def test_multi_gpu():
from renom.cuda import cuGetDeviceCount
class NN2(rm.Model):
def __init__(self):
super(NN2, self).__init__()
self.layer1 = rm.Dense(output_size=2)
self.layer2 = rm.Dense(output_size=2)
def forward(self, x):
return self.layer2(rm.relu(self.layer1(x)))
def weight_initiallize(self, input_size):
self.layer1.weight_initiallize(input_size)
self.layer2.weight_initiallize(input_size)
nn = NN2()
nn.set_gpu(0)
nn.weight_initiallize((2, ))
nn2 = NN2()
nn2.set_gpu(cuGetDeviceCount() - 1)
for i in range(2):
nn2.copy_params(nn)
x = np.random.rand(100, 2)
with nn.train():
ret1 = nn(x[:50])
with use_device(nn.device_id):
loss1 = rm.softmax_cross_entropy(ret1, np.random.rand(50, 2))
with nn2.train():
ret2 = nn2(x[50:])
with use_device(nn2.device_id):
loss2 = rm.softmax_cross_entropy(ret2, np.random.rand(50, 2))
nn.sync()
nn2.sync()
grad1 = loss1.grad()
with use_device(nn2.device_id):
grad2 = loss2.grad()
grad2.get(nn2.layer1.params.w)
org_l1_w = grad1.get(nn.layer1.params.w)
nn.join_grads(grad1, [(nn2, grad2)])
assert np.allclose(grad1.get(nn.layer1.params.w),
org_l1_w + grad2.get(nn2.layer1.params.w).copy())
grad1.update(models=[nn])
def __itruediv__(self, other):
with use_device(self.device_id):
assert getattr(self, "shape", (1,)) == getattr(self, "shape", (1,))
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
cudiv(self, other, ret)
return ret
def __call__(self, x, *args, **kwargs):
with use_device(self._device_id):
if not self.params:
assert len(
x.shape) > 1, "Input must be at least of 2 dimensions."
self.weight_initiallize(x.shape[1:])
return super(Parametrized, self).__call__(x, *args, **kwargs)
def __add__(self, other):
with use_device(self.device_id):
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
# Only data type float32 is acceptable.
cuadd(self, other, ret)
return ret
def join_grads(self, grads, others):
"""Merge gradients of other models.
Others is a list of tuple of (model, grads) to be merged.
Models listed in the others should have same structure with self."""
values = {
name: params
for name, params, attrs in self.flatten_values()
}
for model, _grads in others:
o = model._get_grads(_grads)
for (name, attrname), diff in o.items():
obj = values[name][attrname]
curdiff = grads.get(obj, None)
if curdiff is not None:
if not isinstance(curdiff, Node):
curdiff = Node(curdiff)
if not isinstance(diff, Node):
diff = Node(diff)
with use_device(curdiff.device_id):
if GPUValue is not None and diff.device_id != curdiff.device_id:
g = GPUValue(shape=diff.shape)
g.copy_from(diff.get_gpu())
diff = Node(g)
newdiff = curdiff + diff
grads.set(obj, newdiff)
def copy(self):
if cuGetDevice() == self.device_id:
ret = GPUValue(shape=self.shape)
self._ptr.memcpyD2D(ret._ptr, self.nbytes)
else:
with use_device(self.device_id):
arr = self.new_array()
ret = GPUValue(arr)
return ret
def _oper_pow(self, other):
if not isinstance(self, GPUValue):
return other.__rpow__(self, modulo)
with use_device(self.device_id):
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
cupow(self, other, ret)
return ret
def __call__(self):
set_cuda_active(True)
with self.gpu_resource:
self._gpu = self.gpus.pop()
try:
with use_device(self._gpu):
return self._exec()
finally:
self.gpus.add(self._gpu)
def __truediv__(self, other):
if not isinstance(self, GPUValue):
return other.__rtruediv__(self)
with use_device(self.device_id):
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
cudiv(self, other, ret)
return ret
def sync(self):
if is_cuda_active():
done = set()
for m in self.iter_models():
device_id = m._device_id
if device_id not in done:
done.add(device_id)
with use_device(m._device_id):
renom.cuda.cuDeviceSynchronize()
def run(self, f, *args, **kwargs):
with self.gpu_resource:
self.active_gpu.id = self.gpus.pop()
try:
set_cuda_active(True)
with use_device(self.active_gpu.id):
return f(*args, **kwargs)
finally:
self.gpus.add(self.active_gpu.id)
release_mem_pool()
def __getitem__(self, indexes):
with use_device(self.device_id):
slices, result_shapes, dest_shapes = build_shapes(self, indexes)
dest_size = calc_int_prod(dest_shapes)
ret = cu_get_item(self, self.size, dest_size, slices)
ret.shape = tuple(result_shapes)
return ret
def T(self):
with use_device(self.device_id):
n = len(self.shape)
assert n < 3
clone = self.zeros_like_me()
if n == 2:
new_shape = list(clone.shape)
with cublas.cublas_handler() as cublas_handle:
cublas.cublas_transpose(cublas_handle, self, clone)
new_shape[0] = clone.shape[1]
new_shape[1] = clone.shape[0]
clone.shape = tuple(new_shape)
return clone
def to_gpu(self, value):
if value.dtype is not self.dtype:
value = value.astype(self.dtype)
assert value.shape == self.shape, "{} {}".format(value.shape, self.shape)
if not self._ptr:
self.nbytes = value.nbytes
self.alloc()
# todo: value.flatten() copies buffer
with use_device(self.device_id):
self._ptr.memcpyH2D(value.ravel(), value.nbytes)
def test_copy_from_another_gpu():
set_cuda_active(True)
src = Variable(rand((100, )))
src.to_gpu()
with use_device(1):
dest = Variable(rand((100, )))
dest.to_gpu()
dest.copy_from(src)
close(src, dest)
close(src._gpu.new_array(), dest._gpu.new_array())
def __call__(self, x, *args, **kwargs):
with use_device(self._device_id):
if self._model_hook:
x, args, kwargs = self._model_hook.call_enter(self, x, args, kwargs)
if not self._model_hook:
ret = self.forward(x, *args, **kwargs)
else:
ret = self._model_hook.on_forward(self, self.forward, x, args, kwargs)
if self._model_hook:
ret = self._model_hook.call_leave(self, ret, x, args, kwargs)
return ret
def copy_params(self, model):
value_list = model.flatten_values()
with use_device(self._device_id):
for names, values, attrs in value_list:
layer = self
for name in names[1:]:
layer = getattr(layer, name)
for k, v in values.items():
if k in layer.params:
layer.params[k].copy_from(v)
else:
layer.params[k] = v.copy()
layer.params[k]._auto_update = v._auto_update
def __setitem__(self, indexes, value):
with use_device(self.device_id):
value = get_gpu(value)
slices, result_shapes, dest_shapes = build_shapes(self, indexes)
if calc_int_prod(result_shapes) == 0:
return
dest_strides = calc_strides(dest_shapes)
mask, broadcasted = _build_broadcast_mask(dest_shapes, value.shape)
broadcasted_strides = calc_strides(broadcasted)
broadcasted_strides = [m * b for m, b in zip(mask, broadcasted_strides)]
valuesize = calc_int_prod(dest_shapes)
cu_set_item(value, valuesize, self, slices, dest_strides, broadcasted_strides)
def __idiv__(self, other):
with use_device(self.device_id):
return self.__itruediv__(other)
def train(self, train_distributor, test_distributor=None):
"""Train method.
This method executes train loop.
If test_distributor is given, validation loss will be calculated.
Args:
train_distributor (Distributor): Distributor for yielding train data.
test_distributor (Distributor): Distributor for yielding test data.
"""
self.epoch = 0
self.train_distributor = train_distributor
self.test_distributor = test_distributor
self.on_event('start')
self.train_loss_list = []
self.test_loss_list = []
models = [self.model]
if self.num_gpu > 1:
models.extend(
[self.model.__class__() for _ in range(self.num_gpu - 1)])
for n in range(self.num_gpu):
models[n].set_gpu(n)
while self.epoch < self.num_epoch:
self.on_event('start_epoch')
self.nth = 0
self.avg_train_loss = 0
for iteration, (data, target) in enumerate(
self.train_distributor.batch(self.batch_size,
self.shuffle)):
datalen = len(data) // len(models)
self.data = [
data[i:i + datalen]
for i in range(0, datalen * len(models), datalen)
]
if is_cuda_active():
self.data = [Node(d) for d in self.data]
for n, d in enumerate(self.data):
with use_device(n):
d.to_gpu()
targetlen = len(target) // len(models)
self.targets = [
target[i:i + targetlen]
for i in range(0, targetlen * len(models), targetlen)
]
if is_cuda_active():
self.targets = [Node(d) for d in self.targets]
for n, d in enumerate(self.targets):
with use_device(n):
d.to_gpu()
for gpu in range(1, self.num_gpu):
models[gpu].copy_params(models[0])
for gpu in range(0, self.num_gpu):
models[gpu].set_models(inference=False)
self.on_event('forward')
self.outputs = []
for gpu in range(self.num_gpu):
model = models[gpu]
with model.train():
self.outputs.append(model(self.data[gpu]))
self.on_event('loss')
self.losses = []
for gpu in range(self.num_gpu):
model = models[gpu]
with use_device(gpu):
self.losses.append(
self.loss_func(self.outputs[gpu],
self.targets[gpu]))
self.avg_train_loss += (self.losses[0] -
self.avg_train_loss) / (iteration + 1)
self.on_event('backward')
self.grads = []
for gpu in range(self.num_gpu):
model = models[gpu]
with use_device(gpu):
self.grads.append(self.losses[gpu].grad())
self.on_event('grad')
if self.num_gpu > 1:
models[0].join_grads(self.grads[0],
zip(models[1:], self.grads[1:]))
self.grads[0].update(self.optimizer)
self.on_event('updated')
self.nth += 1
self.on_event('end_epoch')
self.epoch += 1
# release objects
self.data = self.target = None
self.outputs = self.losses = self.grads = None
self.avg_train_loss = None
def __sub__(self, other):
with use_device(self.device_id):
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
cusub(self, other, ret)
return ret
def __call__(self, x, *args, **kwargs):
with use_device(self._device_id):
if not self.params:
self.weight_initiallize(x.shape[1:])
return super(Parametrized, self).__call__(x, *args, **kwargs)
def __call__(self, *args, **kwargs):
with use_device(self._device_id):
return self.forward(*args, **kwargs)
def __rpow__(self, other, modulo):
with use_device(self.device_id):
new_shape = calc_broadcast_shape(self, other)
ret = GPUValue(shape=new_shape)
curpow(self, other, ret)
return ret
def __div__(self, other):
if not isinstance(self, GPUValue):
return other.__rdiv__(self)
with use_device(self.device_id):
return self.__truediv__(other)
def __isub__(self, other):
with use_device(self.device_id):
assert getattr(self, "shape", (1,)) == getattr(self, "shape", (1,))
cublas.cublas_axpy(-get_gpu(other), get_gpu(self))
return self
def __rmul__(self, other):
with use_device(self.device_id):
return self.__mul__(other)
def __call__(self, x, *args, **kwargs):
with use_device(self._device_id):
x = self.mark_enter(x)
ret = self.forward(x, *args, **kwargs)
return self.mark_leave(ret)
def __call__(self, x):
with use_device(self._device_id):
return self.forward(x)
