def test_simple_conv2d_adjoint_weight(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
x_global_shape):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.conv_feature import DistributedFeatureConv2d
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
x_global_shape = np.asarray(x_global_shape)
layer = DistributedFeatureConv2d(P_x,
in_channels=x_global_shape[1],
out_channels=10,
kernel_size=[3, 3], bias=False)
x = zero_volume_tensor(x_global_shape[0])
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.randn(*x_local_shape)
x.requires_grad = True
y = layer(x)
dy = zero_volume_tensor(x_global_shape[0])
if P_x.active:
dy = torch.randn(*y.shape)
y.backward(dy)
W = zero_volume_tensor()
dW = zero_volume_tensor()
if P_x.active:
W = layer.weight.detach()
dW = layer.weight.grad.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, W, dW, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
def test_linear_adjoint_input(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, P_y_ranks, P_y_shape,
P_w_ranks, P_w_shape, x_global_shape,
y_global_shape):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.linear import DistributedLinear
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
P_w_base = P_world.create_partition_inclusive(P_w_ranks)
P_w = P_w_base.create_cartesian_topology_partition(P_w_shape)
x_global_shape = np.asarray(x_global_shape)
y_global_shape = np.asarray(y_global_shape)
layer = DistributedLinear(P_x,
P_y,
P_w,
x_global_shape[1],
y_global_shape[1],
bias=False)
x = zero_volume_tensor(x_global_shape[0])
if P_x.active:
x_local_shape = compute_subshape(P_x.shape, P_x.index, x_global_shape)
x = torch.Tensor(np.random.randn(*x_local_shape))
x.requires_grad = True
y = layer(x)
dy = zero_volume_tensor(x_global_shape[0])
if P_y.active:
dy = torch.Tensor(np.random.randn(*y.shape))
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
def test_transpose_adjoint(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
P_y_ranks, P_y_shape,
x_global_shape):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.transpose import DistributedTranspose
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
# The global tensor size is the same for x and y
layer = DistributedTranspose(P_x, P_y, preserve_batch=False)
# Forward Input
x = zero_volume_tensor()
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.Tensor(np.random.randn(*x_local_shape))
x.requires_grad = True
# Adjoint Input
dy = zero_volume_tensor()
if P_y.active:
y_local_shape = compute_subshape(P_y.shape,
P_y.index,
x_global_shape)
dy = torch.Tensor(np.random.randn(*y_local_shape))
# y = F @ x
y = layer(x)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
def test_broadcast_adjoint(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, P_y_ranks, P_y_shape,
x_global_shape, transpose_src):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.broadcast import Broadcast
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
# TODO #93: Change this to create a subtensor so we test when local tensors
# have different shape. Then, the output size will also be different, which
# we will have to get from `y` itself.
x_local_shape = np.asarray(x_global_shape)
layer = Broadcast(P_x,
P_y,
transpose_src=transpose_src,
preserve_batch=False)
x = zero_volume_tensor()
if P_x.active:
x = torch.Tensor(np.random.randn(*x_local_shape))
x.requires_grad = True
dy = zero_volume_tensor()
if P_y.active:
# Adjoint Input
dy = torch.Tensor(np.random.randn(*x_local_shape))
# y = F @ x
y = layer(x)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
def test_padnd_adjoint(barrier_fence_fixture,
comm_split_fixture,
x_local_shape,
padding):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.padnd import PadNd
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
x_local_shape = np.asarray(x_local_shape)
padding = np.asarray(padding)
padded_shape = [t + lpad + rpad for t, (lpad, rpad) in zip(x_local_shape, padding)]
layer = PadNd(padding, value=0)
x = torch.tensor(np.random.randn(*x_local_shape))
x.requires_grad = True
dy = torch.tensor(np.random.randn(*padded_shape))
y = layer(x)
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
def test_general_conv1d_adjoint_bias(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, P_y_ranks,
P_y_shape, P_w_ranks, P_w_shape,
x_global_shape):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.conv_general import DistributedGeneralConv1d
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
P_w_base = P_world.create_partition_inclusive(P_w_ranks)
P_w = P_w_base.create_cartesian_topology_partition(P_w_shape)
x_global_shape = np.asarray(x_global_shape)
layer = DistributedGeneralConv1d(P_x,
P_y,
P_w,
in_channels=x_global_shape[1],
out_channels=10,
kernel_size=[3],
bias=True)
x = zero_volume_tensor(x_global_shape[0])
if P_x.active:
x_local_shape = compute_subshape(P_x.shape, P_x.index, x_global_shape)
x = torch.zeros(*x_local_shape)
x.requires_grad = True
y = layer(x)
dy = zero_volume_tensor(x_global_shape[0])
if P_y.active:
dy = torch.randn(*y.shape)
y.backward(dy)
b = zero_volume_tensor()
db = zero_volume_tensor()
if P_w.active and layer.stores_bias:
b = layer.bias.detach()
db = layer.bias.grad.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, b, db, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
P_w_base.deactivate()
P_w.deactivate()
def test_linear_adjoint_bias(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, P_y_ranks, P_y_shape,
P_w_ranks, P_w_shape, x_global_shape,
y_global_shape):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.linear import DistributedLinear
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
P_w_base = P_world.create_partition_inclusive(P_w_ranks)
P_w = P_w_base.create_cartesian_topology_partition(P_w_shape)
x_global_shape = np.asarray(x_global_shape)
y_global_shape = np.asarray(y_global_shape)
layer = DistributedLinear(P_x,
P_y,
P_w,
x_global_shape[1],
y_global_shape[1],
bias=True)
x = zero_volume_tensor(x_global_shape[0])
if P_x.active:
x_local_shape = compute_subshape(P_x.shape, P_x.index, x_global_shape)
# For this test, we are only testing to see if the adjoint works
# correctly for the bias term. But the adjoint test only works on the
# Jacobian of the linear layer. The Jacobian block for b is 0 for x and
# W, so killing x makes the forward operator equal to its Jacobian and
# we can test to see that adjoint is computed correctly.
x = torch.zeros(*x_local_shape)
x.requires_grad = True
y = layer(x)
dy = zero_volume_tensor(x_global_shape[0])
if P_y.active:
dy = torch.Tensor(np.random.randn(*y.shape))
y.backward(dy)
b = zero_volume_tensor()
db = zero_volume_tensor()
if P_w.active and P_w.index[-1] == 0:
b = layer.sublinear.bias.detach()
db = layer.sublinear.bias.grad.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, b, db, y, dy)
def test_halo_exchange_adjoint(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
x_global_shape,
kernel_size, stride, padding, dilation,
MockKernelStyle):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.halo_exchange import HaloExchange
from distdl.nn.padnd import PadNd
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
x_global_shape = np.asarray(x_global_shape)
kernel_size = np.asarray(kernel_size)
stride = np.asarray(stride)
padding = np.asarray(padding)
dilation = np.asarray(dilation)
halo_shape = None
recv_buffer_shape = None
send_buffer_shape = None
if P_x.active:
mockup_layer = MockKernelStyle()
exchange_info = mockup_layer._compute_exchange_info(x_global_shape,
kernel_size,
stride,
padding,
dilation,
P_x.active,
P_x.shape,
P_x.index)
halo_shape = exchange_info[0]
recv_buffer_shape = exchange_info[1]
send_buffer_shape = exchange_info[2]
pad_layer = PadNd(halo_shape, value=0)
halo_layer = HaloExchange(P_x, halo_shape, recv_buffer_shape, send_buffer_shape)
x = zero_volume_tensor(x_global_shape[0])
if P_x.active:
x_local_shape = compute_subshape(P_x.shape,
P_x.index,
x_global_shape)
x = torch.tensor(np.random.randn(*x_local_shape))
x = pad_layer.forward(x)
x.requires_grad = True
dy = zero_volume_tensor(x_global_shape[0])
if P_x.active:
dy = torch.tensor(np.random.randn(*x.shape))
x_clone = x.clone()
dy_clone = dy.clone()
# x_clone is be modified in place by halo_layer, but we assign y to
# reference it for clarity
y = halo_layer(x_clone)
# dy_clone is modified in place by halo_layer-adjoint, but we assign dx to
# reference it for clarity
y.backward(dy_clone)
dx = dy_clone
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
def test_buffer_management_mixed_network(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
import torch
import distdl
from distdl.backends.mpi.buffer import MPIBufferManager
from distdl.backends.mpi.partition import MPIPartition
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
buffer_manager = MPIBufferManager()
P_world = MPIPartition(base_comm)
# Create the partitions
P_1_base = P_world.create_partition_inclusive([0])
P_1 = P_1_base.create_cartesian_topology_partition([1, 1, 1, 1])
P_22_base = P_world.create_partition_inclusive([0, 1, 2, 3])
P_22 = P_22_base.create_cartesian_topology_partition([1, 1, 2, 2])
tr1 = distdl.nn.DistributedTranspose(P_1,
P_22,
buffer_manager=buffer_manager)
c1 = distdl.nn.DistributedConv2d(P_22,
in_channels=1,
out_channels=5,
kernel_size=[3, 3],
padding=[1, 1],
bias=False,
buffer_manager=buffer_manager)
c2 = distdl.nn.DistributedConv2d(P_22,
in_channels=5,
out_channels=10,
kernel_size=[3, 3],
padding=[1, 1],
bias=False,
buffer_manager=buffer_manager)
c3 = distdl.nn.DistributedConv2d(P_22,
in_channels=10,
out_channels=20,
kernel_size=[3, 3],
padding=[1, 1],
bias=False,
buffer_manager=buffer_manager)
tr2 = distdl.nn.DistributedTranspose(P_22,
P_1,
buffer_manager=buffer_manager)
x = zero_volume_tensor(1)
if P_1.active:
x = torch.randn(1, 1, 5, 5)
x.requires_grad = True
# [[00 01 02 03 04] [[00 01 02] [[03 04]
# [10 11 12 13 14] [10 11 12] [13 14]]
# [20 21 22 23 24] to [20 21 22]] [[23 24]
# [30 31 32 33 34] [[30 31 32] [33 34]
# [40 41 42 43 44]] [40 41 42]] [43 44]]
x2 = tr1(x)
n_buffers_by_rank = (3, 1, 1, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [[00 01 02] [[03 04] [[00 01 02] [[03 04]
# [10 11 12] [13 14]] [10 11 12] [13 14]]
# [20 21 22]] [[23 24] to [20 21 22]] [[23 24]
# [[30 31 32] [33 34] [[30 31 32] [33 34]
# [40 41 42]] [43 44]] [40 41 42]] [43 44]]
x3 = c1(x2)
n_buffers_by_rank = (4, 4, 4, 4)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [[00 01 02] [[03 04] [[00 01 02] [[03 04]
# [10 11 12] [13 14]] [10 11 12] [13 14]]
# [20 21 22]] [[23 24] to [20 21 22]] [[23 24]
# [[30 31 32] [33 34] [[30 31 32] [33 34]
# [40 41 42]] [43 44]] [40 41 42]] [43 44]]
x4 = c2(x3)
n_buffers_by_rank = (4, 4, 4, 4)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [[00 01 02] [[03 04] [[00 01 02] [[03 04]
# [10 11 12] [13 14]] [10 11 12] [13 14]]
# [20 21 22]] [[23 24] to [20 21 22]] [[23 24]
# [[30 31 32] [33 34] [[30 31 32] [33 34]
# [40 41 42]] [43 44]] [40 41 42]] [43 44]]
x5 = c3(x4)
n_buffers_by_rank = (4, 4, 4, 4)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [[00 01 02] [[03 04] [[00 01 02 03 04]
# [10 11 12] [13 14]] [10 11 12 13 14]
# [20 21 22]] [[23 24] to [20 21 22 23 24]
# [[30 31 32] [33 34] [30 31 32 33 34]
# [40 41 42]] [43 44]] [40 41 42 43 44]]
y = tr2(x5)
n_buffers_by_rank = (4, 4, 4, 4)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
dy = zero_volume_tensor(1)
if P_1.active:
dy = torch.randn(1, 20, 5, 5)
dy.requires_grad = True
y.backward(dy)
dx = x.grad
# Through the backward call the buffer count do not change
n_buffers_by_rank = (4, 4, 4, 4)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# And adjointness is still preserved
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_1_base.deactivate()
P_1.deactivate()
P_22_base.deactivate()
P_22.deactivate()
def test_buffer_management_transpose_network(barrier_fence_fixture,
comm_split_fixture):
import numpy as np
import torch
import distdl
from distdl.backends.mpi.buffer import MPIBufferManager
from distdl.backends.mpi.partition import MPIPartition
from distdl.utilities.torch import zero_volume_tensor
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
buffer_manager = MPIBufferManager()
P_world = MPIPartition(base_comm)
# Create the partitions
P_1_base = P_world.create_partition_inclusive([0])
P_1 = P_1_base.create_cartesian_topology_partition([1, 1])
P_3_base = P_world.create_partition_inclusive([1, 2, 3])
P_3 = P_3_base.create_cartesian_topology_partition([1, 3])
P_4_base = P_world.create_partition_inclusive([0, 1, 2, 3])
P_4 = P_4_base.create_cartesian_topology_partition([1, 4])
tr1 = distdl.nn.DistributedTranspose(P_1,
P_4,
buffer_manager=buffer_manager)
tr2 = distdl.nn.DistributedTranspose(P_4,
P_4,
buffer_manager=buffer_manager)
tr3 = distdl.nn.DistributedTranspose(P_4,
P_3,
buffer_manager=buffer_manager)
tr4 = distdl.nn.DistributedTranspose(P_3,
P_1,
buffer_manager=buffer_manager)
x = zero_volume_tensor(1)
if P_1.active:
x = torch.randn(1, 10)
x.requires_grad = True
# [0 1 2 3 4 5 6 7 8 9] to
# [0 1 2] [3 4 5] [6 7] [8 9]
x2 = tr1(x)
n_buffers_by_rank = (3, 1, 1, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [0 1 2] [3 4 5] [6 7] [8 9] to
# [0 1 2] [3 4 5] [6 7] [8 9]
x3 = tr2(x2)
n_buffers_by_rank = (3, 1, 1, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [0 1 2] [3 4 5] [6 7] [8 9] to
# [] [0 1 2 3] [4 5 6] [7 8 9]
x4 = tr3(x3)
n_buffers_by_rank = (3, 2, 2, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# [] [0 1 2 3] [4 5 6] [7 8 9] to
# [0 1 2 3 4 5 6 7 8 9]
y = tr4(x4)
n_buffers_by_rank = (3, 2, 2, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
dy = zero_volume_tensor(1)
if P_1.active:
dy = torch.randn(1, 10)
dy.requires_grad = True
y.backward(dy)
dx = x.grad
# Through the backward call the buffer count do not change
n_buffers_by_rank = (3, 2, 2, 1)
assert len(buffer_manager.buffers_map[np.float32]) == n_buffers_by_rank[
P_world.rank]
# And adjointness is still preserved
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_1_base.deactivate()
P_1.deactivate()
P_3_base.deactivate()
P_3.deactivate()
P_4_base.deactivate()
P_4.deactivate()
def test_repartition_identity(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, x_global_shape, balanced):
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.repartition import Repartition
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y = P_x
# The global tensor size is the same for x and y
layer = Repartition(P_x, P_y, preserve_batch=False)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
if balanced:
x_local_shape = compute_subshape(P_x.shape, P_x.index,
x_global_shape)
else:
quotient = np.atleast_1d(x_global_shape) // np.atleast_1d(
P_x_shape)
remainder = np.atleast_1d(x_global_shape) % np.atleast_1d(
P_x_shape)
loc = np.where(P_x.index == 0)
x_local_shape = quotient.copy()
x_local_shape[loc] += remainder[loc]
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
# Adjoint Input
dy = zero_volume_tensor(device=device)
if P_y.active:
y_local_shape = compute_subshape(P_y.shape, P_y.index, x_global_shape)
dy = torch.randn(*y_local_shape, device=device)
# y = F @ x
y = layer(x)
# In the balanced case, this should be a true identity, so there should
# be no communication performed, just self-copies.
if balanced:
for sl, sz, p in layer.P_x_to_y_overlaps:
assert p == "self" or (sl, sz, p) == (None, None, None)
for sl, sz, p in layer.P_y_to_x_overlaps:
assert p == "self" or (sl, sz, p) == (None, None, None)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
def test_halo_exchange_adjoint(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, x_global_shape, dtype,
kernel_size, stride, padding, dilation,
MockKernelStyle):
import numpy as np
import torch
import torch.nn.functional as F
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.halo_exchange import HaloExchange
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import distdl_padding_to_torch_padding
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
x_global_shape = np.asarray(x_global_shape)
kernel_size = np.asarray(kernel_size)
stride = np.asarray(stride)
padding = np.asarray(padding)
dilation = np.asarray(dilation)
halo_shape = None
recv_buffer_shape = None
send_buffer_shape = None
if P_x.active:
mockup_layer = MockKernelStyle()
exchange_info = mockup_layer._compute_exchange_info(
x_global_shape, kernel_size, stride, padding, dilation, P_x.active,
P_x.shape, P_x.index)
halo_shape = exchange_info[0]
recv_buffer_shape = exchange_info[1]
send_buffer_shape = exchange_info[2]
halo_layer = HaloExchange(P_x, halo_shape, recv_buffer_shape,
send_buffer_shape)
halo_layer = halo_layer.to(device)
x = zero_volume_tensor(x_global_shape[0], device=device)
dy = zero_volume_tensor(x_global_shape[0], device=device)
if P_x.active:
x_local_shape = compute_subshape(P_x.shape, P_x.index, x_global_shape)
padding = distdl_padding_to_torch_padding(halo_shape)
x = torch.randn(*x_local_shape, device=device).to(dtype)
# Pad the input with the halo space. We are only testing the behavior of
# the halo exchange so the input must be padded before we can do anything.
x = F.pad(x, pad=padding, mode="constant", value=0)
# dy is also padded, but we wanted it to start with data inside it.
dy = torch.randn(*x.shape, device=device).to(dtype)
x.requires_grad = True
# Halo Exchange (both fwd and adj) is in-place. So, we copy the input
# data and save the original for the adjoint test. Because it is in-place,
# the clones themselves are modified. This also prevents issues with us
# in-place operations on leaf-nodes.
x_clone = x.clone()
dy_clone = dy.clone()
# x_clone is be modified in place by halo_layer, but we assign y to
# reference it for clarity. y and x_clone are the same object.
y = halo_layer(x_clone)
# dy_clone is modified in place by halo_layer-adjoint, but we assign dx to
# reference it for clarity. dx and dy_clone are the same object.
# dx is not in the grad field as you might expect because the operation is
# in-place.
y.backward(dy_clone)
dx = dy_clone
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
def test_transpose_adjoint(barrier_fence_fixture, comm_split_fixture,
P_x_ranks, P_x_shape, P_y_ranks, P_y_shape,
x_global_shape, balanced):
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.transpose import DistributedTranspose
from distdl.utilities.slicing import compute_subshape
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
P_y_base = P_world.create_partition_inclusive(P_y_ranks)
P_y = P_y_base.create_cartesian_topology_partition(P_y_shape)
# The global tensor size is the same for x and y
layer = DistributedTranspose(P_x, P_y, preserve_batch=False)
layer = layer.to(device)
# Forward Input
x = zero_volume_tensor(device=device)
if P_x.active:
if balanced:
x_local_shape = compute_subshape(P_x.shape, P_x.index,
x_global_shape)
else:
quotient = np.atleast_1d(x_global_shape) // np.atleast_1d(
P_x_shape)
remainder = np.atleast_1d(x_global_shape) % np.atleast_1d(
P_x_shape)
loc = np.where(P_x.index == 0)
x_local_shape = quotient.copy()
x_local_shape[loc] += remainder[loc]
x = torch.randn(*x_local_shape, device=device)
x.requires_grad = True
# Adjoint Input
dy = zero_volume_tensor(device=device)
if P_y.active:
y_local_shape = compute_subshape(P_y.shape, P_y.index, x_global_shape)
dy = torch.randn(*y_local_shape, device=device)
# y = F @ x
y = layer(x)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
P_y_base.deactivate()
P_y.deactivate()
def test_all_sum_reduce_adjoint(barrier_fence_fixture,
comm_split_fixture,
P_x_ranks, P_x_shape,
x_global_shape,
axes_reduce):
import numpy as np
import torch
from distdl.backends.mpi.partition import MPIPartition
from distdl.nn.all_sum_reduce import AllSumReduce
from distdl.utilities.torch import zero_volume_tensor
device = torch.device('cuda' if use_cuda else 'cpu')
# Isolate the minimum needed ranks
base_comm, active = comm_split_fixture
if not active:
return
P_world = MPIPartition(base_comm)
# Create the partitions
P_x_base = P_world.create_partition_inclusive(P_x_ranks)
P_x = P_x_base.create_cartesian_topology_partition(P_x_shape)
# have different shape. Then, the output size will also be different, which
# we will have to get from `y` itself.
x_local_shape = np.asarray(x_global_shape)
layer = AllSumReduce(P_x, axes_reduce)
layer = layer.to(device)
x = zero_volume_tensor(device=device)
if P_x.active:
x = 10*torch.ones(*x_local_shape, device=device)
x.requires_grad = True
dy = zero_volume_tensor(device=device)
if P_x.active:
# Adjoint Input
dy = 0.1*torch.ones(*x_local_shape, device=device)
# y = F @ x
y = layer(x)
# dx = F* @ dy
y.backward(dy)
dx = x.grad
x = x.detach()
dx = dx.detach()
dy = dy.detach()
y = y.detach()
reduced_entry_value = 1
for k in range(len(P_x_shape)):
if k in axes_reduce:
reduced_entry_value *= P_x_shape[k]
assert(torch.all(y == 10*reduced_entry_value))
assert(torch.all(dx == 0.1*reduced_entry_value))
check_adjoint_test_tight(P_world, x, dx, y, dy)
P_world.deactivate()
P_x_base.deactivate()
P_x.deactivate()
