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- import time
- import torch
- import torch.distributed as dist
- import numpy as np
- import argparse
- import deepspeed
- import os
- from deepspeed.runtime.comm.nccl import NcclBackend
- parser = argparse.ArgumentParser()
- parser.add_argument('--local_rank', type=int, default=-1)
- args = parser.parse_args()
- deepspeed.init_distributed(dist_backend='nccl')
- args.local_rank = int(os.environ['LOCAL_RANK'])
- torch.cuda.set_device(args.local_rank)
- device = torch.device("cuda", args.local_rank)
- size = dist.get_world_size()
- rank = dist.get_rank()
- backend = NcclBackend()
- local_rank = args.local_rank
- # A simulated compression function using torch.distributed
- def torch_sim(a):
- a_sign = a.sign().add_(1).bool().float().add_(-0.5).mul_(2.0)
- scale = a.norm() / np.sqrt(a.numel())
- a_compressed = scale * a_sign
- a_sign = None
- worker_error = a - a_compressed
- dist.all_reduce(a_compressed)
- a_compressed.mul_(1 / dist.get_world_size())
- a_server_sign = a_compressed.sign().add_(1).bool().float().add_(-0.5).mul_(2.0)
- a_list = torch.chunk(a_compressed, chunks=dist.get_world_size())
- server_scale = [chunk_a.norm() / np.sqrt(chunk_a.numel()) for chunk_a in a_list]
- a_sign_list = torch.chunk(a_server_sign, dist.get_world_size())
- a_server_compressed = torch.cat(
- [server_scale[i] * a_sign_list[i] for i in range(dist.get_world_size())])
- rank = dist.get_rank()
- server_error = a_list[rank] - server_scale[rank] * a_sign_list[rank]
- torch.cuda.synchronize()
- torch.distributed.barrier()
- return a_server_compressed, worker_error, server_error
- tensor_size = 300 * 2**20
- server_size = int(tensor_size / size)
- if tensor_size % (8 * size) != 0:
- right_tensor_size = tensor_size + (8 * size - (tensor_size % (8 * size)))
- else:
- right_tensor_size = tensor_size
- right_server_size = right_tensor_size // size
- # Adding bias to the initialization of the gradient we are communicating
- # In order to get rid of the case where some elements in the gradient are too small
- a = (torch.rand(tensor_size, device=device) - 0.5) + 0.01 * rank
- worker_error = torch.zeros(right_tensor_size, device=device)
- server_error = torch.zeros(right_server_size, device=device)
- a_torch, worker_error_torch, server_error_torch = torch_sim(a)
- torch.cuda.empty_cache()
- a_after = backend.compressed_allreduce(a, worker_error, server_error, local_rank)
- threshold = 1e-6
- magnitude_threshold = 1e-6
- diff_mask = (a_after - a_torch) > threshold
- diff_server_mask = torch.chunk(diff_mask, size)[rank]
- mpi_server = torch.chunk(a_after, size)[rank] + server_error
- torch_server = torch.chunk(a_torch, size)[rank] + server_error_torch
- test_correctness = True
- # If the number in the compensated_server_m is too small (e.g 1e-8), then calling sign() might be problematic
- # The test would skip those numbers that are too small in compensated_server_m
- if test_correctness:
- if torch.sum(diff_server_mask) == 0:
- print('Successfully passed the test for NCCL Backend at Rank {}'.format(rank))
- else:
- check_mag_mask = mpi_server[diff_server_mask] > magnitude_threshold
- if torch.sum(check_mag_mask) == 0:
- print(
- 'Successfully passed the test for NCCL Backend at Rank {}'.format(rank))
- else:
- print('Fails at {} of positions'.format(torch.sum(check_mag_mask)))
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