diff --git a/docs/design/v1/p2p_nccl_connector.md b/docs/design/v1/p2p_nccl_connector.md index b1df93cfc85d..8f6a2b3b2dd7 100644 --- a/docs/design/v1/p2p_nccl_connector.md +++ b/docs/design/v1/p2p_nccl_connector.md @@ -31,7 +31,7 @@ Each P/D instance periodically sends a heartbeat packet to the Proxy/Router (cur ## KV Cache Transfer Methods -There are three methods for KVcache transfer: PUT, GET, and PUT_ASYNC. These methods can be specified using the `--kv-transfer-config` and `kv_connector_extra_config` parameters, specifically through the `send_type` field. Both PUT and PUT_ASYNC involve the P instance actively sending KVcache to the D instance. The difference is that PUT is a synchronous transfer method that blocks the main process, while PUT_ASYNC is an asynchronous transfer method. PUT_ASYNC uses a dedicated thread for sending KVcache, which means it does not block the main process. In contrast, the GET method involves the P instance saving the KVcache to the memory buffer after computing the prefill. The D instance then actively retrieves the computed KVcache from the P instance once it has allocated space for the KVcache. +There are three methods for KVCache transfer: PUT, GET, and PUT_ASYNC. These methods can be specified using the `--kv-transfer-config` and `kv_connector_extra_config` parameters, specifically through the `send_type` field. Both PUT and PUT_ASYNC involve the P instance actively sending KVCache to the D instance. The difference is that PUT is a synchronous transfer method that blocks the main process, while PUT_ASYNC is an asynchronous transfer method. PUT_ASYNC uses a dedicated thread for sending KVCache, which means it does not block the main process. In contrast, the GET method involves the P instance saving the KVCache to the memory buffer after computing the prefill. The D instance then actively retrieves the computed KVCache from the P instance once it has allocated space for the KVCache. Experimental results have shown that the performance of these methods, from highest to lowest, is as follows: PUT_ASYNC → GET → PUT. @@ -39,13 +39,13 @@ Experimental results have shown that the performance of these methods, from high As long as the address of the counterpart is known, point-to-point KV cache transfer (using NCCL) can be performed, without being constrained by rank and world size. To support dynamic scaling (expansion and contraction) of instances with PD disaggregation. This means that adding or removing P/D instances does not require a full system restart. -Each P/D instance only needs to create a single `P2pNcclEngine` instance. This instance maintains a ZMQ Server, which runs a dedicated thread to listen on the `zmq_addr` address and receive control flow requests from other instances. These requests include requests to establish an NCCL connection and requests to send KVcache metadata (such as tensor shapes and data types). However, it does not actually transmit the KVcache data itself. +Each P/D instance only needs to create a single `P2pNcclEngine` instance. This instance maintains a ZMQ Server, which runs a dedicated thread to listen on the `zmq_addr` address and receive control flow requests from other instances. These requests include requests to establish an NCCL connection and requests to send KVCache metadata (such as tensor shapes and data types). However, it does not actually transmit the KVCache data itself. -When a P instance and a D instance transmit KVcache for the first time, they need to establish a ZMQ connection and an NCCL group. For subsequent KVcache transmissions, this ZMQ connection and NCCL group are reused. The NCCL group consists of only two ranks, meaning the world size is equal to 2. This design is intended to support dynamic scaling, which means that adding or removing P/D instances does not require a full system restart. As long as the address of the counterpart is known, point-to-point KVcache transmission can be performed, without being restricted by rank or world size. +When a P instance and a D instance transmit KVCache for the first time, they need to establish a ZMQ connection and an NCCL group. For subsequent KVCache transmissions, this ZMQ connection and NCCL group are reused. The NCCL group consists of only two ranks, meaning the world size is equal to 2. This design is intended to support dynamic scaling, which means that adding or removing P/D instances does not require a full system restart. As long as the address of the counterpart is known, point-to-point KVCache transmission can be performed, without being restricted by rank or world size. ## NCCL Group Topology -Currently, only symmetric TP (Tensor Parallelism) methods are supported for KVcache transmission. Asymmetric TP and PP (Pipeline Parallelism) methods will be supported in the future. Figure 2 illustrates the 1P2D setup, where each instance has a TP (Tensor Parallelism) degree of 2. There are a total of 7 NCCL groups: three vLLM instances each have one NCCL group with TP=2. Additionally, the 0th GPU card of the P instance establishes an NCCL group with the 0th GPU card of each D instance. Similarly, the 1st GPU card of the P instance establishes an NCCL group with the 1st GPU card of each D instance. +Currently, only symmetric TP (Tensor Parallelism) methods are supported for KVCache transmission. Asymmetric TP and PP (Pipeline Parallelism) methods will be supported in the future. Figure 2 illustrates the 1P2D setup, where each instance has a TP (Tensor Parallelism) degree of 2. There are a total of 7 NCCL groups: three vLLM instances each have one NCCL group with TP=2. Additionally, the 0th GPU card of the P instance establishes an NCCL group with the 0th GPU card of each D instance. Similarly, the 1st GPU card of the P instance establishes an NCCL group with the 1st GPU card of each D instance. ![image2](https://github.com/user-attachments/assets/837e61d6-365e-4cbf-8640-6dd7ab295b36) @@ -53,32 +53,18 @@ Each NCCL group occupies a certain amount of GPU memory buffer for communication ## GPU Memory Buffer and Tensor Memory Pool -The trade-off in the size of the memory buffer is as follows: For P instances, the memory buffer is not required in PUT and PUT_ASYNC modes, but it is necessary in GET mode. For D instances, a memory buffer is needed in all three modes. The memory buffer for D instances should not be too large. Similarly, for P instances in GET mode, the memory buffer should also not be too large. The memory buffer of D instances is used to temporarily store KVcache sent by P instances. If it is too large, it will reduce the KVcache space available for normal inference by D instances, thereby decreasing the inference batch size and ultimately leading to a reduction in output throughput. The size of the memory buffer is configured by the parameter `kv_buffer_size`, measured in bytes, and is typically set to 5%～10% of the memory size. +The trade-off in the size of the memory buffer is as follows: For P instances, the memory buffer is not required in PUT and PUT_ASYNC modes, but it is necessary in GET mode. For D instances, a memory buffer is needed in all three modes. The memory buffer for D instances should not be too large. Similarly, for P instances in GET mode, the memory buffer should also not be too large. The memory buffer of D instances is used to temporarily store KVCache sent by P instances. If it is too large, it will reduce the KVCache space available for normal inference by D instances, thereby decreasing the inference batch size and ultimately leading to a reduction in output throughput. The size of the memory buffer is configured by the parameter `kv_buffer_size`, measured in bytes, and is typically set to 5%～10% of the memory size. -If the `--max-num-seqs` parameter for P instances is set to a large value, due to the large batch size, P instances will generate a large amount of KVcache simultaneously. This may exceed the capacity of the memory buffer of D instances, resulting in KVcache loss. Once KVcache is lost, D instances need to recompute Prefill, which is equivalent to performing Prefill twice. Consequently, the time-to-first-token (TTFT) will significantly increase, leading to degraded performance. +If the `--max-num-seqs` parameter for P instances is set to a large value, due to the large batch size, P instances will generate a large amount of KVCache simultaneously. This may exceed the capacity of the memory buffer of D instances, resulting in KVCache loss. Once KVCache is lost, D instances need to recompute Prefill, which is equivalent to performing Prefill twice. Consequently, the time-to-first-token (TTFT) will significantly increase, leading to degraded performance. -To address the above issues, I have designed and developed a local Tensor memory pool for storing KVcache, inspired by the buddy system used in Linux memory modules. Since the memory is sufficiently large, typically in the TB range on servers, there is no need to consider prefix caching or using block-based designs to reuse memory, thereby saving space. When the memory buffer is insufficient, KVcache can be directly stored in the Tensor memory pool, and D instances can subsequently retrieve KVcache from it. The read and write speed is that of PCIe, with PCIe 4.0 having a speed of approximately 21 GB/s, which is usually faster than the Prefill speed. Otherwise, solutions like Mooncake and lmcache would not be necessary. The Tensor memory pool acts as a flood diversion area, typically unused except during sudden traffic surges. In the worst-case scenario, my solution performs no worse than the normal situation with a Cache store. +To address the above issues, I have designed and developed a local Tensor memory pool for storing KVCache, inspired by the buddy system used in Linux memory modules. Since the memory is sufficiently large, typically in the TB range on servers, there is no need to consider prefix caching or using block-based designs to reuse memory, thereby saving space. When the memory buffer is insufficient, KVCache can be directly stored in the Tensor memory pool, and D instances can subsequently retrieve KVCache from it. The read and write speed is that of PCIe, with PCIe 4.0 having a speed of approximately 21 GB/s, which is usually faster than the Prefill speed. Otherwise, solutions like Mooncake and lmcache would not be necessary. The Tensor memory pool acts as a flood diversion area, typically unused except during sudden traffic surges. In the worst-case scenario, my solution performs no worse than the normal situation with a Cache store. # Install vLLM ??? console "Commands" ```shell - # Enter the home directory or your working directory. - cd /home - - # Download the installation package, and I will update the commit-id in time. You can directly copy the command. - wget https://vllm-wheels.s3.us-west-2.amazonaws.com/9112b443a042d8d815880b8780633882ad32b183/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl - - # Download the code repository. - git clone -b xpyd-v1 https://github.com/Abatom/vllm.git - cd vllm - - # Set the installation package path. - export VLLM_PRECOMPILED_WHEEL_LOCATION=/home/vllm-1.0.0.dev-cp38-abi3-manylinux1_x86_64.whl - - # installation - pip install -e . -v + pip install "vllm>=0.9.2" ``` # Run xPyD @@ -90,7 +76,7 @@ To address the above issues, I have designed and developed a local Tensor memory - You may need to modify the `kv_buffer_size` and `port` in the following commands (if there is a conflict). - `PUT_ASYNC` offers the best performance and should be prioritized. - The `--port` must be consistent with the `http_port` in the `--kv-transfer-config`. -- The `disagg_prefill_proxy_xpyd.py` script will use port 10001 (for receiving client requests) and port 30001 (for receiving service discovery from P and D instances). +- The `disagg_proxy_p2p_nccl_xpyd.py` script will use port 10001 (for receiving client requests) and port 30001 (for receiving service discovery from P and D instances). - The node running the proxy must have `quart` installed. - Supports multiple nodes; you just need to modify the `proxy_ip` and `proxy_port` in `--kv-transfer-config`. - In the following examples, it is assumed that **the proxy's IP is 10.0.1.1**. @@ -100,8 +86,8 @@ To address the above issues, I have designed and developed a local Tensor memory ### Proxy (e.g. 10.0.1.1) ```shell -cd {your vllm directory}/examples/online_serving/disagg_xpyd/ -python3 disagg_prefill_proxy_xpyd.py & +cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/ +python3 disagg_proxy_p2p_nccl_xpyd.py & ``` ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1) @@ -111,7 +97,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20005 \ + --port 20001 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -123,7 +109,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.9 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20005","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20001"}}' > /var/vllm.log 2>&1 & ``` ### Decode1 (e.g. 10.0.1.3 or 10.0.1.1) @@ -133,7 +119,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20009 \ + --port 20002 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -145,7 +131,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.7 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20009","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20002"}}' > /var/vllm.log 2>&1 & ``` ### Decode2 (e.g. 10.0.1.4 or 10.0.1.1) @@ -167,7 +153,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.7 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003"}}' > /var/vllm.log 2>&1 & ``` ### Decode3 (e.g. 10.0.1.5 or 10.0.1.1) @@ -177,7 +163,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20008 \ + --port 20004 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -189,7 +175,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.7 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20008","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20004"}}' > /var/vllm.log 2>&1 & ``` ## Run 3P1D @@ -197,8 +183,8 @@ python3 disagg_prefill_proxy_xpyd.py & ### Proxy (e.g. 10.0.1.1) ```shell -cd {your vllm directory}/examples/online_serving/disagg_xpyd/ -python3 disagg_prefill_proxy_xpyd.py & +cd {your vllm directory}/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/ +python3 disagg_proxy_p2p_nccl_xpyd.py & ``` ### Prefill1 (e.g. 10.0.1.2 or 10.0.1.1) @@ -208,7 +194,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=0 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20005 \ + --port 20001 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -220,7 +206,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.9 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20005","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"21001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20001"}}' > /var/vllm.log 2>&1 & ``` ### Prefill2 (e.g. 10.0.1.3 or 10.0.1.1) @@ -230,7 +216,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=1 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20009 \ + --port 20002 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -242,7 +228,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.9 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20009","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"22001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20002"}}' > /var/vllm.log 2>&1 & ``` ### Prefill3 (e.g. 10.0.1.4 or 10.0.1.1) @@ -264,7 +250,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.9 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_producer","kv_buffer_size":"1e1","kv_port":"23001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20003"}}' > /var/vllm.log 2>&1 & ``` ### Decode1 (e.g. 10.0.1.5 or 10.0.1.1) @@ -274,7 +260,7 @@ python3 disagg_prefill_proxy_xpyd.py & ```shell VLLM_USE_V1=1 CUDA_VISIBLE_DEVICES=3 vllm serve {your model directory} \ --host 0.0.0.0 \ - --port 20008 \ + --port 20004 \ --tensor-parallel-size 1 \ --seed 1024 \ --served-model-name base_model \ @@ -286,7 +272,7 @@ python3 disagg_prefill_proxy_xpyd.py & --gpu-memory-utilization 0.7 \ --disable-log-request \ --kv-transfer-config \ - '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20008","send_type":"PUT_ASYNC","nccl_num_channels":"16"}}' > /var/vllm.log 2>&1 & + '{"kv_connector":"P2pNcclConnector","kv_role":"kv_consumer","kv_buffer_size":"8e9","kv_port":"24001","kv_connector_extra_config":{"proxy_ip":"10.0.1.1","proxy_port":"30001","http_port":"20004"}}' > /var/vllm.log 2>&1 & ``` # Single request @@ -334,24 +320,6 @@ pgrep python | xargs kill -9 && pkill -f python # Test data -## **Scenario 1**: 1K input & 1K output tokens, E2E P99 latency ~20s -- **1P5D (6×A800) vs vLLM (1×A800)**: - - Throughput ↑7.2% (1085 → 6979/6) - - ITL (P99) ↓81.3% (120ms → 22.9ms) - - TTFT (P99) ↑26.8% (175ms → 222ms) - - TPOT: No change - -- **1P6D (7×A800) vs vLLM (1×A800)**: - - Throughput ↑9.6% (1085 → 8329/7) - - ITL (P99) ↓81.0% (120ms → 22.7ms) - - TTFT (P99) ↑210% (175ms →543ms) - - TPOT: No change - -## **Scenario 2**: 1K input & 200 output tokens, E2E P99 latency ~4s -- **1P1D (2×A800) vs vLLM (1×A800)**: - - Throughput ↑37.4% (537 → 1476/2) - - ITL (P99) ↓81.8% (127ms → 23.1ms) - - TTFT (P99) ↑41.8% (160ms → 227ms) - - TPOT: No change - -![testdata](https://github.com/user-attachments/assets/f791bfc7-9f3d-4e5c-9171-a42f9f4da627) +## **Scenario**: 1K input & 200 output tokens, E2E P99 latency ~2s + +![testdata](https://github.com/user-attachments/assets/cef0953b-4567-4bf9-b940-405b92a28eb1) diff --git a/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py b/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py index 4e82424d6cd7..ec58a183061e 100644 --- a/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py +++ b/examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_proxy_p2p_nccl_xpyd.py @@ -4,7 +4,9 @@ import os import socket import threading +import time import uuid +from typing import Any import aiohttp import msgpack @@ -12,12 +14,25 @@ from quart import Quart, make_response, request count = 0 -prefill_instances: dict[str, str] = {} # http_address: zmq_address -decode_instances: dict[str, str] = {} # http_address: zmq_address +prefill_instances: dict[str, Any] = {} # http_address: (zmq_address, stamp) +decode_instances: dict[str, Any] = {} # http_address: (zmq_address, stamp) prefill_cv = threading.Condition() decode_cv = threading.Condition() +DEFAULT_PING_SECONDS = 5 + + +def _remove_oldest_instances(instances: dict[str, Any]) -> None: + oldest_key = next(iter(instances), None) + while oldest_key is not None: + value = instances[oldest_key] + if value[1] > time.time(): + break + print(f"🔴Remove [HTTP:{oldest_key}, ZMQ:{value[0]}, stamp:{value[1]}]") + instances.pop(oldest_key, None) + oldest_key = next(iter(instances), None) + def _listen_for_register(poller, router_socket): while True: @@ -31,12 +46,23 @@ def _listen_for_register(poller, router_socket): global prefill_instances global prefill_cv with prefill_cv: - prefill_instances[data["http_address"]] = data["zmq_address"] + node = prefill_instances.pop(data["http_address"], None) + prefill_instances[data["http_address"]] = ( + data["zmq_address"], + time.time() + DEFAULT_PING_SECONDS, + ) + _remove_oldest_instances(prefill_instances) + elif data["type"] == "D": global decode_instances global decode_cv with decode_cv: - decode_instances[data["http_address"]] = data["zmq_address"] + node = decode_instances.pop(data["http_address"], None) + decode_instances[data["http_address"]] = ( + data["zmq_address"], + time.time() + DEFAULT_PING_SECONDS, + ) + _remove_oldest_instances(decode_instances) else: print( "Unexpected, Received message from %s, data: %s", @@ -44,6 +70,9 @@ def _listen_for_register(poller, router_socket): data, ) + if node is None: + print(f"🔵Add [HTTP:{data['http_address']}, ZMQ:{data['zmq_address']}]") + def start_service_discovery(hostname, port): if not hostname: @@ -105,12 +134,14 @@ async def handle_request(): with prefill_cv: prefill_list = list(prefill_instances.items()) prefill_addr, prefill_zmq_addr = prefill_list[count % len(prefill_list)] + prefill_zmq_addr = prefill_zmq_addr[0] global decode_instances global decode_cv with decode_cv: decode_list = list(decode_instances.items()) decode_addr, decode_zmq_addr = decode_list[count % len(decode_list)] + decode_zmq_addr = decode_zmq_addr[0] print( f"handle_request count: {count}, [HTTP:{prefill_addr}, " diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py index 52f589a6d718..d47a75461d72 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py @@ -13,7 +13,6 @@ from vllm.distributed.kv_transfer.kv_connector.v1.p2p.p2p_nccl_engine import ( P2pNcclEngine) from vllm.distributed.parallel_state import get_world_group -from vllm.forward_context import get_forward_context from vllm.logger import init_logger from vllm.v1.attention.backends.mla.common import MLACommonMetadata from vllm.v1.core.sched.output import SchedulerOutput @@ -238,32 +237,16 @@ def save_kv_layer(self, layer_name: str, kv_layer: torch.Tensor, assert self.p2p_nccl_engine is not None - def extract_kv_from_layer( - layer: torch.Tensor, - slot_mapping: torch.Tensor, - ) -> torch.Tensor: - """Extract the KV cache from the layer. - - Assume the shape of the layer is (2, num_pages, page_size, xxx) - if MLA is not used, and (num_pages, page_size, xxx) otherwise. - """ - if isinstance(attn_metadata, MLACommonMetadata): - num_pages, page_size = layer.shape[0], layer.shape[1] - return layer.reshape(num_pages * page_size, -1)[slot_mapping, - ...] - num_pages, page_size = layer.shape[1], layer.shape[2] - return layer.reshape(2, num_pages * page_size, -1)[:, slot_mapping, - ...] - connector_metadata = self._get_connector_metadata() assert isinstance(connector_metadata, P2pNcclConnectorMetadata) for request in connector_metadata.requests: request_id = request.request_id ip, port = self.parse_request_id(request_id, True) remote_address = ip + ":" + str(port + self._rank) - kv_cache = extract_kv_from_layer(kv_layer, request.slot_mapping) - self.p2p_nccl_engine.send_tensor(request_id + "#" + layer_name, - kv_cache, remote_address) + self.p2p_nccl_engine.send_tensor( + request_id + "#" + layer_name, kv_layer, remote_address, + request.slot_mapping, + isinstance(attn_metadata, MLACommonMetadata)) def wait_for_save(self): if self.is_producer: @@ -286,9 +269,10 @@ def get_finished( assert self.p2p_nccl_engine is not None - forward_context: ForwardContext = get_forward_context() + no_compile_layers = ( + self._vllm_config.compilation_config.static_forward_context) return self.p2p_nccl_engine.get_finished(finished_req_ids, - forward_context) + no_compile_layers) # ============================== # Scheduler-side methods @@ -418,14 +402,6 @@ def build_connector_meta( block_ids=block_ids, block_size=self._block_size) - # Requests loaded asynchronously are not in the scheduler_output. - # for request_id in self._requests_need_load: - # request, block_ids = self._requests_need_load[request_id] - # meta.add_request(request_id=request.request_id, - # token_ids=request.prompt_token_ids, - # block_ids=block_ids, - # block_size=self._block_size) - self._requests_need_load.clear() return meta diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_engine.py b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_engine.py index 6c9ccb2e301e..b94f2296dcb3 100644 --- a/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_engine.py +++ b/vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_engine.py @@ -8,7 +8,8 @@ import typing from collections import deque from contextlib import contextmanager -from typing import TYPE_CHECKING, Any, Optional +from dataclasses import dataclass +from typing import Any, Optional import msgpack import torch @@ -21,9 +22,6 @@ TensorMemoryPool) from vllm.utils import current_stream, get_ip -if TYPE_CHECKING: - from vllm.forward_context import ForwardContext - logger = logging.getLogger(__name__) DEFAULT_MEM_POOL_SIZE_GB = 32 @@ -59,6 +57,15 @@ def set_p2p_nccl_context(num_channels: str): os.environ.pop(var, None) +@dataclass +class SendQueueItem: + tensor_id: str + remote_address: str + tensor: torch.Tensor + slot_mapping: torch.Tensor + is_mla: bool + + class P2pNcclEngine: def __init__(self, @@ -112,24 +119,26 @@ def __init__(self, self.send_stream = torch.cuda.Stream() self.recv_stream = torch.cuda.Stream() - mem_pool_size_gb = self.config.get_from_extra_config( - "mem_pool_size_gb", DEFAULT_MEM_POOL_SIZE_GB) - self.pool = TensorMemoryPool(max_block_size=int(mem_pool_size_gb) * - 1024**3) # GB + mem_pool_size_gb = float( + self.config.get_from_extra_config("mem_pool_size_gb", + DEFAULT_MEM_POOL_SIZE_GB)) + self.pool = TensorMemoryPool(max_block_size=int(mem_pool_size_gb * + 1024**3)) # GB # The sending type includes tree mutually exclusive options: # PUT, GET, PUT_ASYNC. - self.send_type = self.config.get_from_extra_config("send_type", "PUT") + self.send_type = self.config.get_from_extra_config( + "send_type", "PUT_ASYNC") if self.send_type == "GET": # tensor_id: torch.Tensor self.send_store: dict[str, torch.Tensor] = {} else: # PUT or PUT_ASYNC # tensor_id: torch.Tensor - self.send_queue: deque[list[Any]] = deque() + self.send_queue: deque[SendQueueItem] = deque() self.send_request_id_to_tensor_ids: dict[str, set[str]] = {} if self.send_type == "PUT_ASYNC": - self._send_thread = threading.Thread(target=self._send_async, + self._send_thread = threading.Thread(target=self.send_async, daemon=True) self._send_thread.start() @@ -146,13 +155,12 @@ def __init__(self, "nccl_num_channels", "8") self._listener_thread = threading.Thread( - target=self._listen_for_requests, daemon=True) + target=self.listen_for_requests, daemon=True) self._listener_thread.start() self._ping_thread = None if port_offset == 0 and self.proxy_address != "": - self._ping_thread = threading.Thread(target=self._ping, - daemon=True) + self._ping_thread = threading.Thread(target=self.ping, daemon=True) self._ping_thread.start() logger.info( @@ -162,7 +170,7 @@ def __init__(self, self.http_address, self.zmq_address, self.proxy_address, self.send_type, self.buffer_size_threshold, self.nccl_num_channels) - def _create_connect(self, remote_address: typing.Optional[str] = None): + def create_connect(self, remote_address: typing.Optional[str] = None): assert remote_address is not None if remote_address not in self.socks: sock = self.context.socket(zmq.DEALER) @@ -184,7 +192,7 @@ def _create_connect(self, remote_address: typing.Optional[str] = None): comm: ncclComm_t = self.nccl.ncclCommInitRank( 2, unique_id, rank) self.comms[remote_address] = (comm, rank) - logger.info("🤝ncclCommInitRank Success, %s👉%s, MyRank: %s", + logger.info("🤝ncclCommInitRank Success, %s👉%s, MyRank:%s", self.zmq_address, remote_address, rank) return self.socks[remote_address], self.comms[remote_address] @@ -194,44 +202,54 @@ def send_tensor( tensor_id: str, tensor: torch.Tensor, remote_address: typing.Optional[str] = None, + slot_mapping: torch.Tensor = None, + is_mla: bool = False, ) -> bool: if remote_address is None: with self.recv_store_cv: self.recv_store[tensor_id] = tensor self.recv_store_cv.notify() return True - else: - if self.send_type == "PUT": - return self._send_sync(tensor_id, tensor, remote_address) - elif self.send_type == "PUT_ASYNC": - with self.send_queue_cv: - self.send_queue.append([tensor_id, remote_address, tensor]) - self.send_queue_cv.notify() - else: # GET - with self.send_store_cv: - tensor_size = tensor.element_size() * tensor.numel() - while (self.buffer_size + tensor_size - > self.buffer_size_threshold): - oldest_tenser_id = next(iter(self.send_store)) - oldest_tenser = self.send_store.pop(oldest_tenser_id) - oldest_tenser_size = oldest_tenser.element_size( - ) * oldest_tenser.numel() - self.buffer_size -= oldest_tenser_size - logger.info( - "⛔[GET]Send to %s, tensor_id:%s, tensor_size:%d," - " buffer_size:%d, oldest_tenser_size:%d, rank:%d", - remote_address, tensor_id, tensor_size, - self.buffer_size, oldest_tenser_size, self.rank) - - self.send_store[tensor_id] = tensor - self.buffer_size += tensor_size - logger.debug( - "🔵[GET]Send to %s, tensor_id:%s, tensor_size:%d, " - "shape:%s, rank:%d, buffer_size:%d(%.2f%%)", - remote_address, tensor_id, tensor_size, tensor.shape, - self.rank, self.buffer_size, - self.buffer_size / self.buffer_size_threshold * 100) + item = SendQueueItem(tensor_id=tensor_id, + remote_address=remote_address, + tensor=tensor, + slot_mapping=slot_mapping, + is_mla=is_mla) + + if self.send_type == "PUT": + return self.send_sync(item) + + if self.send_type == "PUT_ASYNC": + with self.send_queue_cv: + self.send_queue.append(item) + self.send_queue_cv.notify() + return True + + # GET + with self.send_store_cv: + tensor_size = tensor.element_size() * tensor.numel() + while (self.buffer_size + tensor_size + > self.buffer_size_threshold): + oldest_tenser_id = next(iter(self.send_store)) + oldest_tenser = self.send_store.pop(oldest_tenser_id) + oldest_tenser_size = oldest_tenser.element_size( + ) * oldest_tenser.numel() + self.buffer_size -= oldest_tenser_size + logger.info( + "⛔[GET]Send to %s, tensor_id:%s, tensor_size:%d," + " buffer_size:%d, oldest_tenser_size:%d, rank:%d", + remote_address, tensor_id, tensor_size, self.buffer_size, + oldest_tenser_size, self.rank) + + self.send_store[tensor_id] = tensor + self.buffer_size += tensor_size + logger.debug( + "🔵[GET]Send to %s, tensor_id:%s, tensor_size:%d, " + "shape:%s, rank:%d, buffer_size:%d(%.2f%%)", remote_address, + tensor_id, tensor_size, tensor.shape, self.rank, + self.buffer_size, + self.buffer_size / self.buffer_size_threshold * 100) return True def recv_tensor( @@ -267,7 +285,7 @@ def recv_tensor( return None if remote_address not in self.socks: - self._create_connect(remote_address) + self.create_connect(remote_address) sock = self.socks[remote_address] comm, rank = self.comms[remote_address] @@ -282,121 +300,121 @@ def recv_tensor( remote_address, tensor_id, data["ret"]) return None - tensor = torch.empty(data["shape"], - dtype=getattr(torch, data["dtype"]), - device=self.device) + with torch.cuda.stream(self.recv_stream): + tensor = torch.empty(data["shape"], + dtype=getattr(torch, data["dtype"]), + device=self.device) - self._recv(comm, tensor, rank ^ 1, self.recv_stream) + self.recv(comm, tensor, rank ^ 1, self.recv_stream) return tensor - def _listen_for_requests(self): + def listen_for_requests(self): while True: socks = dict(self.poller.poll()) - if self.router_socket in socks: - remote_address, message = self.router_socket.recv_multipart() - data = msgpack.loads(message) - if data["cmd"] == "NEW": - unique_id = self.nccl.unique_id_from_bytes( - bytes(data["unique_id"])) - with torch.cuda.device(self.device): - rank = 1 - with set_p2p_nccl_context(self.nccl_num_channels): - comm: ncclComm_t = self.nccl.ncclCommInitRank( - 2, unique_id, rank) - self.comms[remote_address.decode()] = (comm, rank) - logger.info( - "🤝ncclCommInitRank Success, %s👈%s, MyRank:%s", - self.zmq_address, remote_address.decode(), rank) - elif data["cmd"] == "PUT": - tensor_id = data["tensor_id"] - try: - with torch.cuda.stream(self.recv_stream): - tensor = torch.empty(data["shape"], - dtype=getattr( - torch, data["dtype"]), - device=self.device) - self.router_socket.send_multipart( - [remote_address, b"0"]) - comm, rank = self.comms[remote_address.decode()] - self._recv(comm, tensor, rank ^ 1, self.recv_stream) - tensor_size = tensor.element_size() * tensor.numel() - if (self.buffer_size + tensor_size - > self.buffer_size_threshold): - # Store Tensor in memory pool - addr = self.pool.store_tensor(tensor) - tensor = (addr, tensor.dtype, tensor.shape) - logger.warning( - "🔴[PUT]Recv Tensor, Out Of Threshold, " - "%s👈%s, data:%s, addr:%d", self.zmq_address, - remote_address.decode(), data, addr) - else: - self.buffer_size += tensor_size - - except torch.cuda.OutOfMemoryError: - self.router_socket.send_multipart( - [remote_address, b"1"]) - tensor = None + if self.router_socket not in socks: + continue + + remote_address, message = self.router_socket.recv_multipart() + data = msgpack.loads(message) + if data["cmd"] == "NEW": + unique_id = self.nccl.unique_id_from_bytes( + bytes(data["unique_id"])) + with torch.cuda.device(self.device): + rank = 1 + with set_p2p_nccl_context(self.nccl_num_channels): + comm: ncclComm_t = self.nccl.ncclCommInitRank( + 2, unique_id, rank) + self.comms[remote_address.decode()] = (comm, rank) + logger.info("🤝ncclCommInitRank Success, %s👈%s, MyRank:%s", + self.zmq_address, remote_address.decode(), + rank) + elif data["cmd"] == "PUT": + tensor_id = data["tensor_id"] + try: + with torch.cuda.stream(self.recv_stream): + tensor = torch.empty(data["shape"], + dtype=getattr( + torch, data["dtype"]), + device=self.device) + self.router_socket.send_multipart([remote_address, b"0"]) + comm, rank = self.comms[remote_address.decode()] + self.recv(comm, tensor, rank ^ 1, self.recv_stream) + tensor_size = tensor.element_size() * tensor.numel() + if (self.buffer_size + tensor_size + > self.buffer_size_threshold): + # Store Tensor in memory pool + addr = self.pool.store_tensor(tensor) + tensor = (addr, tensor.dtype, tensor.shape) logger.warning( - "🔴[PUT]Recv Tensor, Out Of Memory, %s👈%s, " - "data:%s", self.zmq_address, - remote_address.decode(), data) - - with self.recv_store_cv: - self.recv_store[tensor_id] = tensor - self._have_received_tensor_id(tensor_id) - self.recv_store_cv.notify() - - elif data["cmd"] == "GET": - tensor_id = data["tensor_id"] - with self.send_store_cv: - tensor = self.send_store.pop(tensor_id, None) - if tensor is not None: - data = { - "ret": 0, - "shape": tensor.shape, - "dtype": - str(tensor.dtype).replace("torch.", "") - } - # LRU - self.send_store[tensor_id] = tensor - self._have_sent_tensor_id(tensor_id) - else: - data = {"ret": 1} - - self.router_socket.send_multipart( - [remote_address, msgpack.dumps(data)]) - - if data["ret"] == 0: - comm, rank = self.comms[remote_address.decode()] - self._send(comm, tensor.to(self.device), rank ^ 1, - self.send_stream) - else: + "🔴[PUT]Recv Tensor, Out Of Threshold, " + "%s👈%s, data:%s, addr:%d", self.zmq_address, + remote_address.decode(), data, addr) + else: + self.buffer_size += tensor_size + + except torch.cuda.OutOfMemoryError: + self.router_socket.send_multipart([remote_address, b"1"]) + tensor = None logger.warning( - "🚧Unexpected, Received message from %s, data:%s", - remote_address, data) + "🔴[PUT]Recv Tensor, Out Of Memory, %s👈%s, " + "data:%s", self.zmq_address, remote_address.decode(), + data) - def _have_sent_tensor_id(self, tensor_id: str): + with self.recv_store_cv: + self.recv_store[tensor_id] = tensor + self.have_received_tensor_id(tensor_id) + self.recv_store_cv.notify() + + elif data["cmd"] == "GET": + tensor_id = data["tensor_id"] + with self.send_store_cv: + tensor = self.send_store.pop(tensor_id, None) + if tensor is not None: + data = { + "ret": 0, + "shape": tensor.shape, + "dtype": str(tensor.dtype).replace("torch.", "") + } + # LRU + self.send_store[tensor_id] = tensor + self.have_sent_tensor_id(tensor_id) + else: + data = {"ret": 1} + + self.router_socket.send_multipart( + [remote_address, msgpack.dumps(data)]) + + if data["ret"] == 0: + comm, rank = self.comms[remote_address.decode()] + self.send(comm, tensor.to(self.device), rank ^ 1, + self.send_stream) + else: + logger.warning( + "🚧Unexpected, Received message from %s, data:%s", + remote_address, data) + + def have_sent_tensor_id(self, tensor_id: str): request_id = tensor_id.split('#')[0] if request_id not in self.send_request_id_to_tensor_ids: self.send_request_id_to_tensor_ids[request_id] = set() self.send_request_id_to_tensor_ids[request_id].add(tensor_id) - def _have_received_tensor_id(self, tensor_id: str): + def have_received_tensor_id(self, tensor_id: str): request_id = tensor_id.split('#')[0] if request_id not in self.recv_request_id_to_tensor_ids: self.recv_request_id_to_tensor_ids[request_id] = set() self.recv_request_id_to_tensor_ids[request_id].add(tensor_id) - def _send_async(self): + def send_async(self): while True: with self.send_queue_cv: while not self.send_queue: self.send_queue_cv.wait() - tensor_id, remote_address, tensor = self.send_queue.popleft() + item = self.send_queue.popleft() if not self.send_queue: self.send_queue_cv.notify() - self._send_sync(tensor_id, tensor, remote_address) + self.send_sync(item) def wait_for_sent(self): if self.send_type == "PUT_ASYNC": @@ -409,22 +427,21 @@ def wait_for_sent(self): "🚧[PUT_ASYNC]It took %.3fms to wait for the send_queue" " to be empty, rank:%d", duration * 1000, self.rank) - def _send_sync( - self, - tensor_id: str, - tensor: torch.Tensor, - remote_address: typing.Optional[str] = None, - ) -> bool: - if remote_address is None: + def send_sync(self, item: SendQueueItem) -> bool: + if item.remote_address is None: return False - if remote_address not in self.socks: - self._create_connect(remote_address) + if item.remote_address not in self.socks: + self.create_connect(item.remote_address) - sock = self.socks[remote_address] - comm, rank = self.comms[remote_address] + with self.send_stream: + tensor = self.extract_kv_from_layer(item.is_mla, item.tensor, + item.slot_mapping) + + sock = self.socks[item.remote_address] + comm, rank = self.comms[item.remote_address] data = { "cmd": "PUT", - "tensor_id": tensor_id, + "tensor_id": item.tensor_id, "shape": tensor.shape, "dtype": str(tensor.dtype).replace("torch.", "") } @@ -435,20 +452,21 @@ def _send_sync( logger.error( "🔴Send Tensor, Peer Out Of Memory/Threshold, %s 👉 %s, " "MyRank:%s, data:%s, tensor:%s, size:%fGB, response:%s", - self.zmq_address, remote_address, rank, data, tensor.shape, + self.zmq_address, item.remote_address, rank, data, + tensor.shape, tensor.element_size() * tensor.numel() / 1024**3, response.decode()) return False - self._send(comm, tensor.to(self.device), rank ^ 1, self.send_stream) + self.send(comm, tensor.to(self.device), rank ^ 1, self.send_stream) if self.send_type == "PUT_ASYNC": - self._have_sent_tensor_id(tensor_id) + self.have_sent_tensor_id(item.tensor_id) return True def get_finished( - self, finished_req_ids: set[str], forward_context: "ForwardContext" + self, finished_req_ids: set[str], no_compile_layers ) -> tuple[Optional[set[str]], Optional[set[str]]]: """ Notifies worker-side connector ids of requests that have @@ -463,7 +481,7 @@ def get_finished( # Clear the buffer upon request completion. for request_id in finished_req_ids: - for layer_name in forward_context.no_compile_layers: + for layer_name in no_compile_layers: tensor_id = request_id + "#" + layer_name if tensor_id in self.recv_store: with self.recv_store_cv: @@ -472,7 +490,6 @@ def get_finished( request_id, None) self.recv_request_id_to_tensor_ids.pop( request_id, None) - addr = 0 if isinstance(tensor, tuple): addr, _, _ = tensor self.pool.free(addr) @@ -485,7 +502,7 @@ def get_finished( return finished_sending or None, finished_recving or None - def _ping(self): + def ping(self): sock = self.context.socket(zmq.DEALER) sock.setsockopt_string(zmq.IDENTITY, self.zmq_address) logger.debug("ping start, zmq_address:%s", self.zmq_address) @@ -499,7 +516,7 @@ def _ping(self): sock.send(msgpack.dumps(data)) time.sleep(3) - def _send(self, comm, tensor: torch.Tensor, dst: int, stream=None): + def send(self, comm, tensor: torch.Tensor, dst: int, stream=None): assert tensor.device == self.device, ( f"this nccl communicator is created to work on {self.device}, " f"but the input tensor is on {tensor.device}") @@ -512,7 +529,7 @@ def _send(self, comm, tensor: torch.Tensor, dst: int, stream=None): comm, cudaStream_t(stream.cuda_stream)) stream.synchronize() - def _recv(self, comm, tensor: torch.Tensor, src: int, stream=None): + def recv(self, comm, tensor: torch.Tensor, src: int, stream=None): assert tensor.device == self.device, ( f"this nccl communicator is created to work on {self.device}, " f"but the input tensor is on {tensor.device}") @@ -531,3 +548,21 @@ def close(self) -> None: self._send_thread.join() if self._ping_thread is not None: self._ping_thread.join() + + @staticmethod + def extract_kv_from_layer( + is_mla: bool, + layer: torch.Tensor, + slot_mapping: torch.Tensor, + ) -> torch.Tensor: + """Extract the KV cache from the layer. + Assume the shape of the layer is (2, num_pages, page_size, xxx) + if MLA is not used, and (num_pages, page_size, xxx) otherwise. + """ + if is_mla: + num_pages, page_size = layer.shape[0], layer.shape[1] + return layer.reshape(num_pages * page_size, -1)[slot_mapping, ...] + + num_pages, page_size = layer.shape[1], layer.shape[2] + return layer.reshape(2, num_pages * page_size, -1)[:, slot_mapping, + ...]