameters.

Highlights

Checkpoint Engine Integration: Introduced support for loading model weights using the checkpoint-engine library, enabling accelerated model loading and weight synchronization, particularly beneficial for co-located/disaggregated deployments and Tensor Parallelism (TP).
New Load Format: Added a new ckpt_engine load format option, allowing users to specify this method for loading models.
API Endpoint for Weight Updates: Implemented a new FastAPI endpoint /update_weights_from_ckpt_engine to facilitate dynamic, in-place updates of model weights via the checkpoint engine without requiring a server restart.
Custom Checkpoint Engine Connector: Developed a dedicated CkptEngineConnector to manage the communication and weight extraction process from the checkpoint engine, including handling ZMQ communication and IPC buffers.
Parameter Server Initialization: Integrated the ParameterServer from checkpoint-engine into the model runner, allowing it to register checkpoints, gather metadata, and update weights using either broadcast or P2P methods.

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Code Review

This pull request introduces support for loading model weights from ckpt-engine to accelerate model loading and weight synchronization. The changes are extensive, adding new components for checkpoint engine interaction and modifying existing parts of the model loading and execution pipeline, including configurations, connectors, schedulers, and model runners.

My review focuses on ensuring the correctness, robustness, and maintainability of these new features. I have identified several critical issues that could lead to runtime errors or incorrect behavior, such as improper handling of environment variables, NameError exceptions due to undefined variables in error-handling paths, and potential data loss in the weight iteration logic. Additionally, I've provided suggestions to improve code clarity and security by addressing hardcoded values, removing dead code, and recommending safer serialization alternatives to pickle.

gemini-code-assist · 2025-09-19T17:20:33Z

python/sglang/srt/connector/ckpt_engine.py

+        for key, tensor in self.final_state_dict.items():
+            yield key, tensor


After the main while loop in weight_iterator finishes, any weights remaining in self.pending_weights (e.g., a gate_proj weight without a corresponding up_proj weight in the processed payloads) are not yielded. This will result in missing weights and likely cause model loading to fail. You should process any remaining items in self.pending_weights after the loop.

Suggested change

for key, tensor in self.final_state_dict.items():

yield key, tensor

for key, tensor in self.final_state_dict.items():

yield key, tensor

for key, tensor in self.pending_weights.items():

yield key, tensor

gemini-code-assist · 2025-09-19T17:20:34Z

python/sglang/srt/model_executor/model_runner.py

+            return iter
+
+        def model_load_weights(model, iter):
+            DefaultModelLoader.load_weights_and_postprocess(model, iter, target_device)


The variable target_device is not defined within the scope of update_weights_from_ckpt_engine. This will cause a NameError when model_load_weights is called. It should probably be device_config.device, which is available in the outer scope.

Suggested change

DefaultModelLoader.load_weights_and_postprocess(model, iter, target_device)

DefaultModelLoader.load_weights_and_postprocess(model, iter, device_config.device)

gemini-code-assist · 2025-09-19T17:20:34Z

python/sglang/srt/model_executor/model_runner.py

+                message = (
+                    f"Failed to update weights: {e}.\nRolling back to original weights."
+                )
+                del iter


The variable iter is not defined in this scope before being deleted, which will cause a NameError. This line should be removed.

gemini-code-assist · 2025-09-19T17:20:34Z

python/sglang/ckptengine_update.py

+    rank = int(os.getenv("RANK"))
+    world_size = int(os.getenv("WORLD_SIZE"))


The script will crash with a TypeError if the RANK or WORLD_SIZE environment variables are not set, because os.getenv will return None and int(None) is invalid. A similar issue exists on line 151. To make the script more robust, you should handle the case where these environment variables might not be set, for example by using os.environ which raises a KeyError if the variable is not found, providing a more explicit error.

Suggested change

rank = int(os.getenv("RANK"))

world_size = int(os.getenv("WORLD_SIZE"))

rank = int(os.environ["RANK"])

world_size = int(os.environ["WORLD_SIZE"])

gemini-code-assist · 2025-09-19T17:20:34Z

python/sglang/srt/connector/ckpt_engine.py

+    def get_zmq_handle(self, tp_rank: int):
+        # FIXME: There needs a local rank


The FIXME comment indicates that tp_rank might not be the correct rank to use for getting the physical GPU ID, especially in a multi-node environment. tp_rank is a global rank, but _get_physical_gpu_id seems to expect a local rank on the node. Using the wrong rank could lead to incorrect GPU selection and failures. This should be resolved to ensure correctness in distributed setups. A similar issue is present on line 98.

gemini-code-assist · 2025-09-19T17:20:35Z

python/sglang/srt/managers/tp_worker.py

@@ -339,6 +341,12 @@ def update_weights_from_tensor(self, recv_req: UpdateWeightsFromTensorReqInput):
        )
        return success, message

+    def update_weights_from_ckpt_engine(self, recv_req: UpdateWeightFromCkptEngineReqInput):


There is a typo in the type hint for recv_req. It should be UpdateWeightsFromCkptEngineReqInput to match the imported class name.

Suggested change

def update_weights_from_ckpt_engine(self, recv_req: UpdateWeightFromCkptEngineReqInput):

def update_weights_from_ckpt_engine(self, recv_req: UpdateWeightsFromCkptEngineReqInput):

gemini-code-assist · 2025-09-19T17:20:35Z

python/sglang/srt/model_executor/model_runner.py

+            with open(self.server_args.ckpt_save_meta_file_name, "wb") as f:
+                pickle.dump(self.ps.get_metas(), f)


Using pickle for serialization can introduce security vulnerabilities, as unpickling data from an untrusted source can lead to arbitrary code execution. While you are writing the file here, it will be read elsewhere. As noted in the TODO on line 299, using a safer serialization format like JSON is recommended to avoid potential security risks.

gemini-code-assist · 2025-09-19T17:20:35Z

python/sglang/srt/model_executor/update.py

+    logger.info(f"{msg} duration: {end - start:.2f} seconds")
+
+
+def check_vllm_ready(endpoint: str, inference_parallel_size: int):


The function check_vllm_ready uses a global variable rank which is defined at the bottom of the script (line 146). This is not a good practice as it makes the code harder to understand and maintain. The rank should be passed as an argument to the function.

Suggested change

def check_vllm_ready(endpoint: str, inference_parallel_size: int):

def check_vllm_ready(endpoint: str, inference_parallel_size: int, rank: int):

gemini-code-assist · 2025-09-19T17:20:35Z

python/sglang/srt/model_loader/loader.py

+    def load_model_from_ckpt_engine(
+        self, model, client, model_config: ModelConfig, device_config: DeviceConfig
+    ) -> nn.Module:
+        socket = client.get_socket_handle(device_config.gpu_id)


The socket variable is assigned but never used. This appears to be dead code and should be removed to improve clarity.

gemini-code-assist · 2025-09-19T17:20:35Z

python/sglang/srt/model_loader/loader.py

+            # FIXME: use more elegant method
+            if key == "model.embed_tokens.weight":
+                key = "lm_head.weight"


Hardcoding the remapping of model.embed_tokens.weight to lm_head.weight is brittle and may not work for all models. As the FIXME suggests, a more elegant and configurable method for handling weight name discrepancies should be implemented. This could involve a mapping file or a more general remapping logic.

weixiao-huang · 2025-09-20T03:26:47Z

python/sglang/srt/connector/ckpt_engine.py

+        return weights
+
+    # Implemented as a no-op to make BaseConnector interface consistent.
+    def weight_iterator(


Could we reuse the method checkpoint_engine.worker.update_weights_from_ipc to make code more simple like below

def _process_gate_up_proj(self, named_tensors: list[Tuple[str, torch.Tensor]]): for name, tensor in named_tensors: if "mlp.gate_proj.weight" in name: up_key = name.replace("gate_proj", "up_proj") if up_key in self.pending_weights: up_tensor = self.pending_weights.pop(up_key) self._merge_and_store(name, tensor, up_key, up_tensor) else: self.pending_weights[name] = tensor elif "mlp.up_proj.weight" in name: gate_key = name.replace("up_proj", "gate_proj") if gate_key in self.pending_weights: gate_tensor = self.pending_weights.pop(gate_key) self._merge_and_store(gate_key, gate_tensor, name, tensor) else: self.pending_weights[name] = tensor else: yield name, tensor for key, tensor in self.final_state_dict.items(): yield key, tensor def weight_iterator(self, rank: int = 0) -> Generator[Tuple[str, torch.Tensor], None, None]: from checkpoint_engine.worker import update_weights_from_ipc if self.socket is None: self.get_socket_handle(rank) update_weights_from_ipc( self.zmq_ctx, self.zmq_handle, rank, run=self._process_gate_up_proj, )

That's an excellent suggestion. I will address this in the next commit.

weixiao-huang · 2025-09-20T03:30:47Z

python/sglang/srt/connector/ckpt_engine.py

+                    up_tensor = self.pending_weights.pop(up_key)
+                    self._merge_and_store(item["name"], tensor, up_key, up_tensor)
+                else:
+                    self.pending_weights[item["name"]] = tensor


weights are received by using a fixed bucket size. So all weights may be split to multiple turn to be received. This tensor data from buffer may be changed in the next update turn. A workaround method is to tensor.clone() but will occupy more GPU memory. Is it necessary for this tensor to be saved in pending_weights?

In practice, this pending buffer is unused. The associated code will be removed in a subsequent commit.

weixiao-huang · 2025-09-20T03:36:31Z

python/sglang/srt/connector/ckpt_engine.py

+        self, rank: int = 0
+    ) -> Generator[Tuple[str, torch.Tensor], None, None]:
+        if self.socket is None:
+            self.get_socket_handle(rank)


self.zmq_handle will be changed in each ps.update method in the future's checkpoint-engine==0.1.2 version. see https://github.com/MoonshotAI/checkpoint-engine/blob/03ff7e7268d614b5c5d3af7388e541fc181bd892/checkpoint_engine/ps.py#L812-L818 since self._zmq_addr_counter += 1 will trigger for each ps.update. So self.zmq_handle should be updated by using self.get_zmq_handle() in each request

Thanks, that's a helpful reminder. We are refactoring the static port to be dynamically negotiated, and we'll be sure to include this point.

weixiao-huang · 2025-09-20T03:41:48Z

python/sglang/srt/managers/scheduler.py

@@ -324,6 +325,13 @@ def __init__(
            self.enable_overlap = False
            logger.info("Overlap scheduler is disabled for embedding models.")

+        # TODO: May change it to somewhere
+        os.environ["RANK"] = str(self.tp_rank)


Maybe I'll add rank and world_size args in ParameterServer, see https://github.com/MoonshotAI/checkpoint-engine/pull/20/files

Got it. We're planning to make this a startup argument for SGLang and are working on it.

XucSh · 2025-09-20T03:52:09Z

Tested on Qwen3-0.6B (TP1 & TP2) and Qwen3-8B（TP4） models. New inference instances function correctly after the weight update.

Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

Signed-off-by: CruzZhao <CruzZhao@linux.alibaba.com>

Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

python/sglang/srt/entrypoints/http_server.py

JustinTong0323 · 2025-09-28T22:31:09Z

python/sglang/ckptengine_update.py

This is not a good place to put this file

Is it ok to move it to /scripts?

This scripts seems to be the same as https://github.com/MoonshotAI/checkpoint-engine/blob/main/examples/update.py. Could we write a code to get this python file and exec it? Just like

wget https://raw.githubusercontent.com/MoonshotAI/checkpoint-engine/refs/heads/main/examples/update.py python3 update.py --help

This scripts seems to be the same as https://github.com/MoonshotAI/checkpoint-engine/blob/main/examples/update.py. Could we write a code to get this python file and exec it? Just like

wget https://raw.githubusercontent.com/MoonshotAI/checkpoint-engine/refs/heads/main/examples/update.py python3 update.py --help

In fact, We need to modify the logic of this script because executing it directly allocates most of the GPU memory to the communication buffer, causing Sglang to run out of memory and fail to start. Therefore, the original script logic cannot be used directly.

Got it. will fix

This scripts seems to be the same as https://github.com/MoonshotAI/checkpoint-engine/blob/main/examples/update.py. Could we write a code to get this python file and exec it? Just like

wget https://raw.githubusercontent.com/MoonshotAI/checkpoint-engine/refs/heads/main/examples/update.py python3 update.py --help

We can temporarily maintain this file within sglang, then merge it into the main checkpoint engine repository for easier maintenance.

Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>

Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

stmatengss · 2025-10-19T03:06:21Z

Will reimplement the checkpoint engine connector after merging PR #11755.

ByronHsu

High level questions:

Checkpoint engine needs to load from disk to cpu, and use the pipeline to send to inference engines. disk-to-cpu is still on the critical path. What is the time we save here?
Can you provide examples of how to use in online serving cases? For example, I add 10 new engines, how can i use checkpoint engine to make them load faster.
Can you provide examples of how to use in RL case? How to do efficient broadcast to all inference engines.

Happy to chat online. You can find me at ByronHsu in sglang slack.

XucSh · 2025-10-20T05:10:05Z

High level questions:

Checkpoint engine needs to load from disk to cpu, and use the pipeline to send to inference engines. disk-to-cpu is still on the critical path. What is the time we save here?

Can you provide examples of how to use in online serving cases? For example, I add 10 new engines, how can i use checkpoint engine to make them load faster.

Can you provide examples of how to use in RL case? How to do efficient broadcast to all inference engines.

Happy to chat online. You can find me at ByronHsu in sglang slack.

The checkpoint service is a persistent process that holds weights in memory (GPU/CPU). Each new instance (specifically, each TP rank) has its own ParameterService（checkpoint engine） from which it fetches weights at startup.

stmatengss requested review from CatherineSue, Ying1123, hnyls2002, ispobock, merrymercy, slin1237, xiezhq-hermann and zhyncs as code owners September 19, 2025 17:17

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stmatengss mentioned this pull request Sep 19, 2025

Integrate with sglang MoonshotAI/checkpoint-engine#17

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weixiao-huang reviewed Sep 20, 2025

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stmatengss and others added 16 commits September 20, 2025 12:46

[Model Executor] feat: init a new mode loader from ckpt engine

1e7ae8e

[RL] Load weights from Checkpoint Engine

ca8b8db

Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

fix

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

fix

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

support for tp

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

online update from checkpoint engine

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Signed-off-by: CruzZhao <CruzZhao@linux.alibaba.com>

fix typo about UpdateWeightsFromCkptEngineReqInput

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Signed-off-by: CruzZhao <CruzZhao@linux.alibaba.com>

Fix lint

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Update python/sglang/srt/entrypoints/http_server.py

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Co-authored-by: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com>

Merge branch 'main' into mateng/dev_ckpt_engine

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Merge branch 'main' into mateng/dev_ckpt_engine

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Merge branch 'main' into mateng/dev_ckpt_engine

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add ckptengine port argument

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update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

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Merge branch 'main' into mateng/dev_ckpt_engine

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update

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Signed-off-by: Xuchun Shang <xuchun.shang@linux.alibaba.com>

update

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Merge branch 'main' into mateng/dev_ckpt_engine

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Merge branch 'main' into mateng/dev_ckpt_engine

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Merge branch 'main' into mateng/dev_ckpt_engine

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stmatengss mentioned this pull request Oct 17, 2025

[Feature] Support loading weights from ckpt engine worker #11755

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		for key, tensor in self.final_state_dict.items():
		yield key, tensor

	DefaultModelLoader.load_weights_and_postprocess(model, iter, target_device)
	DefaultModelLoader.load_weights_and_postprocess(model, iter, device_config.device)

		rank = int(os.getenv("RANK"))
		world_size = int(os.getenv("WORLD_SIZE"))

		def get_zmq_handle(self, tp_rank: int):
		# FIXME: There needs a local rank

	def update_weights_from_ckpt_engine(self, recv_req: UpdateWeightFromCkptEngineReqInput):
	def update_weights_from_ckpt_engine(self, recv_req: UpdateWeightsFromCkptEngineReqInput):

		with open(self.server_args.ckpt_save_meta_file_name, "wb") as f:
		pickle.dump(self.ps.get_metas(), f)

		logger.info(f"{msg} duration: {end - start:.2f} seconds")


		def check_vllm_ready(endpoint: str, inference_parallel_size: int):

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XucSh commented Sep 20, 2025

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stmatengss commented Oct 19, 2025

stmatengss commented Sep 19, 2025 •

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