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未验证 提交 6d9e77b9 编写于 作者: L LielinJiang 提交者： GitHub
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Merge pull request #16 from LielinJiang/test-prepare-context 

remove init_context
上级 b1862cf7 91bee2ad
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Showing with 170 addition and 212 deletion
callbacks.py
浏览文件 @ 6d9e77b9
......@@ -16,7 +16,7 @@ import six
import copy
from progressbar import ProgressBar
from distributed import get_local_rank
from paddle.fluid.dygraph.parallel import ParallelEnv
def config_callbacks(callbacks=None,
......@@ -195,7 +195,7 @@ class ProgBarLogger(Callback):
self.steps = self.params['steps']
self.epoch = epoch
self.train_step = 0
if self.verbose and self.epochs and get_local_rank() == 0:
if self.verbose and self.epochs and ParallelEnv().local_rank == 0:
print('Epoch %d/%d' % (epoch + 1, self.epochs))
self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose)
......@@ -213,8 +213,8 @@ class ProgBarLogger(Callback):
logs = logs or {}
self.train_step += 1
if self.train_step % self.log_freq == 0 and self.verbose and get_local_rank(
) == 0:
if self.train_step % self.log_freq == 0 and self.verbose and ParallelEnv(
).local_rank == 0:
# if steps is not None, last step will update in on_epoch_end
if self.steps and self.train_step < self.steps:
self._updates(logs, 'train')
......@@ -223,7 +223,7 @@ class ProgBarLogger(Callback):
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
if self.verbose and get_local_rank() == 0:
if self.verbose and ParallelEnv().local_rank == 0:
self._updates(logs, 'train')
def on_eval_begin(self, logs=None):
......@@ -233,7 +233,7 @@ class ProgBarLogger(Callback):
self.evaled_samples = 0
self.eval_progbar = ProgressBar(
num=self.eval_steps, verbose=self.verbose)
if get_local_rank() == 0:
if ParallelEnv().local_rank == 0:
print('Eval begin...')
def on_eval_batch_end(self, step, logs=None):
......@@ -244,7 +244,7 @@ class ProgBarLogger(Callback):
def on_eval_end(self, logs=None):
logs = logs or {}
if self.verbose and get_local_rank() == 0:
if self.verbose and ParallelEnv().local_rank == 0:
self._updates(logs, 'eval')
print('Eval samples: %d' % (self.evaled_samples))
......@@ -258,7 +258,7 @@ class ModelCheckpoint(Callback):
self.epoch = epoch
def _is_save(self):
return self.model and self.save_dir and get_local_rank() == 0
return self.model and self.save_dir and ParallelEnv().local_rank == 0
def on_epoch_end(self, epoch, logs=None):
if self._is_save() and self.epoch % self.save_freq == 0:
......
distributed.py
浏览文件 @ 6d9e77b9
......@@ -13,30 +13,21 @@
# limitations under the License.
import os
import sys
import six
import time
import math
import socket
import contextlib
from contextlib import closing
from six import string_types
import numpy as np
from collections import OrderedDict
from paddle import fluid
import paddle.fluid.unique_name as nameGen
from paddle.fluid import core
from paddle.fluid import framework
from paddle import fluid
from paddle.fluid.layers import collective
from paddle.fluid.dygraph import to_variable, no_grad, layers
from paddle.fluid.framework import Variable
from paddle.fluid.executor import global_scope
from paddle.fluid.dygraph.parallel import ParallelEnv, ParallelStrategy
from paddle.fluid.io import BatchSampler
from paddle.fluid.dygraph.parallel import Env, DataParallel, ParallelStrategy
from paddle.fluid.layers.collective import _c_allreduce, _c_allgather, _c_broadcast, \
_c_sync_comm_stream, _c_sync_calc_stream
from paddle.fluid.io import BatchSampler, DataLoader
_parallel_context_initialized = False
__parallel_context_init = False
class DistributedBatchSampler(BatchSampler):
"""Sampler that restricts data loading to a subset of the dataset.
......@@ -71,9 +62,10 @@ class DistributedBatchSampler(BatchSampler):
self.shuffle = shuffle
assert isinstance(drop_last, bool), \
"drop_last should be a boolean number"
self.drop_last = drop_last
self.nranks = get_nranks()
self.local_rank = get_local_rank()
self.nranks = ParallelEnv().nranks
self.local_rank = ParallelEnv().local_rank
self.epoch = 0
self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.nranks))
self.total_size = self.num_samples * self.nranks
......@@ -106,27 +98,22 @@ class DistributedBatchSampler(BatchSampler):
num_samples += int(not self.drop_last) * (self.batch_size - 1)
return num_samples // self.batch_size
def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
return _c_allgather(x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
def set_epoch(self, epoch):
self.epoch = epoch
def get_local_rank():
return Env().local_rank
def get_nranks():
return Env().nranks
def _all_gather(x, nranks, ring_id=0, use_calc_stream=True):
return collective._c_allgather(x, nranks, ring_id=ring_id, use_calc_stream=use_calc_stream)
def wait_server_ready(endpoints):
assert not isinstance(endpoints, string_types)
assert not isinstance(endpoints, six.string_types)
while True:
all_ok = True
not_ready_endpoints = []
for ep in endpoints:
ip_port = ep.split(":")
with closing(
with contextlib.closing(
socket.socket(socket.AF_INET,
socket.SOCK_STREAM)) as sock:
sock.settimeout(2)
......@@ -135,10 +122,6 @@ def wait_server_ready(endpoints):
all_ok = False
not_ready_endpoints.append(ep)
if not all_ok:
sys.stderr.write("server not ready, wait 3 sec to retry...\n")
sys.stderr.write("not ready endpoints:" + str(
not_ready_endpoints) + "\n")
sys.stderr.flush()
time.sleep(3)
else:
break
......@@ -154,9 +137,9 @@ def init_communicator(program, rank, nranks, wait_port,
wait_server_ready(other_endpoints)
block = program.global_block()
nccl_id_var = block.create_var(
name=nameGen.generate('nccl_id'),
name=fluid.unique_name.generate('nccl_id'),
persistable=True,
type=core.VarDesc.VarType.RAW)
type=fluid.core.VarDesc.VarType.RAW)
block.append_op(
type='c_gen_nccl_id',
......@@ -181,23 +164,23 @@ def init_communicator(program, rank, nranks, wait_port,
def prepare_distributed_context(place=None):
if place is None:
place = fluid.CUDAPlace(Env().dev_id) if Env().nranks > 1 \
place = fluid.CUDAPlace(ParallelEnv().dev_id) if ParallelEnv().nranks > 1 \
else fluid.CUDAPlace(0)
strategy = ParallelStrategy()
strategy.nranks = Env().nranks
strategy.local_rank = Env().local_rank
strategy.trainer_endpoints = Env().trainer_endpoints
strategy.current_endpoint = Env().current_endpoint
strategy.nranks = ParallelEnv().nranks
strategy.local_rank = ParallelEnv().local_rank
strategy.trainer_endpoints = ParallelEnv().trainer_endpoints
strategy.current_endpoint = ParallelEnv().current_endpoint
if strategy.nranks < 2:
return
global __parallel_context_init
global _parallel_context_initialized
if not __parallel_context_init and isinstance(place, core.CUDAPlace):
if not _parallel_context_initialized and isinstance(place, fluid.CUDAPlace):
def _init_context():
communicator_prog = framework.Program()
communicator_prog = fluid.Program()
init_communicator(communicator_prog, strategy.local_rank, strategy.nranks,
True, strategy.current_endpoint, strategy.trainer_endpoints)
exe = fluid.Executor(place)
......@@ -213,57 +196,5 @@ def prepare_distributed_context(place=None):
else:
assert ("Only support CUDAPlace for now.")
__parallel_context_init = True
_parallel_context_initialized = True
return strategy
\ No newline at end of file
class DistributedDataParallel(DataParallel):
def __init__(self, layers, strategy=None):
if strategy is None:
strategy = ParallelStrategy()
strategy.nranks = Env().nranks
strategy.local_rank = Env().local_rank
strategy.trainer_endpoints = Env().trainer_endpoints
strategy.current_endpoint = Env().current_endpoint
super(DistributedDataParallel, self).__init__(layers, strategy)
@no_grad
def apply_collective_grads(self):
"""
AllReduce the Parameters' gradient.
"""
if not self._is_data_parallel_mode():
return
grad_var_set = set()
grad_vars = []
for param in self._layers.parameters():
# NOTE(zcd): The grad_ivar maybe no generated.
if param.trainable and param._grad_ivar():
g_var = param._grad_ivar()
grad_vars.append(g_var)
assert g_var not in grad_var_set
grad_var_set.add(g_var)
mega_bytes = 128 * 1024 * 1024
group_idx = 0
memory_counter = 0
grad_var_groups = OrderedDict()
dtype = grad_vars[0].dtype
for g_var in grad_vars:
# Note: the dtype of the same group should be the same.
bytes = np.prod(g_var.shape) * core.size_of_dtype(g_var.dtype)
if memory_counter < mega_bytes and dtype == g_var.dtype:
memory_counter += bytes
else:
memory_counter = bytes
group_idx += 1
grad_var_groups.setdefault(group_idx, []).append(g_var)
coalesced_grads_and_vars = self._coalesce_tensors(grad_var_groups)
for coalesced_grad, _, _ in coalesced_grads_and_vars:
collective._c_allreduce(coalesced_grad, coalesced_grad, use_calc_stream=True)
self._split_tensors(coalesced_grads_and_vars)
mnist.py
浏览文件 @ 6d9e77b9
......@@ -26,7 +26,7 @@ from paddle.fluid.optimizer import Momentum
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
from paddle.fluid.io import MNIST as MnistDataset
from model import Model, CrossEntropy, Input, init_context
from model import Model, CrossEntropy, Input, set_device
from metrics import Accuracy
......@@ -106,7 +106,8 @@ class MNIST(Model):
def main():
init_context('dynamic' if FLAGS.dynamic else 'static')
device = set_device(FLAGS.device)
fluid.enable_dygraph(device) if FLAGS.dynamic else None
train_dataset = MnistDataset(mode='train')
val_dataset = MnistDataset(mode='test')
......@@ -118,7 +119,13 @@ def main():
optim = Momentum(
learning_rate=FLAGS.lr, momentum=.9, parameter_list=model.parameters())
model.prepare(optim, CrossEntropy(), Accuracy(topk=(1, 2)), inputs, labels)
model.prepare(
optim,
CrossEntropy(),
Accuracy(topk=(1, 2)),
inputs,
labels,
device=FLAGS.device)
if FLAGS.resume is not None:
model.load(FLAGS.resume)
......@@ -131,6 +138,8 @@ def main():
if __name__ == '__main__':
parser = argparse.ArgumentParser("CNN training on MNIST")
parser.add_argument(
"--device", type=str, default='gpu', help="device to use, gpu or cpu")
parser.add_argument(
"-d", "--dynamic", action='store_true', help="enable dygraph mode")
parser.add_argument(
......
model.py
浏览文件 @ 6d9e77b9
......@@ -20,25 +20,34 @@ import pickle
import numpy as np
import six
import warnings
from collections import Iterable
from collections import OrderedDict
from collections import OrderedDict
from collections import Iterable
from paddle import fluid
from paddle.fluid.framework import in_dygraph_mode, Variable
from paddle.fluid.executor import global_scope
from paddle.fluid.io import is_belong_to_optimizer
from paddle.fluid.dygraph.base import to_variable
from paddle.fluid.dygraph.parallel import ParallelEnv
from paddle.fluid.incubate.fleet.collective import fleet, DistributedStrategy
import paddle.fluid.incubate.fleet.base.role_maker as role_maker
import distributed
from distributed import DistributedBatchSampler
from paddle.fluid.io import DataLoader
from paddle.fluid.incubate.fleet.base import role_maker
from paddle.fluid.io import DataLoader, Dataset
from distributed import DistributedBatchSampler, _all_gather, prepare_distributed_context, _parallel_context_initialized
from metrics import Metric
from callbacks import config_callbacks
__all__ = ['Model', 'Loss', 'CrossEntropy', 'Input']
__all__ = ['Model', 'Loss', 'CrossEntropy', 'Input', 'set_device']
def set_device(device):
assert isinstance(device, six.string_types) and device.lower() in ['cpu', 'gpu'], \
"Expected device in ['cpu', 'gpu'], but got {}".format(device)
place = fluid.CUDAPlace(ParallelEnv().dev_id) \
if device.lower() == 'gpu' and fluid.is_compiled_with_cuda() \
else fluid.CPUPlace()
return place
def to_list(value):
......@@ -84,18 +93,6 @@ def extract_args(func):
return inspect.getargspec(func)[0]
def init_context(backend):
assert isinstance(backend, str) and backend.lower() in ['dynamic', 'static'], \
"Expected backend in ['dynamic', 'static'], but got {}".format(backend)
place = fluid.CUDAPlace(distributed.Env().dev_id) if \
distributed.Env().nranks > 1 else fluid.CUDAPlace(0)
distributed.prepare_distributed_context(place)
backend = backend.lower()
if backend == 'dynamic':
fluid.enable_dygraph(place)
class Input(fluid.dygraph.Layer):
def __init__(self, shape=None, dtype=None, name=None):
super(Input, self).__init__()
......@@ -161,8 +158,8 @@ class StaticGraphAdapter(object):
'test_batch': 0
}
self._nranks = distributed.Env().nranks
self._local_rank = distributed.Env().local_rank
self._nranks = ParallelEnv().nranks
self._local_rank = ParallelEnv().local_rank
@property
def mode(self):
......@@ -267,7 +264,8 @@ class StaticGraphAdapter(object):
# When using static learning rate, static-graph would make it
# a persistable var named 'unique_name.generate("learning_rate")',
# However, dygraph wouldn't save it.
if var.name not in state: continue
if var.name not in state:
continue
else:
# moment and other accumulators
if var.name not in converted_state:
......@@ -416,8 +414,10 @@ class StaticGraphAdapter(object):
metrics = []
with fluid.program_guard(prog, self._startup_prog):
if isinstance(self.model._inputs, dict):
ins = [self.model._inputs[n] \
for n in extract_args(self.model.forward) if n != 'self']
ins = [
self.model._inputs[n]
for n in extract_args(self.model.forward) if n != 'self'
]
else:
ins = self.model._inputs
lbls = self.model._labels if self.model._labels else []
......@@ -430,14 +430,9 @@ class StaticGraphAdapter(object):
losses = self.model._loss_function(outputs, labels)
if self._nranks > 1 and mode != 'train':
outputs = [
distributed._all_gather(o, self._nranks) for o in outputs
]
outputs = [_all_gather(o, self._nranks) for o in outputs]
if mode != 'test':
labels = [
distributed._all_gather(l, self._nranks)
for l in labels
]
labels = [_all_gather(l, self._nranks) for l in labels]
if mode != 'test':
for metric in self.model._metrics:
......@@ -474,24 +469,15 @@ class StaticGraphAdapter(object):
if compiled_prog is not None:
return compiled_prog
device = self.model._device
device_ids = self.model._device_ids
assert self.model._place is not None, \
"device is not set, please call `model.prepare()` first"
if device.lower() == 'gpu':
places = fluid.cuda_places(device_ids)
else:
places = fluid.cpu_places(len(device_ids) if device_ids else None)
place = self.model._place
# XXX *ALL WEIGHTS* should be initialized upon model construction
# even if `forward()` may run different code path for different mode
# therefore startup program only needs to run once
if self._executor is None:
if self._nranks > 1 and device.lower() == 'gpu':
gpu_id = int(distributed.Env().dev_id)
place = fluid.CUDAPlace(gpu_id) if device.lower(
) == 'gpu' else fluid.CPUPlace()
else:
place = places[0]
self._executor = fluid.Executor(place)
# XXX incremental initialization
uninitialized = []
......@@ -509,14 +495,8 @@ class StaticGraphAdapter(object):
if self._nranks < 2:
compiled_prog = fluid.CompiledProgram(prog)
else:
compiled_prog = prog #fleet.main_program
if len(places) > 1:
loss_name = None
if mode == 'train' and self._loss_endpoint is not None:
loss_name = self._loss_endpoint.name
compiled_prog = compiled_prog.with_data_parallel(
loss_name=loss_name, places=places)
compiled_prog = prog
self._compiled_progs[mode] = compiled_prog
......@@ -524,8 +504,8 @@ class DynamicGraphAdapter(object):
def __init__(self, model):
super(DynamicGraphAdapter, self).__init__()
self.model = model
self._nranks = distributed.Env().nranks
self._local_rank = distributed.Env().local_rank
self._nranks = ParallelEnv().nranks
self._local_rank = ParallelEnv().local_rank
self._merge_count = {
'eval_total': 0,
'test_total': 0,
......@@ -534,7 +514,13 @@ class DynamicGraphAdapter(object):
}
if self._nranks > 1:
self.ddp_model = distributed.DistributedDataParallel(self.model)
stradegy = fluid.dygraph.parallel.ParallelStrategy()
stradegy.nranks = ParallelEnv().nranks
stradegy.local_rank = ParallelEnv().local_rank
stradegy.trainer_endpoints = ParallelEnv().trainer_endpoints
stradegy.current_endpoint = ParallelEnv().current_endpoint
self.ddp_model = fluid.dygraph.parallel.DataParallel(self.model,
stradegy)
@property
def mode(self):
......@@ -590,11 +576,8 @@ class DynamicGraphAdapter(object):
else:
losses = []
if self._nranks > 1:
outputs = [
distributed._all_gather(o, self._nranks)
for o in to_list(outputs)
]
labels = [distributed._all_gather(l, self._nranks) for l in labels]
outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
labels = [_all_gather(l, self._nranks) for l in labels]
metrics = []
for metric in self.model._metrics:
# cut off padding value.
......@@ -630,10 +613,7 @@ class DynamicGraphAdapter(object):
inputs = [to_variable(x) for x in to_list(inputs)]
outputs = self.model.forward(*inputs)
if self._nranks > 2:
outputs = [
distributed._all_gather(o, self._nranks)
for o in to_list(outputs)
]
outputs = [_all_gather(o, self._nranks) for o in to_list(outputs)]
return [to_numpy(o) for o in to_list(outputs)]
def parameters(self, *args, **kwargs):
......@@ -720,10 +700,6 @@ class Model(fluid.dygraph.Layer):
self._optimizer = None
self._test_dataloader = None
# init multiple gpus context
self._place = fluid.CUDAPlace(distributed.Env().dev_id) \
if distributed.Env().nranks > 1 else fluid.CUDAPlace(0)
# init backend
if fluid.in_dygraph_mode():
self._adapter = DynamicGraphAdapter(self)
......@@ -740,7 +716,7 @@ class Model(fluid.dygraph.Layer):
return self._adapter.test(*args, **kwargs)
def save(self, *args, **kwargs):
if distributed.get_local_rank() == 0:
if ParallelEnv().local_rank == 0:
return self._adapter.save(*args, **kwargs)
def load(self, path, skip_mismatch=False, reset_optimizer=False):
......@@ -855,6 +831,35 @@ class Model(fluid.dygraph.Layer):
The default is None.
"""
if isinstance(device, fluid.CUDAPlace) or \
(isinstance(device, six.string_types) and device.lower() == 'gpu') \
or (device is None and fluid.is_compiled_with_cuda()):
if isinstance(device, fluid.CUDAPlace):
self._place = device
else:
self._place = fluid.CUDAPlace(ParallelEnv().dev_id) \
if ParallelEnv().nranks > 1 else fluid.CUDAPlace(0)
global _parallel_context_initialized
if ParallelEnv().nranks > 1 and not _parallel_context_initialized:
if fluid.in_dygraph_mode():
fluid.disable_dygraph()
fluid.enable_dygraph(self._place)
fluid.dygraph.parallel.prepare_context()
else:
prepare_distributed_context(self._place)
_parallel_context_initialized = True
elif isinstance(device, fluid.CPUPlace):
self._place = device
elif (isinstance(device, six.string_types) and device.lower() == 'cpu') \
or (device is None):
self._place = fluid.CPUPlace()
else:
raise ValueError(
"Expected device in ('gpu', 'cpu', fluid.CUDAPlace, fluid.CPUPlace, None), \
but got {}".format(device))
self._optimizer = optimizer
if loss_function:
if not isinstance(loss_function, Loss):
......@@ -871,25 +876,20 @@ class Model(fluid.dygraph.Layer):
metrics = metrics or []
for metric in to_list(metrics):
assert isinstance(metric, Metric), \
"{} is not sub class of Metric".format(metric.__class__.__name__)
"{} is not sub class of Metric".format(
metric.__class__.__name__)
self._metrics = to_list(metrics)
self._inputs = inputs
self._labels = labels
self._device = device
if device is None:
self._device = 'GPU' if fluid.is_compiled_with_cuda() else 'CPU'
self._device_ids = device_ids
if not in_dygraph_mode():
self._adapter.prepare()
def fit(
self,
train_dataset=None,
eval_dataset=None,
train_loader=None,
eval_loader=None,
train_data=None,
eval_data=None,
batch_size=1,
epochs=1,
eval_freq=1,
......@@ -904,9 +904,16 @@ class Model(fluid.dygraph.Layer):
"""
FIXME: add more comments and usage
Args:
train_loader (DataLoader): An iterable data loader is used for train.
eval_loader (DataLoader): An iterable data loader is used for
train_data (Dataset|DataLoader): An iterable data loader is used for
train. An instance of paddle.fluid.io.Dataset or
paddle.fluid.io.Dataloader is recomended.
eval_data (Dataset|DataLoader): An iterable data loader is used for
evaluation at the end of epoch. If None, will not do evaluation.
An instance of paddle.fluid.io.Dataset or paddle.fluid.io.Dataloader
is recomended.
batch_size (int): Integer number. The batch size of train_data and eval_data.
When train_data and eval_data are both the instance of Dataloader, this
parameter will be ignored.
epochs (int): Integer number. The number of epochs to train the model.
eval_freq (int): The frequency, in number of epochs, an evalutation
is performed.
......@@ -917,47 +924,57 @@ class Model(fluid.dygraph.Layer):
save_freq (int): The frequency, in number of epochs, to save checkpoint.
verbose (int): The verbosity mode, should be 0, 1, or 2.
0 = silent, 1 = progress bar, 2 = one line per epoch.
drop_last (bool): whether drop the last incomplete batch of train_data
when dataset size is not divisible by the batch size. When train_data
is an instance of Dataloader, this parameter will be ignored.
shuffle (bool): whther to shuffle train_data. When train_data is an instance
of Dataloader, this parameter will be ignored.
num_workers (int): the number of subprocess to load data, 0 for no subprocess
used and loading data in main process. When train_data and eval_data are
both the instance of Dataloader, this parameter will be ignored.
callbacks (Callback|None): A list of `Callback` instances to apply
during training. If None, `ProgBarLogger` and `ModelCheckpoint`
are automatically inserted.
"""
assert train_dataset is not None or train_loader is not None, \
"train_dataset or train_loader must be given"
assert (train_loader is not None and train_dataset is None) or \
(train_loader is None and train_dataset is not None), \
"train_dataset should not be set when train_loader is given"
assert train_data is not None, \
"train_data must be given!"
if fluid.in_dygraph_mode():
feed_list = None
else:
feed_list = [x.forward() for x in self._inputs + self._labels]
if train_loader is None:
if isinstance(train_data, Dataset):
train_sampler = DistributedBatchSampler(
train_dataset,
train_data,
batch_size=batch_size,
shuffle=shuffle,
drop_last=drop_last)
train_loader = DataLoader(
train_dataset,
train_data,
batch_sampler=train_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
else:
train_loader = train_data
if eval_loader is None and eval_dataset is not None:
if eval_data is not None and isinstance(eval_data, Dataset):
eval_sampler = DistributedBatchSampler(
eval_dataset, batch_size=batch_size)
eval_data, batch_size=batch_size)
eval_loader = DataLoader(
eval_dataset,
eval_data,
batch_sampler=eval_sampler,
places=self._place,
feed_list=feed_list,
num_workers=num_workers,
return_list=True)
elif eval_data is not None:
eval_loader = eval_data
else:
eval_loader = None
do_eval = eval_loader is not None
self._test_dataloader = eval_loader
......@@ -1010,7 +1027,7 @@ class Model(fluid.dygraph.Layer):
logs['step'] = step
if mode == 'train' or self._adapter._merge_count.get(
mode + '_batch', 0) <= 0:
logs['batch_size'] = batch_size * distributed.Env().nranks
logs['batch_size'] = batch_size * ParallelEnv().nranks
else:
logs['batch_size'] = self._adapter._merge_count[mode +
'_batch']
......@@ -1035,7 +1052,7 @@ class Model(fluid.dygraph.Layer):
loader = eval_loader
if not isinstance(eval_loader, Iterable):
loader = eval_loader()
logs = _run_one_epoch(eval_loader(), cbks, 'eval')
logs = _run_one_epoch(loader, cbks, 'eval')
cbks.on_end('eval', logs)
cbks.on_end('train', logs)
......
tests/test_model.py
浏览文件 @ 6d9e77b9
......@@ -28,7 +28,7 @@ import contextlib
import paddle
from paddle import fluid
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, Linear
from model import Model, CrossEntropy, Input, Loss, init_context
from model import Model, CrossEntropy, Input, Loss, set_device
from metrics import Accuracy
from callbacks import ProgBarLogger
from paddle.fluid.io import BatchSampler, DataLoader
......@@ -141,7 +141,8 @@ class MyCrossEntropy(Loss):
class TestModel(unittest.TestCase):
def fit(self, dynamic, is_mlp=False):
init_context('dynamic' if dynamic else 'static')
device = set_device('gpu')
fluid.enable_dygraph(device) if dynamic else None
im_shape = (-1, 784)
batch_size = 128
......@@ -156,7 +157,7 @@ class TestModel(unittest.TestCase):
optim = fluid.optimizer.Momentum(
learning_rate=0.01, momentum=.9, parameter_list=model.parameters())
loss = CrossEntropy() if not is_mlp else MyCrossEntropy()
model.prepare(optim, loss, Accuracy(), inputs, labels)
model.prepare(optim, loss, Accuracy(), inputs, labels, device=device)
cbk = ProgBarLogger(50)
model.fit(train_dataset,
val_dataset,
......
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