e489ad7a21
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**
commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:18:24 2025 -0500
Add SPDX license headers to python source files
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
also be easily used by tools to help manage license compliance.
The Linux Foundation runs license scans against the codebase to help
ensure
we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
More information can be found on the SPDX site:
- https://spdx.dev/learn/handling-license-info/
Signed-off-by: Russell Bryant <rbryant@redhat.com>
commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date: Fri Jan 31 14:36:32 2025 -0500
Check for SPDX headers using pre-commit
Signed-off-by: Russell Bryant <rbryant@redhat.com>
---------
Signed-off-by: Russell Bryant <rbryant@redhat.com>
333 lines
12 KiB
Python
333 lines
12 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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import multiprocessing
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import os
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from typing import Dict, List
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import pytest
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import torch
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import torch.distributed
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from vllm.distributed.communication_op import ( # noqa
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tensor_model_parallel_all_reduce)
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from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
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from vllm.distributed.device_communicators.pynccl_wrapper import NCCLLibrary
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from vllm.distributed.parallel_state import (ensure_model_parallel_initialized,
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get_world_group, graph_capture,
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init_distributed_environment)
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from vllm.utils import update_environment_variables
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def distributed_run(fn, world_size):
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number_of_processes = world_size
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processes: List[multiprocessing.Process] = []
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for i in range(number_of_processes):
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env: Dict[str, str] = {}
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env['RANK'] = str(i)
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env['LOCAL_RANK'] = str(i)
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env['WORLD_SIZE'] = str(number_of_processes)
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env['LOCAL_WORLD_SIZE'] = str(number_of_processes)
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env['MASTER_ADDR'] = 'localhost'
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env['MASTER_PORT'] = '12345'
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p = multiprocessing.Process(target=fn, args=(env, ))
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processes.append(p)
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p.start()
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for p in processes:
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p.join()
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for p in processes:
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assert p.exitcode == 0
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def worker_fn_wrapper(fn):
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# `multiprocessing.Process` cannot accept environment variables directly
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# so we need to pass the environment variables as arguments
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# and update the environment variables in the function
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def wrapped_fn(env):
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update_environment_variables(env)
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local_rank = os.environ['LOCAL_RANK']
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device = torch.device(f"cuda:{local_rank}")
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torch.cuda.set_device(device)
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init_distributed_environment()
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fn()
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return wrapped_fn
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@worker_fn_wrapper
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def worker_fn():
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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tensor = torch.ones(16, 1024, 1024,
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dtype=torch.float32).cuda(pynccl_comm.rank)
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tensor = pynccl_comm.all_reduce(tensor)
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torch.cuda.synchronize()
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assert torch.all(tensor == pynccl_comm.world_size).cpu().item()
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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def test_pynccl():
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distributed_run(worker_fn, 2)
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@worker_fn_wrapper
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def multiple_allreduce_worker_fn():
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device = torch.device(f"cuda:{torch.distributed.get_rank()}")
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groups = [
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torch.distributed.new_group(ranks=[0, 1], backend="gloo"),
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torch.distributed.new_group(ranks=[2, 3], backend="gloo")
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]
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group = groups[0] if torch.distributed.get_rank() in [0, 1] else groups[1]
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pynccl_comm = PyNcclCommunicator(group=group, device=device)
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tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
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# two groups can communicate independently
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if torch.distributed.get_rank() in [0, 1]:
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tensor = pynccl_comm.all_reduce(tensor)
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tensor = pynccl_comm.all_reduce(tensor)
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torch.cuda.synchronize()
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assert torch.all(tensor == 4).cpu().item()
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else:
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tensor = pynccl_comm.all_reduce(tensor)
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torch.cuda.synchronize()
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assert torch.all(tensor == 2).cpu().item()
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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def test_pynccl_multiple_allreduce():
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# this tests pynccl for multiple tp groups, in a standalone way
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# i.e. call `pynccl_comm.all_reduce` directly
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distributed_run(multiple_allreduce_worker_fn, 4)
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@worker_fn_wrapper
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def multiple_allreduce_with_vllm_worker_fn():
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device = torch.device(f"cuda:{torch.distributed.get_rank()}")
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ensure_model_parallel_initialized(2, 2)
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tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
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with graph_capture(device=device):
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# two tp groups can communicate independently
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if torch.distributed.get_rank() in [0, 1]:
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tensor = tensor_model_parallel_all_reduce(tensor)
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tensor = tensor_model_parallel_all_reduce(tensor)
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torch.cuda.synchronize()
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assert torch.all(tensor == 4).cpu().item()
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else:
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tensor = tensor_model_parallel_all_reduce(tensor)
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torch.cuda.synchronize()
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assert torch.all(tensor == 2).cpu().item()
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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def test_pynccl_multiple_allreduce_with_vllm():
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# this tests pynccl for multiple tp groups, together with vllm
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# i.e. call `tensor_model_parallel_all_reduce`
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distributed_run(multiple_allreduce_with_vllm_worker_fn, 4)
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@worker_fn_wrapper
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def worker_fn_with_cudagraph():
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with torch.no_grad():
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graph = torch.cuda.CUDAGraph()
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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# run something in the default stream to initialize torch engine
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a = torch.ones((4, 4), device=f'cuda:{pynccl_comm.rank}')
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torch.cuda.synchronize()
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with torch.cuda.graph(graph):
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a_out = pynccl_comm.all_reduce(a)
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torch.cuda.synchronize()
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graph.replay()
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torch.cuda.synchronize()
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assert torch.all(a_out == pynccl_comm.world_size).cpu().item()
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@worker_fn_wrapper
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def all_gather_worker_fn():
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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rank = pynccl_comm.rank
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world_size = pynccl_comm.world_size
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device = f'cuda:{pynccl_comm.rank}'
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num_elems = 1000
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tensor = torch.arange(num_elems, dtype=torch.float32,
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device=device) + rank * num_elems
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result = torch.zeros(num_elems * world_size,
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dtype=torch.float32,
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device=device)
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expected = torch.cat([
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torch.arange(num_elems, dtype=torch.float32) + r * num_elems
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for r in range(world_size)
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]).to(device)
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pynccl_comm.all_gather(result, tensor)
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torch.cuda.synchronize()
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torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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def test_pynccl_all_gather():
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distributed_run(all_gather_worker_fn, 2)
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@worker_fn_wrapper
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def reduce_scatter_worker_fn():
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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rank = pynccl_comm.rank
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world_size = pynccl_comm.world_size
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device = f'cuda:{pynccl_comm.rank}'
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num_elems = 1000
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tensor = torch.arange(num_elems, dtype=torch.float32,
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device=device) + rank * num_elems
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assert (num_elems % world_size == 0)
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result = torch.zeros(num_elems // world_size,
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dtype=torch.float32,
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device=device)
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# Calculate expected result for this rank's chunk
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scattered_size = num_elems // world_size
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all_tensors = [
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torch.arange(num_elems, dtype=torch.float32) + r * num_elems
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for r in range(world_size)
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]
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expected = sum(tensor[rank * scattered_size:(rank + 1) * scattered_size]
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for tensor in all_tensors).to(device)
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pynccl_comm.reduce_scatter(result, tensor)
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torch.cuda.synchronize()
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torch.testing.assert_close(result, expected, rtol=1e-5, atol=1e-8)
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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def test_pynccl_reduce_scatter():
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distributed_run(reduce_scatter_worker_fn, 2)
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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def test_pynccl_with_cudagraph():
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distributed_run(worker_fn_with_cudagraph, 2)
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@worker_fn_wrapper
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def send_recv_worker_fn():
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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if pynccl_comm.rank == 0:
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tensor = torch.ones(16, 1024, 1024,
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dtype=torch.float32).cuda(pynccl_comm.rank)
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else:
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tensor = torch.empty(16, 1024, 1024,
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dtype=torch.float32).cuda(pynccl_comm.rank)
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if pynccl_comm.rank == 0:
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pynccl_comm.send(tensor,
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dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
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else:
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pynccl_comm.recv(tensor,
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src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
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torch.cuda.synchronize()
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assert torch.all(tensor == 1).cpu().item()
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@pytest.mark.skipif(torch.cuda.device_count() < 2,
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reason="Need at least 2 GPUs to run the test.")
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def test_pynccl_send_recv():
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distributed_run(send_recv_worker_fn, 2)
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@worker_fn_wrapper
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def multiple_send_recv_worker_fn():
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device = torch.device(f"cuda:{torch.distributed.get_rank()}")
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groups = [
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torch.distributed.new_group(ranks=[0, 2], backend="gloo"),
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torch.distributed.new_group(ranks=[1, 3], backend="gloo")
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]
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group = groups[0] if torch.distributed.get_rank() in [0, 2] else groups[1]
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pynccl_comm = PyNcclCommunicator(group=group, device=device)
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if torch.distributed.get_rank() == 0:
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tensor = torch.ones(16, 1024, 1024, dtype=torch.float32, device=device)
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elif torch.distributed.get_rank() == 1:
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tensor = 2 * torch.ones(
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16, 1024, 1024, dtype=torch.float32, device=device)
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else:
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tensor = torch.empty(16,
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1024,
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1024,
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dtype=torch.float32,
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device=device)
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if torch.distributed.get_rank() in [0, 1]:
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pynccl_comm.send(tensor,
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dst=(pynccl_comm.rank + 1) % pynccl_comm.world_size)
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else:
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pynccl_comm.recv(tensor,
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src=(pynccl_comm.rank - 1) % pynccl_comm.world_size)
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torch.cuda.synchronize()
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if torch.distributed.get_rank() in [0, 2]:
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assert torch.all(tensor == 1).cpu().item()
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else:
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assert torch.all(tensor == 2).cpu().item()
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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def test_pynccl_multiple_send_recv():
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distributed_run(multiple_send_recv_worker_fn, 4)
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@pytest.mark.skipif(torch.cuda.device_count() < 4,
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reason="Need at least 4 GPUs to run the test.")
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def test_pynccl_broadcast():
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distributed_run(broadcast_worker_fn, 4)
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@worker_fn_wrapper
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def broadcast_worker_fn():
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# Test broadcast for every root rank.
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# Essentially this is an all-gather operation.
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pynccl_comm = PyNcclCommunicator(get_world_group().cpu_group,
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device=get_world_group().device)
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recv_tensors = [
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torch.empty(16,
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1024,
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1024,
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dtype=torch.float32,
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device=pynccl_comm.device)
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for i in range(pynccl_comm.world_size)
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]
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recv_tensors[pynccl_comm.rank] = torch.ones(
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16, 1024, 1024, dtype=torch.float32,
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device=pynccl_comm.device) * pynccl_comm.rank
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for i in range(pynccl_comm.world_size):
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pynccl_comm.broadcast(recv_tensors[i], src=i)
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# the broadcast op might be launched in a different stream
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# need to synchronize to make sure the tensor is ready
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torch.cuda.synchronize()
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assert torch.all(recv_tensors[i] == i).cpu().item()
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def test_ncclGetUniqueId():
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lib = NCCLLibrary()
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unique_id = lib.ncclGetUniqueId()
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# `list(unique_id.internal)` is something like this:
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# [34, -16, 23, 83, 109, -19, 59, 95, 2, 0, -86, 55, 10, -128, 0, 29, 0,
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# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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# 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
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# as long as the function doesn't raise an exception, we're good
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assert unique_id is not None
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