pytorch/test/distributed/test_c10d_spawn_gloo.py

import copy
import os
import sys
import tempfile

import test_c10d_spawn
import torch
import torch.distributed as c10d
import torch.nn as nn
from test_c10d_spawn import _torch_dist_nn_available
from torch.testing._internal.common_cuda import TEST_CUDA, TEST_MULTIGPU
from torch.testing._internal.common_distributed import requires_gloo, \
    create_device, MultiProcessTestCase, skip_if_lt_x_gpu
from torch.testing._internal.common_utils import TestCase, run_tests, sandcastle_skip_if, TEST_WITH_DEV_DBG_ASAN

# Fails on Python-3.9, see https://github.com/pytorch/pytorch/issues/51619
if sys.version_info < (3, 9):
    class ProcessGroupShareTensorTest(test_c10d_spawn.AbstractProcessGroupShareTensorTest, TestCase):

        @classmethod
        def opts(cls, threads=2):
            opts = c10d.ProcessGroupGloo._Options()
            opts._timeout = 5.0
            opts._devices = [create_device(interface='lo')]
            opts._threads = threads
            return opts

        @classmethod
        def _init_pg_gloo(cls, rank, filename, world_size):
            store = c10d.FileStore(filename, world_size)
            return c10d.ProcessGroupGloo(
                store, rank, world_size, ProcessGroupShareTensorTest.opts())

        @sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
        def test_shared_broadcast_gloo(self):
            self._test_multiprocess(
                ProcessGroupShareTensorTest._test_broadcast_process,
                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
                ProcessGroupShareTensorTest._init_pg_gloo,
                1)

        @sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
        def test_shared_allreduce_gloo(self):
            self._test_multiprocess(
                ProcessGroupShareTensorTest._test_allreduce_process,
                [torch.ones(2, 2).to(i) for i in range(self.world_size)],
                ProcessGroupShareTensorTest._init_pg_gloo,
                1)

        @sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
        def test_shared_allgather_gloo(self):
            self._test_multiprocess(
                ProcessGroupShareTensorTest._test_allgather_process,
                [torch.ones(2, 2).to(i) * i for i in range(self.world_size)],
                ProcessGroupShareTensorTest._init_pg_gloo,
                self.world_size)

        @classmethod
        def _test_allgather_chunk_process(
                cls, rank, filename, shared_tensor, world_size, init_pg, c2p, p2c):
            pg = init_pg(rank, filename, world_size)
            chunks = torch.chunk(shared_tensor, world_size, dim=0)
            x = chunks[rank]
            ys = [torch.zeros_like(x) for _ in range(world_size)]
            pg.allgather(ys, x).wait()
            c2p.put((rank, chunks[0].to("cpu"), ys[0].to("cpu")))
            c2p.put((rank, chunks[1].to("cpu"), ys[1].to("cpu")))
            p2c.get()

        @sandcastle_skip_if(not TEST_MULTIGPU, "At least 2 CUDA GPUS needed")
        def test_shared_allgather_chunk_gloo(self):
            self._test_multiprocess(
                ProcessGroupShareTensorTest._test_allgather_chunk_process,
                torch.tensor(range(4)).reshape(2, 2),
                ProcessGroupShareTensorTest._init_pg_gloo,
                self.world_size)


class DistributedDataParallelSingleProcessTest(TestCase):
    def setUp(self):
        self.rank = 0
        self.world_size = 1
        self.file = tempfile.NamedTemporaryFile(delete=False)  # noqa: P201

    def tearDown(self):
        try:
            os.remove(self.file.name)
        except OSError:
            pass

    def _test_base(self, net, inp, check_allclose=True):
        store = c10d.FileStore(self.file.name, self.world_size)
        process_group = c10d.ProcessGroupGloo(store, self.rank, self.world_size)
        if inp[0].is_cuda:
            device_ids = [torch.cuda.current_device()]
        else:
            device_ids = None

        ddp = nn.parallel.DistributedDataParallel(
            copy.deepcopy(net),
            device_ids=device_ids,
            process_group=process_group
        )

        net_opt = torch.optim.Adam(net.parameters(), lr=0.001)
        ddp_opt = torch.optim.Adam(ddp.parameters(), lr=0.001)

        for i, j in zip(ddp.parameters(), net.parameters()):
            self.assertTrue(i.allclose(j))

        for _ in range(10):
            net_out = net(*inp)
            ddp_out = ddp(*inp)

            net_out.sum().backward()
            ddp_out.sum().backward()

            net_opt.step()
            ddp_opt.step()

        if check_allclose:
            for i, j in zip(ddp.parameters(), net.parameters()):
                self.assertTrue(i.allclose(j))

    @requires_gloo()
    def test_cpu(self):
        self._test_base(nn.Linear(2, 2), [torch.randn(30, 2)])

    @requires_gloo()
    @sandcastle_skip_if(not TEST_CUDA, "At least 1 CUDA GPUS needed")
    def test_cuda(self):
        self._test_base(nn.Linear(2, 2).to(0), [torch.randn(30, 2).to(0)])

    @requires_gloo()
    @sandcastle_skip_if(not TEST_CUDA, "At least 1 CUDA GPUS needed")
    def test_rnn(self):
        # This test is inspired by the bug reported in
        # https://github.com/pytorch/pytorch/issues/36268
        BATCH_SIZE = 12  # Divisible by 2, 3, 4
        INPUT_DIM = 256
        OUTPUT_DIM = 256
        HIDDEN_DIM = 256
        N_LAYERS = 3
        SEQ_LEN = 100

        class Net(nn.Module):
            def __init__(self, input_dim, hidden_dim, output_dim, hidden_layers):
                super(Net, self).__init__()
                self.input_dim = input_dim
                self.hidden_dim = hidden_dim
                self.output_dim = output_dim
                self.hidden_layers = hidden_layers

                self.lstm = nn.LSTM(input_dim, hidden_dim, hidden_layers, batch_first=True)
                self.h2o = nn.Linear(hidden_dim, output_dim)

            def forward(self, x, y):
                self.lstm.flatten_parameters()
                h_t, _ = self.lstm(x)
                output = self.h2o(h_t)
                loss = nn.functional.mse_loss(output, y)
                return loss

        net = Net(INPUT_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS).to(0)
        inp = [
            torch.randn((BATCH_SIZE, SEQ_LEN, INPUT_DIM)).to(0),
            torch.rand((BATCH_SIZE, SEQ_LEN, OUTPUT_DIM)).to(0)
        ]

        # Not checking result allclose as the parameter inconsistency exist
        # prior to this change. See #37079
        self._test_base(net, inp, check_allclose=False)


# Skip dev-asan as torch + multiprocessing spawn have known issues
if not TEST_WITH_DEV_DBG_ASAN:
    class TestDistributedNNFunctions(MultiProcessTestCase):
        def setUp(self):
            super(TestDistributedNNFunctions, self).setUp()
            self._spawn_processes()

        def tearDown(self):
            super(TestDistributedNNFunctions, self).tearDown()
            try:
                os.remove(self.file_name)
            except OSError:
                pass

        @property
        def op_timeout_sec(self):
            return 1

        @property
        def world_size(self):
            return 2

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_broadcast(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x = torch.ones(5, 5, device=device) + self.rank
            x.requires_grad = True
            y = torch.distributed.nn.broadcast(x, 1)
            self.assertEqual(y, 1 + torch.ones(5, 5))
            z = y.sin().sum()
            z.backward()
            # We can't check the gradient of communications numerically so we have to do some calculations
            if self.rank == 1:
                self.assertEqual(x.grad, 2 * torch.cos(x))
            elif self.rank == 0:
                self.assertEqual(x.grad, torch.zeros(5, 5, device=device))

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_gather(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x = torch.ones(5, 5, device=device) + self.rank
            x.requires_grad = True
            tensors = torch.distributed.nn.gather(x, 1)
            if self.rank == 1:
                for i, t in enumerate(tensors):
                    self.assertEqual(t, torch.ones(5, 5, device=device) + i)
            elif self.rank == 0:
                for i, t in enumerate(tensors):
                    zeros = torch.zeros(5, 5, device=device)
                    self.assertEqual(t, zeros)
            y = torch.sum(torch.stack(tensors), axis=0)
            z = y.sin().sum()
            z.backward()

            # Test gradient
            x_s = 3 * torch.ones(5, 5, device=device)
            self.assertEqual(x.grad, x_s.cos())

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_scatter(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x0 = torch.ones(5, 5, device=device)
            x1 = torch.ones(5, 5, device=device) + 1
            x0.requires_grad = True
            x1.requires_grad = True

            y = torch.distributed.nn.scatter([x0, x1], 1)
            if self.rank == 1:
                self.assertEqual(y, 1 + torch.ones(5, 5, device=device))
            elif self.rank == 0:
                self.assertEqual(y, torch.ones(5, 5, device=device))
            z = y.sin().sum()
            z.backward()

            # Test gradient
            if self.rank == 1:
                x0_s = torch.ones(5, 5, device=device).cos()
                x1_s = (2 * torch.ones(5, 5, device=device)).cos()
                self.assertEqual(x0.grad, x0_s)
                self.assertEqual(x1.grad, x1_s)
            if self.rank == 0:
                self.assertEqual(x0.grad, torch.zeros(5, 5, device=device))

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_reduce(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x = torch.ones(5, 5, device=device) + self.rank
            x.requires_grad = True
            y = torch.distributed.nn.reduce(x, 1, op=c10d.ReduceOp.SUM)

            if self.rank == 1:
                self.assertEqual(y, 3 * torch.ones(5, 5, device=device))

            z = y.sin().sum()
            z.backward()
            # Gradients are broadcasted to both ranks
            x_g = (3 * torch.ones(5, 5, device=device)).cos()
            self.assertEqual(x.grad, x_g)

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_allreduce(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x = torch.ones(5, 5, device=device) + self.rank
            x.requires_grad = True
            y = torch.distributed.nn.all_reduce(x, op=c10d.ReduceOp.SUM)

            self.assertEqual(y, 3 * torch.ones(5, 5, device=device))

            z = y.sin().sum()
            z.backward()
            x_g = 2 * (3 * torch.ones(5, 5, device=device)).cos()
            self.assertEqual(x.grad, x_g)

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_all_gather(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x = torch.ones(5, 5, device=device) + self.rank
            x.requires_grad = True
            tensors = torch.distributed.nn.all_gather(x)
            for i, t in enumerate(tensors):
                self.assertEqual(t, torch.ones(5, 5, device=device) + i)
            y = torch.sum(torch.stack(tensors), axis=0)
            z = y.sin().sum()
            z.backward()

            x_s = 2 * (3 * torch.ones(5, 5, device=device)).cos()
            self.assertEqual(x.grad, x_s)

        @requires_gloo()
        @skip_if_lt_x_gpu(2)
        @sandcastle_skip_if(not _torch_dist_nn_available, "torch.distributed.nn is not available")
        def test_all_to_all(self):
            store = c10d.FileStore(self.file_name, self.world_size)
            # This is required because these functions calls directly to the .dist and needs
            # the world to be initialized
            c10d.init_process_group(store=store, rank=self.rank, world_size=self.world_size, backend='gloo')
            device = torch.device(f"cuda:{self.rank}")
            x0 = torch.ones(5, 5, device=device) + 2 * self.rank
            x1 = torch.ones(5, 5, device=device) + 2 * self.rank
            x0.requires_grad = True
            x1.requires_grad = True
            tensors = torch.distributed.nn.all_to_all([x0, x1])
            for i, t in enumerate(tensors):
                self.assertEqual(t, torch.ones(5, 5, device=device) + 2 * i)
            y = torch.sum(torch.stack(tensors), axis=0)
            z = y.sin().sum()
            z.backward()
            x_s = (4 * torch.ones(5, 5, device=device)).cos()
            self.assertEqual(x0.grad, x_s)
            self.assertEqual(x1.grad, x_s)


if __name__ == '__main__':
    run_tests()