# 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. import unittest import paddle.fluid as fluid from paddle.fluid.transpiler.distribute_transpiler import delete_ops import traceback 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) return def get_main_program(self): main = fluid.Program() with fluid.program_guard(main): self.net_conf() self.origin_prog = main.clone() return main def get_trainer(self, config=None): t = self._transpiler_instance(config) return t.get_trainer_program() def get_pserver(self, ep, config=None): t = self._transpiler_instance(config) pserver = t.get_pserver_program(ep) startup = t.get_startup_program(ep, pserver) return pserver, startup def _transpiler_instance(self, config=None): 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) return self.transpiler def transpiler_test_impl(self): pass def test_transpiler(self): main = fluid.Program() startup = fluid.Program() 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 = self.get_trainer() 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, (1000L, 1000L)) # 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 startup.global_block().vars.has_key("fc_w"): fc_w_var = startup.global_block().vars["fc_w"] elif startup2.global_block().vars.has_key("fc_w"): 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) return 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) return 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.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) return 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) return 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): def emb_pool(ids): table_size = 1000 emb_size = 64 emb = fluid.layers.embedding( input=ids, size=[table_size, emb_size], dtype='float32', param_attr='shared_w', # 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) if __name__ == "__main__": unittest.main()