Chengmo
5 years ago
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/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.h"
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namespace ops = paddle::operators;
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namespace plat = paddle::platform;
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REGISTER_OP_CUDA_KERNEL(
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distributed_lookup_table,
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ops::DistributedLookupTableKernel<plat::CUDADeviceContext, float>);
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <algorithm>
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#include <string>
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#include <vector>
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#include "paddle/fluid/framework/data_type.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/distributed/parameter_prefetch.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class DistributedLookupTableKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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auto ids_vars = context.MultiInputVar("Ids");
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auto emb_vars = context.MultiOutput<framework::Tensor>("Embeddings");
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auto id_names = context.InputNames("Ids");
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auto embedding_name = context.InputNames("W").front();
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auto out_names = context.OutputNames("Outputs");
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auto lookup_tables = context.Attr<std::vector<std::string>>("table_names");
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auto endpoints = context.Attr<std::vector<std::string>>("endpoints");
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auto is_distributed = context.Attr<bool>("is_distributed");
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operators::distributed::prefetchs(id_names, out_names, embedding_name,
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is_distributed, lookup_tables, endpoints,
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context, context.scope());
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}
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};
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} // namespace operators
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} // namespace paddle
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Distribute CTR model for test fleet api
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"""
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from __future__ import print_function
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import shutil
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import tempfile
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import time
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import paddle
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import paddle.fluid as fluid
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import os
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import numpy as np
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import ctr_dataset_reader
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from test_dist_fleet_base import runtime_main, FleetDistRunnerBase
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from dist_fleet_ctr import TestDistCTR2x2, fake_ctr_reader
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from paddle.distributed.fleet.base.util_factory import fleet_util
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# Fix seed for test
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fluid.default_startup_program().random_seed = 1
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fluid.default_main_program().random_seed = 1
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class TestDistGpuPsCTR2x2(TestDistCTR2x2):
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"""
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For test CTR model, using Fleet api & PS-GPU
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"""
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def check_model_right(self, dirname):
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model_filename = os.path.join(dirname, "__model__")
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with open(model_filename, "rb") as f:
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program_desc_str = f.read()
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program = fluid.Program.parse_from_string(program_desc_str)
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with open(os.path.join(dirname, "__model__.proto"), "w") as wn:
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wn.write(str(program))
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def do_pyreader_training(self, fleet):
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"""
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do training using dataset, using fetch handler to catch variable
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Args:
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fleet(Fleet api): the fleet object of Parameter Server, define distribute training role
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"""
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = fluid.CUDAPlace(device_id)
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exe = fluid.Executor(place)
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fleet.init_worker()
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exe.run(fleet.startup_program)
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batch_size = 4
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train_reader = paddle.batch(fake_ctr_reader(), batch_size=batch_size)
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self.reader.decorate_sample_list_generator(train_reader)
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for epoch_id in range(1):
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self.reader.start()
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try:
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pass_start = time.time()
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while True:
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loss_val = exe.run(program=fleet.main_program,
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fetch_list=[self.avg_cost.name])
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loss_val = np.mean(loss_val)
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reduce_output = fleet_util.all_reduce(
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np.array(loss_val), mode="sum")
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loss_all_trainer = fleet_util.all_gather(float(loss_val))
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loss_val = float(reduce_output) / len(loss_all_trainer)
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message = "TRAIN ---> pass: {} loss: {}\n".format(epoch_id,
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loss_val)
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fleet_util.print_on_rank(message, 0)
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pass_time = time.time() - pass_start
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except fluid.core.EOFException:
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self.reader.reset()
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model_dir = tempfile.mkdtemp()
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fleet.save_inference_model(
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exe, model_dir, [feed.name for feed in self.feeds], self.avg_cost)
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self.check_model_right(model_dir)
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if fleet.is_first_worker():
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fleet.save_persistables(executor=exe, dirname=model_dir)
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shutil.rmtree(model_dir)
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fleet.stop_worker()
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def do_dataset_training(self, fleet):
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dnn_input_dim, lr_input_dim, train_file_path = ctr_dataset_reader.prepare_data(
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)
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device_id = int(os.getenv("FLAGS_selected_gpus", "0"))
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place = fluid.CUDAPlace(device_id)
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exe = fluid.Executor(place)
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fleet.init_worker()
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exe.run(fleet.startup_program)
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thread_num = 2
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batch_size = 128
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filelist = []
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for _ in range(thread_num):
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filelist.append(train_file_path)
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# config dataset
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dataset = paddle.fleet.DatasetFactory().create_dataset()
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dataset.set_batch_size(batch_size)
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dataset.set_use_var(self.feeds)
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pipe_command = 'python ctr_dataset_reader.py'
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dataset.set_pipe_command(pipe_command)
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dataset.set_filelist(filelist)
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dataset.set_thread(thread_num)
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for epoch_id in range(1):
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pass_start = time.time()
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dataset.set_filelist(filelist)
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exe.train_from_dataset(
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program=fleet.main_program,
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dataset=dataset,
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fetch_list=[self.avg_cost],
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fetch_info=["cost"],
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print_period=2,
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debug=int(os.getenv("Debug", "0")))
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pass_time = time.time() - pass_start
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if os.getenv("SAVE_MODEL") == "1":
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model_dir = tempfile.mkdtemp()
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fleet.save_inference_model(exe, model_dir,
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[feed.name for feed in self.feeds],
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self.avg_cost)
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self.check_model_right(model_dir)
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if fleet.is_first_worker():
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fleet.save_persistables(executor=exe, dirname=model_dir)
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shutil.rmtree(model_dir)
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fleet.stop_worker()
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if __name__ == "__main__":
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runtime_main(TestDistGpuPsCTR2x2)
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