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Paddle/python/paddle/fluid/tests/unittests/test_dist_transpiler.py

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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import math
import unittest
import paddle.fluid as fluid
from paddle.fluid.transpiler.distribute_transpiler import delete_ops
import traceback
import collections
import six
class TranspilerTest(unittest.TestCase):
def setUp(self):
self.trainer_id = 0
self.trainers = 2
self.pservers = 2
# NOTE: we do not actually bind this port
self.pserver_eps = "127.0.0.1:6174,127.0.0.1:6175"
self.pserver1_ep = "127.0.0.1:6174"
self.pserver2_ep = "127.0.0.1:6175"
self.sync_mode = True
self.transpiler = None
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
sgd_optimizer.minimize(avg_cost)
def get_main_program(self):
main = fluid.Program()
main.random_seed = 1
with fluid.program_guard(main):
self.net_conf()
self.origin_prog = main.clone()
return main
def get_trainer(self, config=None):
src = fluid.default_startup_program().clone()
t = self._transpiler_instance(config)
trainer_main = t.get_trainer_program()
trainer_startup = fluid.default_startup_program()
assert (src.num_blocks == 1)
assert (trainer_startup.num_blocks == src.num_blocks)
return trainer_main, trainer_startup
def get_pserver(self, ep, config=None, sync_mode=True):
t = self._transpiler_instance(config, sync_mode)
pserver = t.get_pserver_program(ep)
startup = t.get_startup_program(ep, pserver)
return pserver, startup
def _transpiler_instance(self, config=None, sync_mode=True):
if not self.transpiler:
main = self.get_main_program()
self.transpiler = fluid.DistributeTranspiler(config=config)
self.transpiler.transpile(
self.trainer_id,
program=main,
pservers=self.pserver_eps,
trainers=self.trainers,
sync_mode=sync_mode)
return self.transpiler
def transpiler_test_impl(self):
pass
def test_transpiler(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
self.transpiler_test_impl()
class TestBasicModel(TranspilerTest):
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
pserver2, startup2 = self.get_pserver(self.pserver2_ep)
trainer, trainer_startup = self.get_trainer()
# splited var blocks should be in startup program
self.assertTrue("fc_w.block0" in trainer_startup.global_block().vars)
self.assertTrue("fc_w.block1" in trainer_startup.global_block().vars)
self.assertTrue("fc_w" in trainer_startup.global_block().vars)
self.assertTrue("fc_b" in trainer_startup.global_block().vars)
self.assertTrue("fc_w@GRAD" not in trainer_startup.global_block().vars)
self.assertTrue("fc_b@GRAD" not in trainer_startup.global_block().vars)
src = [op.type for op in trainer_startup.global_block().ops]
dst = ['fill_constant', 'fill_constant', 'uniform_random', 'recv', 'recv', \
'fetch_barrier', 'concat']
self.assertEqual(src, dst)
self.assertEqual([op.type for op in trainer.global_block().ops], [
'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'split_byref', 'send',
'send_barrier', 'recv', 'recv', 'fetch_barrier', 'concat'
])
self.assertEqual(len(pserver.blocks), 3)
# block0: listen_and_serv
self.assertEqual([op.type for op in pserver.blocks[0].ops],
["listen_and_serv"])
# block1~2: optimize pass
self.assertEqual([op.type for op in pserver.blocks[1].ops],
["sum", "scale", "sgd"])
# confirm startup program
self.assertEqual([op.type for op in startup.global_block().ops],
["fill_constant", "fill_constant", "uniform_random"])
# the variable #fc_w will be split into two blocks
fc_w_var = startup.global_block().var("fc_w.block1")
self.assertEqual(fc_w_var.shape, (500, 1000))
# all parameters should be optimized on pserver
pserver_params = []
for prog in [pserver, pserver2]:
for blk in prog.blocks:
for op in blk.ops:
if "Param" in op.input_names:
param_name = op.input("Param")[0]
is_block_idx = param_name.find(".block")
if is_block_idx != -1:
origin_param_name = param_name[:is_block_idx]
else:
origin_param_name = param_name
pserver_params.append(origin_param_name)
trainer_params = []
for op in self.origin_prog.global_block().ops:
if "Param" in op.input_names:
trainer_params.append(op.input("Param")[0])
self.assertEqual(set(pserver_params), set(trainer_params))
class TestBasicModelWithLargeBlockSize(TranspilerTest):
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
config.min_block_size = 1048576
pserver, startup = self.get_pserver(self.pserver1_ep, config)
pserver2, startup2 = self.get_pserver(self.pserver2_ep, config)
trainer, _ = self.get_trainer(config)
self.assertEqual([op.type for op in trainer.global_block().ops], [
'mul', 'elementwise_add', 'elementwise_sub', 'square', 'mean',
'fill_constant', 'mean_grad', 'square_grad', 'elementwise_sub_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'send', 'send_barrier',
'recv', 'recv', 'fetch_barrier'
])
self.assertEqual(len(pserver.blocks), 2)
# block0: listen_and_serv
self.assertEqual([op.type for op in pserver.blocks[0].ops],
["listen_and_serv"])
# block1~2: optimize pass
self.assertEqual([op.type for op in pserver.blocks[1].ops],
["sum", "scale", "sgd"])
# confirm startup program
self.assertEqual([op.type for op in startup.global_block().ops],
["fill_constant", "fill_constant"])
# the variable #fc_w will be split into two blocks
fc_w_var = startup2.global_block().var("fc_w")
self.assertEqual(fc_w_var.shape, (1000, 1000))
# all parameters should be optimized on pserver
pserver_params = []
for prog in [pserver, pserver2]:
for blk in prog.blocks:
for op in blk.ops:
if "Param" in op.input_names:
param_name = op.input("Param")[0]
is_block_idx = param_name.find(".block")
if is_block_idx != -1:
origin_param_name = param_name[:is_block_idx]
else:
origin_param_name = param_name
pserver_params.append(origin_param_name)
trainer_params = []
for op in self.origin_prog.global_block().ops:
if "Param" in op.input_names:
trainer_params.append(op.input("Param")[0])
self.assertEqual(set(pserver_params), set(trainer_params))
class TestNoSliceVar(TranspilerTest):
def setUp(self):
super(TestNoSliceVar, self).setUp()
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = False
_, startup = self.get_pserver(self.pserver1_ep, config)
_, startup2 = self.get_pserver(self.pserver2_ep, config)
if "fc_w" in startup.global_block().vars:
fc_w_var = startup.global_block().vars["fc_w"]
elif "fc_w" in startup2.global_block().vars:
fc_w_var = startup2.global_block().vars["fc_w"]
self.assertEqual(fc_w_var.shape, (1000, 1000))
class TestLRDecay(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.exponential_decay(
learning_rate=1.0,
decay_steps=2100,
decay_rate=0.1,
staircase=True))
sgd_optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
self.assertEqual(len(pserver.blocks), 4)
lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
self.assertEqual(lr_decay_ops, [
"increment", "cast", "fill_constant", "elementwise_div", "floor",
"fill_constant", "elementwise_pow", "fill_constant",
"elementwise_mul"
])
class TestLRDecayConditional(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(
learning_rate=fluid.layers.piecewise_decay([10000, 20000],
[1.0, 0.5, 1.0]))
sgd_optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
serv_op = pserver.blocks[0].ops[0]
sub_blocks = []
optimize_blocks = []
for b in serv_op.all_attrs()["optimize_blocks"]:
optimize_blocks.append(b.idx)
for b in pserver.blocks:
if b.idx not in optimize_blocks:
sub_blocks.append(b.idx)
self.assertEqual(len(pserver.blocks), 7)
lr_decay_ops = [op.type for op in pserver.blocks[1].ops]
self.assertEqual(lr_decay_ops, [
"increment", "cast", "fill_constant", "fill_constant", "less_than",
"logical_not", "conditional_block", "fill_constant",
"fill_constant", "less_than", "logical_not", "logical_and",
"logical_and", "conditional_block", "fill_constant",
"conditional_block"
])
# test the condition blocks
for b in sub_blocks:
if b == 0:
continue
block = pserver.blocks[b]
self.assertEqual([op.type for op in block.ops], ["assign"])
class TestL2Decay(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(
input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(
name='fc_w',
regularizer=fluid.regularizer.L2Decay(),
gradient_clip=fluid.clip.GradientClipByValue(0.1)),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.1)
sgd_optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
self.assertEqual(len(pserver.blocks), 3)
self.assertEqual([op.type for op in pserver.blocks[1].ops],
["sum", "scale", "clip", "sgd"])
self.assertEqual(
[op.type for op in pserver.blocks[2].ops],
["sum", "scale", "clip", "scale", "elementwise_add", "sgd"])
# TODO(typhoonzero): test clipping and L2Decay ops are removed from trainer
class TestL2DecayWithPiecewise(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
base_lr = 1.0
bd = [1, 10, 20, 30]
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
sgd_optimizer = fluid.optimizer.Momentum(
learning_rate=fluid.layers.piecewise_decay(
boundaries=bd, values=lr),
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
sgd_optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
trainer, _ = self.get_trainer()
self.assertEqual(len(pserver.blocks), 9)
self.assertEqual([op.type for op in pserver.blocks[1].ops], [
"increment", "cast", "fill_constant", "fill_constant", "less_than",
"logical_not", "conditional_block", "fill_constant",
"fill_constant", "less_than", "logical_not", "logical_and",
"logical_and", "conditional_block", "fill_constant",
"fill_constant", "less_than", "logical_not", "logical_and",
"logical_and", "conditional_block", "fill_constant",
"fill_constant", "less_than", "logical_not", "logical_and",
"logical_and", "conditional_block", "fill_constant",
"conditional_block"
])
self.assertEqual(
[op.type for op in pserver.blocks[7].ops],
["sum", "scale", "scale", "elementwise_add", "momentum"])
self.assertEqual(
[op.type for op in pserver.blocks[8].ops],
["sum", "scale", "scale", "elementwise_add", "momentum"])
class TestDistLookupTableBase(TranspilerTest):
def network_with_table(self, is_sparse, is_distributed):
self.table_size = 1000
self.emb_size = 64
self.lookup_table_name = 'shared_w'
def emb_pool(ids):
emb = fluid.layers.embedding(
input=ids,
size=[self.table_size, self.emb_size],
dtype='float32',
param_attr=self.lookup_table_name, # share parameter
is_sparse=is_sparse,
is_distributed=is_distributed)
pool = fluid.layers.sequence_pool(input=emb, pool_type='average')
return pool
title_ids = fluid.layers.data(
name='title_ids', shape=[1], dtype='int64', lod_level=1)
brand_ids = fluid.layers.data(
name='brand_ids', shape=[1], dtype='int64', lod_level=1)
title_emb = emb_pool(title_ids)
brand_emb = emb_pool(brand_ids)
fc0 = fluid.layers.concat(input=[title_emb, brand_emb], axis=1)
predict = fluid.layers.fc(input=fc0,
size=2,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.003)
optimizer.minimize(avg_cost)
class TestLocalLookupTable(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=False)
def transpiler_test_impl(self):
pserver1, startup1 = self.get_pserver(self.pserver1_ep)
self.assertEqual(len(pserver1.blocks), 3)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["sum", "scale", "adam", "scale", "scale"])
# 2 optimize for table adam
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["sum", "adam", "scale", "scale"])
trainer, _ = self.get_trainer()
self.assertEqual(len(trainer.blocks), 1)
ops = [
'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
'fill_constant', 'mean_grad', 'cross_entropy_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_selected_rows', 'send',
'send_barrier', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
class TestDistLookupTable(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=True)
def transpiler_test_impl(self):
pserver1, startup1 = self.get_pserver(self.pserver1_ep)
self.assertEqual(len(pserver1.blocks), 6)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["sum", "scale", "adam", "scale", "scale"])
# 2 optimize for table sgd
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["sum", "sgd"])
# 3 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
["lookup_sparse_table"])
# 4 prefetch -> lookup_sparse_table for data1
self.assertEqual([op.type for op in pserver1.blocks[4].ops],
["lookup_sparse_table"])
# 5 save table
self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
trainer, _ = self.get_trainer()
self.assertEqual(len(trainer.blocks), 1)
ops = [
'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
'prefetch', 'merge_ids', 'sequence_pool', 'concat', 'mul',
'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_ids', 'send', 'send_barrier', 'recv', 'recv',
'fetch_barrier'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
class TestAsyncLocalLookupTable(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=False)
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
self.assertEqual(len(pserver1.blocks), 3)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["adam", "scale", "scale"])
# 2 optimize for table adam
# NOTE: if param is not selected rows, the grad will scaled to grad / trainer_num
self.assertEqual([op.type for op in pserver1.blocks[2].ops],
["adam", "scale", "scale"])
trainer, _ = self.get_trainer(config)
self.assertEqual(len(trainer.blocks), 1)
ops = [
'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool',
'concat', 'mul', 'elementwise_add', 'cross_entropy', 'mean',
'fill_constant', 'mean_grad', 'cross_entropy_grad',
'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad',
'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'recv',
'recv', 'recv', 'concat'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
class TestAsyncDistLookupTable(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=True)
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
pserver1, startup1 = self.get_pserver(self.pserver1_ep, config, False)
self.assertEqual(len(pserver1.blocks), 6)
# 0 listen_and_serv
# 1 optimize for fc_w or fc_b adam
self.assertEqual([op.type for op in pserver1.blocks[1].ops],
["adam", "scale", "scale"])
# 2 optimize for table sgd
self.assertEqual([op.type for op in pserver1.blocks[2].ops], ["sgd"])
# 3 prefetch -> lookup_sparse_table for data0
self.assertEqual([op.type for op in pserver1.blocks[3].ops],
["lookup_sparse_table"])
# 4 prefetch -> lookup_sparse_table for data1
self.assertEqual([op.type for op in pserver1.blocks[4].ops],
["lookup_sparse_table"])
# 5 save table
self.assertEqual([op.type for op in pserver1.blocks[5].ops], ["save"])
trainer, _ = self.get_trainer(config)
self.assertEqual(len(trainer.blocks), 1)
ops = [
'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'split_ids',
'prefetch', 'merge_ids', 'sequence_pool', 'concat', 'mul',
'elementwise_add', 'cross_entropy', 'mean', 'fill_constant',
'mean_grad', 'cross_entropy_grad', 'elementwise_add_grad', 'send',
'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad',
'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad',
'sum', 'split_ids', 'send', 'recv', 'recv'
]
self.assertEqual([op.type for op in trainer.blocks[0].ops], ops)
class TestDistLookupTableSliceSize(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=True)
def transpiler_test_impl(self):
config = fluid.DistributeTranspilerConfig()
pserver1, _ = self.get_pserver(self.pserver1_ep, config)
self.assertTrue(self.transpiler.has_distributed_lookup_table)
lookup_table_var = pserver1.global_block().vars[
self.transpiler.table_name]
row_size = lookup_table_var.shape[0]
calc_row_size = int(math.ceil(self.table_size / self.pservers))
self.assertEqual(row_size, calc_row_size)
class TestDistArgsInProgram(TestDistLookupTableBase):
def net_conf(self):
self.network_with_table(is_sparse=True, is_distributed=True)
def transpiler_test_impl(self):
trainer, _ = self.get_trainer()
self.assertTrue(trainer._is_distributed)
self.assertTrue(trainer._is_chief)
self.assertEqual(trainer._distributed_lookup_table,
self.lookup_table_name)
self.assertEqual(trainer._endpoints,
[self.pserver1_ep, self.pserver2_ep])
class TestRMSPropOptimizer(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, startup = self.get_pserver(self.pserver1_ep)
pserver2, startup2 = self.get_pserver(self.pserver2_ep)
self.assertEqual(len(pserver.blocks), 3)
# block1~2: optimize pass
self.assertEqual([op.type for op in pserver.blocks[1].ops],
["sum", "scale", "rmsprop"])
# the variable #fc_w will be split into two blocks
fc_w_var = startup.global_block().var("fc_w.block1")
self.assertEqual(fc_w_var.shape, (500, 1000))
moment_var = startup.global_block().var("momentum_1")
self.assertEqual(moment_var.shape, (500, 1000))
class TestLoadSliceVar(TranspilerTest):
def net_conf(self):
x = fluid.layers.data(name='x', shape=[1000], dtype='float32')
y_predict = fluid.layers.fc(input=x,
size=1000,
act=None,
param_attr=fluid.ParamAttr(name='fc_w'),
bias_attr=fluid.ParamAttr(name='fc_b'))
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
optimizer = fluid.optimizer.RMSProp(learning_rate=0.1)
optimizer.minimize(avg_cost)
def transpiler_test_impl(self):
pserver, _ = self.get_pserver(self.pserver1_ep)
pserver2, _ = self.get_pserver(self.pserver2_ep)
self.assertTrue(pserver._slice_vars_and_attrs)
self.assertTrue(pserver2._slice_vars_and_attrs)
for idx in six.moves.xrange(len(pserver._slice_vars_and_attrs)):
self.assertEqual(pserver._slice_vars_and_attrs[idx][0],
pserver2._slice_vars_and_attrs[idx][0])
total_numel = six.moves.reduce(
lambda x, y: x * y, pserver._slice_vars_and_attrs[idx][0].shape)
self.assertEqual(
total_numel,
six.moves.reduce(lambda x, y: x * y,
pserver._slice_vars_and_attrs[idx][2].shape) +
six.moves.reduce(lambda x, y: x * y,
pserver2._slice_vars_and_attrs[idx][2].shape))
if __name__ == "__main__":
unittest.main()
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