[Feature][1/2] Impl the connector based on the llmdatadist for v1 #684

jianzs · 2025-04-27T13:26:26Z

This PR implements the connector functionality for NPU based on LLMDataDist, building upon the connector API merged in vLLM v1. (vllm-project/vllm#15960) We've successfully tested various scenarios in offline environments:

Single-machine: Verified 2P2D testing with dense models (Llama) and MoE models (DeepSeek v2 Lite)
Two-machine: Completed 1P1D testing with DeepSeek R1 W8A8

Key implementation aspects include:

Cross-machine PD: LLMDataDist requires NPU device IP for connection establishment. Our approach utilizes a global rank table (JSON) on each machine containing:

Unique server IDs
IP addresses and device IDs for each card
Server ID specification in connector extra config at startup for instance information retrieval

nPmD: Given that the community's nPmD design, particularly the router component API, is still evolving, we've implemented a solution using a meta server component (to be provided separately) that:

Records prefill completion details (device and dp rank information)
Responds to decode node queries with prefill node locations
Enables decode nodes to retrieve data from appropriate prefill nodes

We propose initially merging the 1P1D implementation, where the global rank table contains information for two nodes, allowing direct prefill node identification. The nPmD implementation can be refined and merged following community discussion.

Todo:

Implement 1P1D (one prefill, one decode) configuration support
Add sample script for automatic global rank table generation
Document global rank table format specifications
Provide user guide for PD (Prefill-Decode) functionality
Add test cases

re #448

Note:
A minor modification to vLLM's codebase is required to run this example successfully. The patch enables the scheduler process to locate the appropriate connector class by importing the necessary module.

The change should be made in vllm/v1/core/sched/scheduler.py, adding an import statement for vllm_ascend.distributed.

This is a temporary solution, and we need to implement a more elegant module discovery mechanism.

diff --git a/vllm/v1/core/sched/scheduler.py b/vllm/v1/core/sched/scheduler.py
index 69e7cc8ee..b15525971 100644
--- a/vllm/v1/core/sched/scheduler.py
+++ b/vllm/v1/core/sched/scheduler.py
@@ -31,6 +31,8 @@ from vllm.v1.structured_output import StructuredOutputManager
 
 logger = init_logger(__name__)
 
+# TODO(jianzs): Find the suitable place to put this.
+import vllm_ascend.distributed
 
 class Scheduler(SchedulerInterface):

Limits:

We use a hash function (string_to_int64_hash) to convert request IDs to datadist request IDs. This conversion is lossy, potentially creating duplicate IDs, leading to duplicate CacheKeys and allocate_cache failures.

jianzs · 2025-05-06T03:01:32Z

Measure the time it takes for KV transfers at different sequence lengths.

Environment:

Two machines
1P1D
DeepSeek R1 W8A8
TP=16，DP=1

Stacked charts show higher times than overall charts because each stage of measurement has NPU synchronization before and after, especially extract kv, scatter update, and inject kv, all of which synchronize on each layer, resulting in significant host overhead.

load kv = pull cache + inject kv
save kv = extract kv + scatter update

Kevin-XiongC · 2025-05-08T03:48:46Z

Hi, I tried this PR. But it seems like there exists a precision issue here. The prompt is "What is the largest animal in the world?" with temperature == 0 using qwen2.5 0.5b.

using PD disaggregated

{"id":"cmpl-request2005","object":"text_completion","created":1746675979,"model":"/deepseek/Qwen2.5-0.5B-Instruct","choices":[{"index":0,"text":" T of the\nTheorem's Conclusion.\nIn this case, we have to find the value that will make the equation true. We can do so by substituting the given values into the original equation","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":10,"total_tokens":50,"completion_tokens":40,"prompt_tokens_details":null}}

normal

{"id":"cmpl-request16718","object":"text_completion","created":1746676100,"model":"/deepseek/Qwen2.5-0.5B-Instruct","choices":[{"index":0,"text":" The largest animal in the world, as of 2021, was the blue whale. Blue whales can grow up to weigh over 150 tons and be up to 30","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":9,"total_tokens":49,"completion_tokens":40,"prompt_tokens_details":null}}

jianzs · 2025-05-08T07:48:51Z

Hi, I tried this PR. But it seems like there exists a precision issue here. The prompt is "What is the largest animal in the world?" with temperature == 0 using qwen2.5 0.5b.

using PD disaggregated

{"id":"cmpl-request2005","object":"text_completion","created":1746675979,"model":"/deepseek/Qwen2.5-0.5B-Instruct","choices":[{"index":0,"text":" T of the\nTheorem's Conclusion.\nIn this case, we have to find the value that will make the equation true. We can do so by substituting the given values into the original equation","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":10,"total_tokens":50,"completion_tokens":40,"prompt_tokens_details":null}}

normal

{"id":"cmpl-request16718","object":"text_completion","created":1746676100,"model":"/deepseek/Qwen2.5-0.5B-Instruct","choices":[{"index":0,"text":" The largest animal in the world, as of 2021, was the blue whale. Blue whales can grow up to weigh over 150 tons and be up to 30","logprobs":null,"finish_reason":"length","stop_reason":null,"prompt_logprobs":null}],"usage":{"prompt_tokens":9,"total_tokens":49,"completion_tokens":40,"prompt_tokens_details":null}}

Thank you for reporting this issue. I've tested with DeepSeek v2 Lite and Llama2 7B, and observed that:

The first response is incorrect
Second and subsequent responses are as expected

Could you confirm if you're seeing incorrect responses consistently in your tests? And are your configurations, including parallelism, identical in both the disaggregated and singlestone environments?

Kevin-XiongC · 2025-05-08T08:29:51Z

Yes, I see this consistently.

I used the same sh script 'disaggregated_prefill_multi_prefill.sh' but changed its tp to 1 and model to qwen2.5 0.5b.

and as the aggregated one. I've used the default settings below.

 python -m vllm.entrypoints.openai.api_server --model Qwen2.5-0.5B-Instruct

as for the ranktable

{
    "server_group_list":[
        {
            "group_id": "0",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "1",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-0",
                    "device": [
                        {
                            "device_id": "0",
                            "device_ip": "172.22.17.1",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "2",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-1",
                    "device": [
                        {
                            "device_id": "4",
                            "device_ip": "172.22.17.5",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        }
    ]
}

jianzs · 2025-05-08T09:37:51Z

Yes, I see this consistently.

I used the same sh script 'disaggregated_prefill_multi_prefill.sh' but changed its tp to 1 and model to qwen2.5 0.5b.

and as the aggregated one. I've used the default settings below.

 python -m vllm.entrypoints.openai.api_server --model Qwen2.5-0.5B-Instruct

as for the ranktable

{
    "server_group_list":[
        {
            "group_id": "0",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "1",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-0",
                    "device": [
                        {
                            "device_id": "0",
                            "device_ip": "172.22.17.1",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "2",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-1",
                    "device": [
                        {
                            "device_id": "4",
                            "device_ip": "172.22.17.5",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        }
    ]
}

I fixed an accuracy issue. Please try again.

Kevin-XiongC · 2025-05-08T10:00:59Z

Yes, I see this consistently.
I used the same sh script 'disaggregated_prefill_multi_prefill.sh' but changed its tp to 1 and model to qwen2.5 0.5b.
and as the aggregated one. I've used the default settings below.

 python -m vllm.entrypoints.openai.api_server --model Qwen2.5-0.5B-Instruct

as for the ranktable

{
    "server_group_list":[
        {
            "group_id": "0",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "1",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-0",
                    "device": [
                        {
                            "device_id": "0",
                            "device_ip": "172.22.17.1",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "2",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-1",
                    "device": [
                        {
                            "device_id": "4",
                            "device_ip": "172.22.17.5",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        }
    ]
}

I fixed an accuracy issue. Please try again.

Hi, thx for your work. Unfortunately, it still produces inconsistent results with the 0.5B model, but when I switch to 1.5B model, the disaggregated version produces the correct one. I hope it helps

jianzs · 2025-05-09T02:56:48Z

Yes, I see this consistently.
I used the same sh script 'disaggregated_prefill_multi_prefill.sh' but changed its tp to 1 and model to qwen2.5 0.5b.
and as the aggregated one. I've used the default settings below.

 python -m vllm.entrypoints.openai.api_server --model Qwen2.5-0.5B-Instruct

as for the ranktable

{
    "server_group_list":[
        {
            "group_id": "0",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "1",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-0",
                    "device": [
                        {
                            "device_id": "0",
                            "device_ip": "172.22.17.1",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "2",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-1",
                    "device": [
                        {
                            "device_id": "4",
                            "device_ip": "172.22.17.5",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        }
    ]
}

I fixed an accuracy issue. Please try again.

Hi, thx for your work. Unfortunately, it still produces inconsistent results with the 0.5B model, but when I switch to 1.5B model, the disaggregated version produces the correct one. I hope it helps

Thx. Fix a bug, please try again...

Kevin-XiongC · 2025-05-09T06:30:55Z

Yes, I see this consistently.
I used the same sh script 'disaggregated_prefill_multi_prefill.sh' but changed its tp to 1 and model to qwen2.5 0.5b.
and as the aggregated one. I've used the default settings below.

 python -m vllm.entrypoints.openai.api_server --model Qwen2.5-0.5B-Instruct

as for the ranktable

{
    "server_group_list":[
        {
            "group_id": "0",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "1",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-0",
                    "device": [
                        {
                            "device_id": "0",
                            "device_ip": "172.22.17.1",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        },
        {
            "group_id": "2",
            "version": "1.0",
            "server_count": "1",
            "server_list": [
                {
                    "server_ip": "10.172.116.166",
                    "server_id": "server-1",
                    "device": [
                        {
                            "device_id": "4",
                            "device_ip": "172.22.17.5",
                            "rank_id": "0"
                        }
                    ],
                    "container_ip": "10.172.116.166"
                }
            ],
            
            "status": "completed"
        }
    ]
}

I fixed an accuracy issue. Please try again.

Hi, thx for your work. Unfortunately, it still produces inconsistent results with the 0.5B model, but when I switch to 1.5B model, the disaggregated version produces the correct one. I hope it helps

Thx. Fix a bug, please try again...

Great! It works for me now.

ganyi1996ppo · 2025-05-11T02:48:27Z

vllm_ascend/distributed/llmdatadist_connector_v1.py

+                self.num_layers, kv_cache_shape, kv_hidden_dtype)
+            self._attach_kv_buffer(kv_buffer)
+
+            target_tp_rank = self.tp_rank % min(


Why mod the min size of the tp size of pd? Can't it just adopt the tp rank.

This design originally aimed to support heterogeneous parallelism between prefill and decode phases. For scenarios where prefill TP size < decode TP size, each rank could determine its connection count using the modulo method.

However, due to current LLMDataDist constraints, decode TP size must be ≤ prefill TP size. Consequently, using either modulo operation or direct TP rank assignment achieves identical results.

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

Ensure correct input for npu_reshape_and_cache function The 'slot_indices' parameter of npu_reshape_and_cache must be: - A torch.int32 tensor - Located on the NPU device Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

Eliminates the need to launch the meta server in the 1p1d environment. Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

ganyi1996ppo · 2025-05-11T02:57:07Z

examples/disaggregated-prefill-v1/disaggregated_prefill_multi_prefill.sh

+        "kv_buffer_device": "npu",
+        "kv_role": "kv_producer",
+        "kv_rank": 0,
+        "kv_parallel_size": 2,


What dose this kv_parallel_size do?

The v0 implementation needed this, but I'm unsure if it's still necessary.

ganyi1996ppo · 2025-05-11T03:00:41Z

The code looks good to me in general, but I'm not very familiar with the llmdatadist. Can @whx-sjtu review this PR for some of its detail?

whx-sjtu · 2025-05-11T15:35:21Z

vllm_ascend/distributed/llmdatadist_connector_v1.py

+    device_ip: str
+    dp_rank: int
+    tp_rank: int
+    cluster_id: int


You may need to add a new member super_device_id if you want to run disaggregated-prefill on A3 super node.

mjp9527 · 2025-05-12T04:01:14Z

vllm_ascend/distributed/llmdatadist_connector_v1.py

+            datadist_request_id = string_to_int64_hash(request.request_id)
+            kv_cache_key = llm_datadist.CacheKey(remote_cluster_id,
+                                                 datadist_request_id, 1)
+            self.llm_datadist_engine.kv_transfer.pull_cache(


Good job! I 'm confused about how to implement prefix caching. If enable prefix caching, using decode instance to run remaining prompt?

Thx for reporting. Indeed, I haven't considered prefix_cache, and don't know if it's compatible.

wangxiyuan · 2025-05-12T09:22:38Z

examples/disaggregated-prefill-v1/disagg_prefill_proxy_server.py

@@ -0,0 +1,85 @@
+# SPDX-License-Identifier: Apache-2.0


Can you add a quick ReadMe in disaggregated-prefill-v1 folder to describe how to use the example, i.e. run bash disaggregated_prefill_multi_prefill.sh and then xxxx

wangxiyuan · 2025-05-12T09:26:37Z

vllm_ascend/distributed/llmdatadist_connector_v1.py

+        if self.role == llm_datadist.LLMRole.PROMPT:
+            options["llm.listenIpInfo"] = f"{self.local_device_ip}:26000"
+        self.datadist_engine.init(options)
+        self.kv_transfer = self.datadist_engine.kv_cache_manager


AFAIK, we're working on replacing kv_cache_manager to cache_manager, @zzzzwwjj

AFAIK, we're working on replacing kv_cache_manager to cache_manager, @zzzzwwjj

@wangxiyuan
Hi, I am playing around with cache_manager right now. But I find difficulties running the demo in https://gitee.com/ascend/samples/blob/master/python/level1_single_api/10_llm_data_dist/cache_manager_api_samples/pull_blocks_sample.py

with error below

llm_datadist.status.LLMException: [link] failed, error code is LLMStatusCode.LLM_LINK_FAILED, {1: 0, 2: 1}.

I am wondering if this this class is available in a single node case. In addition I added environment variable as the documentation stated here.

ganyi1996ppo · 2025-05-13T14:19:14Z

vllm_ascend/distributed/llmdatadist_connector_v1.py

+                                          axis=-2)
+
+            # Release reference count
+            self.llm_datadist_engine.kv_transfer.deallocate_cache(kv_buffer)


Not very familiar with llmdatadist, why deallocate_cache once you scatter_update the kv cache into datadist_kv_cache? the npu tensor you allocated seems based on this kv_buffer's address.

A newly allocated KV buffer has two references: one by the Cache ID, which is released by deallocate_cache; the other by the Cache Key, automatically released when a pull cache request is received. This deallocate_cache call releases the Cache ID reference, ensuring the KV buffer is automatically freed when a pull cache request arrives.

OK, sounds reasonable, that means the life cycle is totally maintained by llmdatadist, when we allocate cache with cachekey right?

Got, thanks for the explaination

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

jianzs · 2025-05-22T06:33:11Z

@wangxiyuan @Yikun Sorry, I've been swamped lately and haven't had time to merge the PR yet. The vLLM connector API changed. Let's merge this PR first, then I'll submit a new one for the new API as soon as possible.

github-actions bot added the module:core label Apr 27, 2025

jianzs mentioned this pull request Apr 27, 2025

[Feature] Support the v1 connector API #605

Open

jianzs force-pushed the zhengsj/datadist-conn-v1 branch 3 times, most recently from 2208ee4 to eb2591c Compare April 28, 2025 03:20

jianzs changed the title ~~[WIP][Feature] Impl the connector based on the llmdatadist for v1~~ [Feature] Impl the connector based on the llmdatadist for v1 Apr 28, 2025

jianzs force-pushed the zhengsj/datadist-conn-v1 branch from 01e6bd3 to 70e4719 Compare April 28, 2025 07:05

jianzs mentioned this pull request Apr 28, 2025

[Bug]: Cannot use PD separation feature with v0.8.4rc1 #696

Open

jianzs force-pushed the zhengsj/datadist-conn-v1 branch 4 times, most recently from f984e3b to c2318fe Compare May 6, 2025 00:59

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jianzs force-pushed the zhengsj/datadist-conn-v1 branch from 247e501 to 974b99e Compare May 8, 2025 06:22

jianzs force-pushed the zhengsj/datadist-conn-v1 branch from 7863de0 to 03ae1bf Compare May 9, 2025 02:58

ganyi1996ppo reviewed May 11, 2025

View reviewed changes

jianzs added 4 commits May 11, 2025 10:56

feat: impl the connector based on the llmdatadist for v1

884ed70

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

feat: resolve npu_reshape_and_cache error

08e158e

Ensure correct input for npu_reshape_and_cache function The 'slot_indices' parameter of npu_reshape_and_cache must be: - A torch.int32 tensor - Located on the NPU device Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

chore: lint code

e0bb3fb

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

feat: add offline inference example

82e62e9

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

jianzs added 10 commits May 11, 2025 10:56

feat: simplify 1p1d startup

b04a6c7

Eliminates the need to launch the meta server in the 1p1d environment. Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

chore: rename file

73489fd

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

chore: lint code

3ae97db

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

fix: resolve import issue when running with vllm 0.8.4

f26c0e0

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

chore: remove v0.8.4 patch

d967a87

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

chore: refine the init optins of datadist

0a1ddd2

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

feat: refine linking logic

5ff5a06

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

fix: manage KV cache buffer lifecycle to prevent premature deallocation

0d90c24

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

fix: correct finding the kv cache shape for mha

791993a

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

fix: reverse iteration over link_rets to safely remove clusters

5c752ed

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

ganyi1996ppo reviewed May 11, 2025

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MengqingCao mentioned this pull request May 14, 2025

[RFC]: P/D Disaggregation Support #841

Open

12 tasks

jianzs added 3 commits May 14, 2025 12:08

fix: correct block_ids assignment

cd57ee5

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

fix: typo

85eb470

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

avoid memory lack

540a57e

Signed-off-by: Jade Zheng <zheng.shoujian@outlook.com>

jianzs force-pushed the zhengsj/datadist-conn-v1 branch from ea7522f to 540a57e Compare May 22, 2025 06:21

jianzs changed the title ~~[Feature] Impl the connector based on the llmdatadist for v1~~ [Feature][1/2] Impl the connector based on the llmdatadist for v1 May 22, 2025

jianzs added the ready read for review label May 22, 2025

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[Feature][1/2] Impl the connector based on the llmdatadist for v1 #684

[Feature][1/2] Impl the connector based on the llmdatadist for v1 #684

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jianzs commented May 22, 2025

[Feature][1/2] Impl the connector based on the llmdatadist for v1 #684

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[Feature][1/2] Impl the connector based on the llmdatadist for v1 #684

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jianzs commented May 22, 2025

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