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https://github.com/microsoft/mscclpp.git
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af0bb86e07
First step to merge msccl-tools into mscclpp repo. In this step will move all msccl related code, pass the current tests and do some necessary refactor. Add `mscclpp.language` module Add `_InstructionOptimizer` and `DagOptimizer` class to optimize the dag Add `DagLower` to lower dag to intermediate representation Add documents for mscclpp.language Remove msccl related code
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import argparse
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from mscclpp.language import *
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from mscclpp.language.collectives import AllReduce
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from mscclpp.language.buffer import Buffer
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def allreduce_nvls(gpus, instances):
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"""
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Allreduce via NVLS channel
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Steps:
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1. Sync all the ranks to make sure the data is ready.
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2. Call group_load_reduce to reduce the data.
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3. Call group_store to propagate the data to all the ranks.
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"""
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size = gpus
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chunksperloop = gpus
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collective = AllReduce(size, chunksperloop, True)
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with MSCCLPPProgram(
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"allreduce_nvls",
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collective,
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size,
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instances,
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):
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# Each rank sends the nth chunk to the nth rank into scratch space
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for rank in range(size):
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index = rank
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c = chunk(rank, Buffer.input, index)
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reduce_chunks = []
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# make sure the data is ready
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for nghr in range(size):
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if rank != nghr:
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c_peer = chunk(nghr, Buffer.input, index)
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reduce_chunks.append(c_peer)
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c.signal(nghr, Buffer.input, index, sendtb=0)
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for nghr in range(size):
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if rank != nghr:
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c.wait(nghr, Buffer.input, index, recvtb=0)
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c = c.group_load_reduce(reduce_chunks, recvtb=0)
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ngbrs = [nghr for nghr in range(size) if nghr != rank]
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c.group_store(ngbrs, sendtb=0)
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Json()
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Check()
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parser = argparse.ArgumentParser()
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parser.add_argument("num_gpus", type=int, help="number of gpus")
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parser.add_argument("instances", type=int, help="number of instances")
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args = parser.parse_args()
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allreduce_nvls(args.num_gpus, args.instances)
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