# 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 traceback import math import collections import six import unittest import numpy as np import gc gc.set_debug(gc.DEBUG_COLLECTABLE) import paddle.fluid as fluid 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(wait_port=False) 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() # NOTE: run gc.collect to eliminate pybind side objects to # prevent random double-deallocate when inherited in python. del self.transpiler del main del startup gc.collect() 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 TestDecayedAdagrad(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) opt = fluid.optimizer.DecayedAdagrad(learning_rate=0.1) opt.minimize(avg_cost) def transpiler_test_impl(self): pserver, startup = self.get_pserver(self.pserver1_ep) trainer, _ = self.get_trainer() class TestFtrl(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) opt = fluid.optimizer.Ftrl(learning_rate=0.1) opt.minimize(avg_cost) def transpiler_test_impl(self): pserver, startup = self.get_pserver(self.pserver1_ep) trainer, _ = self.get_trainer() 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", "sum", "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", "sum", "momentum"]) self.assertEqual([op.type for op in pserver.blocks[8].ops], ["sum", "scale", "scale", "sum", "momentum"]) class TestEmptyPserverOptimizeBlocks(TranspilerTest): def net_conf(self): x = fluid.layers.data(name='x', shape=[1000], dtype='float32') # only one parameter y_predict = fluid.layers.fc(input=x, size=1000, act=None, param_attr=fluid.ParamAttr(name='fc_w'), bias_attr=False) 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=1.0) sgd_optimizer.minimize(avg_cost) def transpiler_test_impl(self): config = fluid.DistributeTranspilerConfig() config.slice_var_up = False pserver, startup = self.get_pserver(ep=self.pserver2_ep, config=config) self.assertEqual(len(pserver.blocks), 2) self.assertEqual(len(pserver.blocks[1].ops), 0) 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, table_name, is_distributed): emb = fluid.layers.embedding( input=ids, size=[self.table_size, self.emb_size], dtype='float32', param_attr=table_name, 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) profile_ids = fluid.layers.data( name='brand_ids', shape=[1], dtype='int64', lod_level=1) title_emb = emb_pool(title_ids, self.lookup_table_name, is_distributed) brand_emb = emb_pool(brand_ids, self.lookup_table_name, is_distributed) profile_emb = emb_pool(profile_ids, "profile_emb", False) fc0 = fluid.layers.concat( input=[title_emb, brand_emb, profile_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), 4) # 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", "scale", "adam", "scale", "scale"]) # 3 optimize for table 2 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[3].ops], ["sum", "scale", "adam", "scale", "scale"]) trainer, _ = self.get_trainer() self.assertEqual(len(trainer.blocks), 1) ops = [ 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add', 'cross_entropy2', 'mean', 'fill_constant', 'mean_grad', 'cross_entropy_grad2', 'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad', 'split_selected_rows', 'send', 'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'send_barrier', 'recv', 'recv', 'fetch_barrier' ] 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"]) # 4 prefetch -> lookup_sparse_table for data0 self.assertEqual([op.type for op in pserver1.blocks[2].ops], ["sum", "scale", "adam", "scale", "scale"]) # 2 optimize for table sgd self.assertEqual([op.type for op in pserver1.blocks[3].ops], ["sum", "sgd"]) # 3 prefetch -> lookup_sparse_table for data0 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, trainer_startup = self.get_trainer() self.assertEqual(len(trainer.blocks), 1) ops = [ 'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add', 'cross_entropy2', 'mean', 'fill_constant', 'mean_grad', 'cross_entropy_grad2', 'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad', 'split_selected_rows', 'send', '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) startup_ops = [ 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'uniform_random', 'uniform_random', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat', 'fake_init' ] self.assertEqual([op.type for op in trainer_startup.blocks[0].ops], startup_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), 4) # 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"]) # 3 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[3].ops], ["adam", "scale", "scale"]) trainer, _ = self.get_trainer(config) self.assertEqual(len(trainer.blocks), 1) ops = [ 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add', 'cross_entropy2', 'mean', 'fill_constant', 'mean_grad', 'cross_entropy_grad2', 'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad', 'split_selected_rows', 'send', 'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'recv', 'recv' ] 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 adam self.assertEqual([op.type for op in pserver1.blocks[2].ops], ["adam", "scale", "scale"]) # 3 optimize for table sgd self.assertEqual([op.type for op in pserver1.blocks[3].ops], ["sgd"]) # 4 prefetch -> lookup_sparse_table for data0 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, trainer_startup = self.get_trainer(config) self.assertEqual(len(trainer.blocks), 1) ops = [ 'split_ids', 'prefetch', 'merge_ids', 'sequence_pool', 'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add', 'cross_entropy2', 'mean', 'fill_constant', 'mean_grad', 'cross_entropy_grad2', 'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad', 'split_selected_rows', 'send', '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) startup_ops = [ 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'fill_constant', 'uniform_random', 'uniform_random', 'recv', 'recv', 'recv', 'fetch_barrier', 'concat', 'fake_init' ] self.assertEqual([op.type for op in trainer_startup.blocks[0].ops], startup_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) vars_ps1 = pserver._parameters_on_pservers.get_distributed_vars_by_ep( self.pserver1_ep) vars_ps2 = pserver._parameters_on_pservers.get_distributed_vars_by_ep( self.pserver2_ep) self.assertTrue(vars_ps1) self.assertTrue(vars_ps2) for idx in six.moves.xrange(len(vars_ps1)): total_numel = 0 ps1_numel, ps2_numel = 0, 0 ps1_var = vars_ps1[idx] if not ps1_var.is_slice: total_numel = six.moves.reduce(lambda x, y: x * y, vars_ps1[idx].origin.shape) ps1_numel = six.moves.reduce(lambda x, y: x * y, vars_ps1[idx].slice.shape) else: ps2_var = None for var in vars_ps2: if var.origin.name == ps1_var.origin.name: ps2_var = var break total_numel = six.moves.reduce(lambda x, y: x * y, ps1_var.origin.shape) ps1_numel = six.moves.reduce(lambda x, y: x * y, ps1_var.slice.shape) ps2_numel = six.moves.reduce(lambda x, y: x * y, ps2_var.slice.shape) self.assertEqual(total_numel, ps1_numel + ps2_numel) class TestNCCL2Transpile(TranspilerTest): def test_nccl2_transpile(self): if fluid.core.is_compiled_with_cuda(): #test nccl2 only with cuda main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): self.net_conf() config = fluid.DistributeTranspilerConfig() config.mode = "nccl2" config.wait_port = False t = fluid.DistributeTranspiler(config=config) t.transpile( 0, trainers="127.0.0.1:6174,127.0.0.1:6175", current_endpoint="127.0.0.1:6174", startup_program=startup) print([op.type for op in startup.global_block().ops]) self.assertEqual(startup.global_block().ops[-1].type, "gen_nccl_id") self.assertIsNotNone(startup.global_block().vars.get("NCCLID")) gc.collect() else: pass # test for remote prefetch class TestRemoteLookupTable(TestDistLookupTableBase): def net_conf(self): import os os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1" 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), 4) # 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", "scale", "adam", "scale", "scale"]) # 3 optimize for table 2 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[3].ops], ["sum", "scale", "adam", "scale", "scale"]) trainer, _ = self.get_trainer() self.assertEqual(len(trainer.blocks), 1) ops = [ 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'lookup_table', 'sequence_pool', 'concat', 'mul', 'elementwise_add', 'cross_entropy2', 'mean', 'fill_constant', 'mean_grad', 'cross_entropy_grad2', 'elementwise_add_grad', 'send', 'mul_grad', 'send', 'concat_grad', 'sequence_pool_grad', 'lookup_table_grad', 'split_selected_rows', 'send', 'sequence_pool_grad', 'lookup_table_grad', 'sequence_pool_grad', 'lookup_table_grad', 'sum', 'split_selected_rows', 'send', 'send_barrier', 'recv', 'recv', 'fetch_barrier' ] self.assertEqual([op.type for op in trainer.blocks[0].ops], ops) # test for remote prefetch class TestRemoteNce(TestDistLookupTableBase): def network_with_table(self, is_sparse, is_distributed): num_total_classes = 20 sampler = "uniform" nid_freq_arr = np.random.dirichlet(np.ones(20) * 1000).astype('float32') input = fluid.layers.data(name="input", shape=[10], dtype="float32") label = fluid.layers.data(name="label", shape=[1], dtype="int64") w_param = fluid.default_main_program().global_block().create_parameter( shape=[num_total_classes, 10], dtype='float32', name='nce_w', initializer=fluid.initializer.ConstantInitializer()) b_param = fluid.default_main_program().global_block().create_parameter( shape=[num_total_classes, 1], dtype='float32', name='nce_b', initializer=fluid.initializer.ConstantInitializer()) cost = fluid.layers.nce(input=input, label=label, num_total_classes=num_total_classes, sampler=sampler, custom_dist=nid_freq_arr.tolist(), sample_weight=None, param_attr='nce_w', bias_attr='nce_b', seed=1, num_neg_samples=5, is_sparse=is_sparse) avg_cost = fluid.layers.mean(cost) # optimizer optimizer = fluid.optimizer.Adam(learning_rate=0.003) optimizer.minimize(avg_cost) def net_conf(self): import os os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1" self.network_with_table(is_sparse=True, is_distributed=False) def transpiler_test_impl(self): trainer, _ = self.get_trainer() out_vars = ["nce_w"] in_vars = ["nce_b"] recv_var_names = [] for op in trainer.blocks[0].ops: if op.type == "recv": for var in op.output("Out"): recv_var_names.append(var) for out_var in out_vars: self.assertFalse(out_var in recv_var_names) for in_var in in_vars: self.assertTrue(in_var in recv_var_names) # test for remote prefetch class TestRemoteHsigmoid(TestDistLookupTableBase): def network_with_table(self, is_sparse, is_distributed): num_total_classes = 3 input = fluid.layers.data(name="input", shape=[1], dtype="float32") label = fluid.layers.data(name="label", shape=[1], dtype="int64") path_table = fluid.layers.data( name='path_table', shape=[3], dtype='int64') path_code = fluid.layers.data( name='path_code', shape=[3], dtype='int64') w_param = fluid.default_main_program().global_block().create_parameter( shape=[num_total_classes, 10], dtype='float32', name='hs_w', initializer=fluid.initializer.ConstantInitializer()) b_param = fluid.default_main_program().global_block().create_parameter( shape=[3, 1], dtype='float32', name='hs_b', initializer=fluid.initializer.ConstantInitializer()) emb = fluid.layers.embedding( input=input, is_sparse=is_sparse, size=[3, 3], param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(num_total_classes)))) cost = fluid.layers.hsigmoid( input=emb, label=label, num_classes=num_total_classes, path_table=path_table, path_code=path_code, is_custom=True, is_sparse=is_sparse) avg_cost = fluid.layers.mean(cost) # optimizer optimizer = fluid.optimizer.SGD(learning_rate=0.003) optimizer.minimize(avg_cost) def net_conf(self): import os os.environ['PADDLE_ENABLE_REMOTE_PREFETCH'] = "1" self.network_with_table(is_sparse=True, is_distributed=False) def transpiler_test_impl(self): trainer, _ = self.get_trainer() params_to_check = list() for op in trainer.blocks[0].ops: if op.type == "hierarchical_sigmoid": params_to_check = [op.input("W")[0], op.input("Bias")[0]] for name in ["epmap", "table_names", "epmap"]: assert op.has_attr(name) if name == "epmap": assert op.attr(name)[0] == u'127.0.0.1:6174' elif name == "table_names": assert op.attr(name)[0] == u'hierarchical_sigmoid_0.w_0' else: assert op.attr(name) == 3 elif op.type == "lookup_table": params_to_check.append(op.input("W")[0]) else: pass op_count = 0 for op in trainer.blocks[0].ops: if op.type == "recv": assert len(op.output("Out")) == 1 assert op.output("Out")[0] == u'hierarchical_sigmoid_0.b_0' op_count += 1 assert op_count == 1 if __name__ == "__main__": unittest.main()