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jc
95257c1dbd
* RDMACommunicator send key and value scale --------- Co-authored-by: kevin <chengyf112@gmail.com> Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import traceback
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from fastdeploy.cache_manager.transfer_factory.utils import get_rdma_nics
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from fastdeploy.utils import get_logger
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logger = get_logger("cache_messager", "cache_messager.log")
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class RDMACommManager:
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"""
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RDMACommManager to manage rdma communication
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"""
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def __init__(
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self,
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splitwise_role,
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gpu_id,
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cache_k_ptr_list,
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cache_v_ptr_list,
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max_block_num,
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block_bytes,
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rdma_port,
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cache_k_scale_ptr_list=[],
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cache_v_scale_ptr_list=[],
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scale_block_bytes=0,
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prefill_tp_size=1,
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prefill_tp_idx=0,
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):
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try:
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import os
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import subprocess
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from fastdeploy.platforms import current_platform
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if os.getenv("KVCACHE_GDRCOPY_FLUSH_ENABLE", "") == "" and current_platform.is_cuda():
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command = ["nvidia-smi", "-i", "0", "--query-gpu=compute_cap", "--format=csv,noheader"]
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result = subprocess.run(
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command,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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check=False,
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)
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logger.info(f"nvidia-smi command: {command}")
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logger.info(f"nvidia-smi output: {result.stdout}")
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if result.returncode != 0:
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raise RuntimeError(f"Failed to get compute capability via nvidia-smi: {result.stderr.strip()}")
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major, minor = result.stdout.strip().split(".")
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if major == "8": # for ampere arch
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os.environ["KVCACHE_GDRCOPY_FLUSH_ENABLE"] = "1"
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logger.info("Setting environment variable: export KVCACHE_GDRCOPY_FLUSH_ENABLE=1")
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if os.getenv("KVCACHE_RDMA_NICS", "") == "" and current_platform.is_cuda():
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rdma_nics = get_rdma_nics()
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os.environ["KVCACHE_RDMA_NICS"] = rdma_nics
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logger.info(f"Setting environment variable: export KVCACHE_RDMA_NICS={rdma_nics}")
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except Exception as e:
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raise RuntimeError(f"Failed to initialize RDMA environment! {e} {traceback.format_exc()}")
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try:
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import rdma_comm
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except ImportError:
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raise RuntimeError(
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"The installation of the RDMA library failed. Confirm whether your network card supports RDMA transmission."
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)
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self.messager = rdma_comm.RDMACommunicator(
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splitwise_role,
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gpu_id,
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str(rdma_port) if splitwise_role == "decode" else "0",
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cache_k_ptr_list,
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cache_v_ptr_list,
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max_block_num,
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block_bytes,
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cache_k_scale_ptr_list,
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cache_v_scale_ptr_list,
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scale_block_bytes,
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prefill_tp_size,
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prefill_tp_idx,
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)
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self.splitwise_role = splitwise_role
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self.connected_rdma = set()
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logger.info(
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f"init rdma messager {gpu_id} {rdma_port}, prefill_tp_size: {prefill_tp_size}, prefill_tp_idx: {prefill_tp_idx}"
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)
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def connect(self, ip, port, tp_size=0):
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"""
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Connect to remote gpu and write cache.
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"""
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assert self.splitwise_role == "prefill", "only prefill can call this method"
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ret = self.messager.is_connected(ip, str(port))
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if ret:
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return True
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ret = self.messager.connect(ip, str(port), tp_size)
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logger.info(f"connect to remote rdma address {ip}:{port} status is {ret}")
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return ret == 0
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def write_cache(self, ip, port, local_block_ids, remote_block_ids, layer_idx):
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"""
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Connect to remote gpu and write cache.
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"""
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return self.messager.write_cache(ip, str(port), local_block_ids, remote_block_ids, layer_idx)
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