def test_reduce_add_coalesced(self):
numel = 5
num_bytes = numel * 8
tensors = [
torch.randn(numel).long().cuda(),
torch.randn(numel).cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel).long().cuda(),
torch.randn(numel * 2).int().cuda(), # int is 2x shorter
torch.randn(numel).cuda(),
]
dup_tensors = [tensors, list(map(lambda t: t.cuda(1), tensors))]
r_tensors = list(map(comm.reduce_add, zip(*dup_tensors)))
for r, t in zip(r_tensors, tensors):
self.assertEqual(r.get_device(), t.get_device())
self.assertEqual(r, t * 2)
self.assertIsInstance(r, type(t))
rc_tensors = comm.reduce_add_coalesced(dup_tensors,
buffer_size=num_bytes * 5 // 2)
self.assertEqual(r_tensors, rc_tensors)
for r, rc in zip(r_tensors, rc_tensors):
self.assertEqual(rc.get_device(), r.get_device())
self.assertIsInstance(rc, type(r))
def backward(ctx, *inputs):
inputs = [i.data for i in inputs]
inputs = [inputs[i:i + ctx.num_inputs]
for i in range(0, len(inputs), ctx.num_inputs)]
results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
return (None,) + tuple([Variable(t) for tensors in outputs for t in tensors])
def collect_gradients(self): if self.ngradev > 1: # in case some parameters might not be used during the forward propagation on some GPUs: p.data.new_zeros(p.data.size()) if p.grad is None else p.grad instead of p.grad, but in most cases, this can warn you in case you miss the use of some parameters in the forward computation. grads = comm.reduce_add_coalesced([[p.grad for p in filter_para_grad(net.parameters())] for net in self.nets[:self.ngradev]], self.output_device)# if self.ngradev > 1 else [p.grad for p in filter_para_grad(self.nets[0].parameters())] for mp, grad in zip(filter_para_grad(self.module.parameters()), grads): mp.grad = grad
def hook(*unused):
# reduce gradients across devices on a single machine
if len(self.device_ids) > 1:
# collect gradients from all copies
all_grads = [[] for _ in range(len(self._module_copies))]
for dev_idx, module in enumerate(self._module_copies):
for p in module.parameters():
if not p.requires_grad or p.grad is None:
continue
all_grads[dev_idx].append(p.grad.data)
# reduce grads
reduced_grads = reduce_add_coalesced(
all_grads, self.output_device,
self.nccl_reduce_bucket_size)
# update grads with reduced grads
for grad, reduced in zip(all_grads[0], reduced_grads):
grad.copy_(reduced)
# clear the gradients and parameters across all replicas
for module in self._module_copies[1:]:
for param in module.parameters():
if param.requires_grad:
param.grad = None
param.data.set_()
def collect_gradients(self):
grads = comm.reduce_add_coalesced([[
p.data.new_zeros(p.data.size()) if p.grad is None else p.grad
for p in net.parameters()
] for net in self.nets], self.output_device)
for mp, grad in zip(self.module.parameters(), grads):
mp.grad = grad
def forward(ctx, destination, num_inputs, *grads):
ctx.target_gpus = [
grads[i].get_device() for i in range(0, len(grads), num_inputs)
]
grads = [
grads[i:i + num_inputs] for i in range(0, len(grads), num_inputs)
]
return comm.reduce_add_coalesced(grads, destination)
def forward(ctx, num_inputs, *inputs):
ctx.num_inputs = num_inputs
ctx.target_gpus = [inputs[i].get_device() for i in range(0, len(inputs), num_inputs)]
inputs = [inputs[i:i + num_inputs] for i in range(0, len(inputs), num_inputs)]
# sort before reduce sum
inputs = sorted(inputs, key=lambda i: i[0].get_device())
results = comm.reduce_add_coalesced(inputs, ctx.target_gpus[0])
outputs = comm.broadcast_coalesced(results, ctx.target_gpus)
return tuple([t for tensors in outputs for t in tensors])
def collect_gradients_func(self, func):
if self.ngradev > 1:
grads = comm.reduce_add_coalesced(
[[p.grad for p in filter_para_grad(func(net).parameters())]
for net in self.nets[:self.ngradev]], self.output_device)
for mp, grad in zip(
filter_para_grad(func(self.module).parameters()), grads):
mp.grad = grad
def _test_reduce_add_coalesced(self, tensors, buffer_size):
dup_tensors = [tensors, list(map(lambda t: t.cuda(1), tensors))]
r_tensors = list(map(comm.reduce_add, zip(*dup_tensors)))
for r, t in zip(r_tensors, tensors):
self.assertEqual(r.get_device(), t.get_device())
self.assertEqual(r, t * 2)
self.assertIsInstance(r, type(t))
rc_tensors = comm.reduce_add_coalesced(dup_tensors, buffer_size=buffer_size)
self.assertEqual(r_tensors, rc_tensors)
for r, rc in zip(r_tensors, rc_tensors):
self.assertEqual(rc.get_device(), r.get_device())
self.assertIsInstance(rc, type(r))
def hook(*unused):
# reduce gradients across devices on a single machine
if len(self.device_ids) > 1:
# collect gradients from all copies
all_grads = [[] for _ in range(len(self._module_copies))]
for dev_idx, module in enumerate(self._module_copies):
for p in module.parameters():
if not p.requires_grad or p.grad is None:
continue
all_grads[dev_idx].append(p.grad.data)
# reduce grads
reduced_grads = reduce_add_coalesced(
all_grads, self.output_device,
self.nccl_reduce_bucket_size)
# update grads with reduced grads
for grad, reduced in zip(all_grads[0], reduced_grads):
grad.copy_(reduced)
# clear the gradients and parameters across all replicas
for module in self._module_copies[1:]:
for param in module.parameters():
if param.requires_grad:
param.grad = None
with torch.no_grad():
param.set_()
if self.nprocs_per_node > 1:
grads = []
for p in self.module.parameters():
if not p.requires_grad or p.grad is None:
continue
p.grad.data.div_(
self.nprocs_per_node_device.type(p.grad.data.dtype))
grads.append(p.grad.data)
communication_op = functools.partial(
dist.all_reduce, group=self.local_node_group)
communicate(grads, communication_op)
# convert model back to ps-numerator
self.ps_numerator()
def backward(self):
group_params = [[(n, m[n]) for n, p in self.param_group
if m[n].grad is not None] for m in self.named_params]
grad_params = [g for g in group_params if len(g) > 0]
assert all([len(g) == len(grad_params[0])
for g in grad_params]), [len(g) for g in grad_params]
grad = [[p.grad for n, p in g] for g in grad_params]
reduced_grad = reduce_add_coalesced(grad, self.device)
grads = dict([(n, g)
for ((n, p), g) in zip(grad_params[0], reduced_grad)])
l2norm = 0
for n, p in self.param_group:
if n in grads:
p.grad = grads[n].float(
) if grads[n].dtype == torch.half else grads[n]
l2norm += p.grad.norm().item()**2
else:
assert p.grad is None, n
return l2norm
def backward(self, *grad_outputs):
grad_outputs = [grad_outputs[i:i + self.num_inputs]
for i in range(0, len(grad_outputs), self.num_inputs)]
return comm.reduce_add_coalesced(grad_outputs, self.input_device)
def forward(ctx, destination, num_inputs, *grads):
ctx.target_gpus = [grads[i].get_device() for i in range(0, len(grads), num_inputs)]
grads = [grads[i:i + num_inputs]
for i in range(0, len(grads), num_inputs)]
return comm.reduce_add_coalesced(grads, destination)
def collect_gradients(self):
if self.optm_splt is not None:
if self.is_contiguous_parameters:
for i, (
net,
device,
) in enumerate(zip(self.nets, self.device_ids)):
_dev_grads = [[
para.grad
for para in get_contiguous_parameters_m(_net, index=i)
] for _net in self.nets[:self.ngradev]]
if i > 0:
_dev_grads.insert(
0,
_dev_grads.pop(i) if i < self.ngradev else [
para.grad for para in
get_contiguous_parameters_m(net, index=i)
])
_dev_grads = comm.reduce_add_coalesced(_dev_grads, device)
for mp, grad in zip(
get_contiguous_parameters_m(net, index=i),
_dev_grads):
mp.grad.copy_(grad)
else:
grads = [[
para.grad for para in filter_para_grad(net.parameters())
] for net in self.nets[:self.ngradev]]
for i, (
net,
device,
(
lind,
rind,
),
) in enumerate(zip(self.nets, self.device_ids,
self.optm_splt)):
_dev_grads = [gradu[lind:rind] for gradu in grads]
if i > 0:
_dev_grads.insert(
0,
_dev_grads.pop(i) if i < self.ngradev else [
_pg.new_zeros(_pg.size(), device=device)
for _pg in _dev_grads[0]
])
_dev_grads = comm.reduce_add_coalesced(_dev_grads, device)
for mp, grad in zip(
range_parameter_iter(net,
lind,
rind,
func=filter_para_grad_iter),
_dev_grads):
mp.grad = grad
elif self.ngradev > 1:
if self.is_contiguous_parameters:
grads = comm.reduce_add_coalesced([[
para.grad for para in get_all_contiguous_parameters_m(net)
] for net in self.nets[:self.ngradev]], self.output_device)
for mp, grad in zip(
get_all_contiguous_parameters_m(self.module), grads):
mp.grad.copy_(grad)
else:
# in case some parameters might not be used during the forward propagation on some GPUs: p.data.new_zeros(p.data.size()) if p.grad is None else p.grad instead of p.grad, but in most cases, this can warn you in case you miss the use of some parameters in the forward computation.
grads = comm.reduce_add_coalesced(
[[
para.grad
for para in filter_para_grad(net.parameters())
] for net in self.nets[:self.ngradev]], self.output_device
) # if self.ngradev > 1 else [p.grad for p in filter_para_grad(self.nets[0].parameters())]
for mp, grad in zip(filter_para_grad(self.module.parameters()),
grads):
mp.grad = grad
def backward(self, *grad_outputs):
grad_outputs = [grad_outputs[i:i + self.num_inputs]
for i in range(0, len(grad_outputs), self.num_inputs)]
return comm.reduce_add_coalesced(grad_outputs, self.input_device)
