Add se-resnext test for 2x2 and parallel exe (#12194)

* wip add se-resnext test for 2x2 and parallel exe

* testing

* finish dist unitest 2x2

* update python interp

* fix gpu number

* make this test can run in parallel

Author: typhoonzero

python/paddle/fluid/tests/unittests/dist_se_resnext.py

@@ -0,0 +1,350 @@
+#   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 numpy as np
+import argparse
+import time
+import math
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.profiler as profiler
+from paddle.fluid import core
+import unittest
+from multiprocessing import Process
+import os
+import sys
+import signal
+
+# Fix seed for test
+fluid.default_startup_program().random_seed = 1
+fluid.default_main_program().random_seed = 1
+
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "piecewise_decay",
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+
+class SE_ResNeXt():
+    def __init__(self, layers=50):
+        self.params = train_parameters
+        self.layers = layers
+
+    def net(self, input, class_dim=1000):
+        layers = self.layers
+        supported_layers = [50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+        if layers == 50:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 6, 3]
+            num_filters = [128, 256, 512, 1024]
+
+            conv = self.conv_bn_layer(
+                input=input,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            conv = fluid.layers.pool2d(
+                input=conv,
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 101:
+            cardinality = 32
+            reduction_ratio = 16
+            depth = [3, 4, 23, 3]
+            num_filters = [128, 256, 512, 1024]
+
+            conv = self.conv_bn_layer(
+                input=input,
+                num_filters=64,
+                filter_size=7,
+                stride=2,
+                act='relu')
+            conv = fluid.layers.pool2d(
+                input=conv,
+                pool_size=3,
+                pool_stride=2,
+                pool_padding=1,
+                pool_type='max')
+        elif layers == 152:
+            cardinality = 64
+            reduction_ratio = 16
+            depth = [3, 8, 36, 3]
+            num_filters = [128, 256, 512, 1024]
+
+            conv = self.conv_bn_layer(
+                input=input,
+                num_filters=64,
+                filter_size=3,
+                stride=2,
+                act='relu')
+            conv = self.conv_bn_layer(
+                input=conv, num_filters=64, filter_size=3, stride=1, act='relu')
+            conv = self.conv_bn_layer(
+                input=conv,
+                num_filters=128,
+                filter_size=3,
+                stride=1,
+                act='relu')
+            conv = fluid.layers.pool2d(
+                input=conv, pool_size=3, pool_stride=2, pool_padding=1, \
+                pool_type='max')
+
+        for block in range(len(depth)):
+            for i in range(depth[block]):
+                conv = self.bottleneck_block(
+                    input=conv,
+                    num_filters=num_filters[block],
+                    stride=2 if i == 0 and block != 0 else 1,
+                    cardinality=cardinality,
+                    reduction_ratio=reduction_ratio)
+
+        pool = fluid.layers.pool2d(
+            input=conv, pool_size=7, pool_type='avg', global_pooling=True)
+        drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
+        stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
+        out = fluid.layers.fc(input=drop, size=class_dim, act='softmax')
+        return out
+
+    def shortcut(self, input, ch_out, stride):
+        ch_in = input.shape[1]
+        if ch_in != ch_out or stride != 1:
+            filter_size = 1
+            return self.conv_bn_layer(input, ch_out, filter_size, stride)
+        else:
+            return input
+
+    def bottleneck_block(self, input, num_filters, stride, cardinality,
+                         reduction_ratio):
+        conv0 = self.conv_bn_layer(
+            input=input, num_filters=num_filters, filter_size=1, act='relu')
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            groups=cardinality,
+            act='relu')
+        conv2 = self.conv_bn_layer(
+            input=conv1, num_filters=num_filters * 2, filter_size=1, act=None)
+        scale = self.squeeze_excitation(
+            input=conv2,
+            num_channels=num_filters * 2,
+            reduction_ratio=reduction_ratio)
+
+        short = self.shortcut(input, num_filters * 2, stride)
+
+        return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
+
+    def conv_bn_layer(self,
+                      input,
+                      num_filters,
+                      filter_size,
+                      stride=1,
+                      groups=1,
+                      act=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) / 2,
+            groups=groups,
+            act=None,
+            bias_attr=False)
+        return fluid.layers.batch_norm(input=conv, act=act)
+
+    def squeeze_excitation(self, input, num_channels, reduction_ratio):
+        pool = fluid.layers.pool2d(
+            input=input, pool_size=0, pool_type='avg', global_pooling=True)
+        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
+        squeeze = fluid.layers.fc(input=pool,
+                                  size=num_channels / reduction_ratio,
+                                  act='relu')
+        stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
+        excitation = fluid.layers.fc(input=squeeze,
+                                     size=num_channels,
+                                     act='sigmoid')
+        scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
+        return scale
+
+
+def get_model(batch_size):
+    # Input data
+    image = fluid.layers.fill_constant(
+        shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
+    label = fluid.layers.fill_constant(
+        shape=[batch_size, 1], dtype='int64', value=0.0)
+
+    # Train program
+    model = SE_ResNeXt(layers=50)
+    out = model.net(input=image, class_dim=102)
+    cost = fluid.layers.cross_entropy(input=out, label=label)
+
+    avg_cost = fluid.layers.mean(x=cost)
+    acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
+    acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
+
+    # Evaluator
+    test_program = fluid.default_main_program().clone(for_test=True)
+
+    # Optimization
+    total_images = 6149  # flowers
+    epochs = [30, 60, 90]
+    step = int(total_images / batch_size + 1)
+
+    bd = [step * e for e in epochs]
+    base_lr = 0.1
+    lr = []
+    lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
+
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=fluid.layers.piecewise_decay(
+            boundaries=bd, values=lr),
+        momentum=0.9,
+        regularization=fluid.regularizer.L2Decay(1e-4))
+    optimizer.minimize(avg_cost)
+
+    # Reader
+    train_reader = paddle.batch(
+        paddle.dataset.flowers.train(), batch_size=batch_size)
+    test_reader = paddle.batch(
+        paddle.dataset.flowers.test(), batch_size=batch_size)
+
+    return test_program, avg_cost, train_reader, test_reader, acc_top1, out
+
+
+def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
+    t = fluid.DistributeTranspiler()
+    t.transpile(
+        trainer_id=trainer_id,
+        program=main_program,
+        pservers=pserver_endpoints,
+        trainers=trainers)
+    return t
+
+
+class DistSeResneXt2x2:
+    def run_pserver(self, pserver_endpoints, trainers, current_endpoint,
+                    trainer_id):
+        get_model(batch_size=2)
+        t = get_transpiler(trainer_id,
+                           fluid.default_main_program(), pserver_endpoints,
+                           trainers)
+        pserver_prog = t.get_pserver_program(current_endpoint)
+        startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(startup_prog)
+        exe.run(pserver_prog)
+
+    def _wait_ps_ready(self, pid):
+        retry_times = 20
+        while True:
+            assert retry_times >= 0, "wait ps ready failed"
+            time.sleep(3)
+            print("waiting ps ready: ", pid)
+            try:
+                # the listen_and_serv_op would touch a file which contains the listen port
+                # on the /tmp directory until it was ready to process all the RPC call.
+                os.stat("/tmp/paddle.%d.port" % pid)
+                return
+            except os.error:
+                retry_times -= 1
+
+    def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
+        test_program, avg_cost, train_reader, test_reader, batch_acc, predict = get_model(
+            batch_size=20)
+        if is_dist:
+            t = get_transpiler(trainer_id,
+                               fluid.default_main_program(), endpoints,
+                               trainers)
+            trainer_prog = t.get_trainer_program()
+        else:
+            trainer_prog = fluid.default_main_program()
+
+        startup_exe = fluid.Executor(place)
+        startup_exe.run(fluid.default_startup_program())
+
+        strategy = fluid.ExecutionStrategy()
+        strategy.num_threads = 1
+        strategy.allow_op_delay = False
+        exe = fluid.ParallelExecutor(
+            True,
+            loss_name=avg_cost.name,
+            exec_strategy=strategy,
+            num_trainers=trainers,
+            trainer_id=trainer_id)
+
+        feed_var_list = [
+            var for var in trainer_prog.global_block().vars.itervalues()
+            if var.is_data
+        ]
+
+        feeder = fluid.DataFeeder(feed_var_list, place)
+        reader_generator = train_reader()
+        first_loss, = exe.run(fetch_list=[avg_cost.name])
+        print(first_loss)
+        for i in xrange(5):
+            loss, = exe.run(fetch_list=[avg_cost.name])
+        last_loss, = exe.run(fetch_list=[avg_cost.name])
+        print(last_loss)
+
+
+def main(role="pserver",
+         endpoints="127.0.0.1:9123",
+         trainer_id=0,
+         current_endpoint="127.0.0.1:9123",
+         trainers=1,
+         is_dist=True):
+    model = DistSeResneXt2x2()
+    if role == "pserver":
+        model.run_pserver(endpoints, trainers, current_endpoint, trainer_id)
+    else:
+        p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
+        ) else fluid.CPUPlace()
+        model.run_trainer(p, endpoints, trainer_id, trainers, is_dist)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 7:
+        print(
+            "Usage: python dist_se_resnext.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
+        )
+    role = sys.argv[1]
+    endpoints = sys.argv[2]
+    trainer_id = int(sys.argv[3])
+    current_endpoint = sys.argv[4]
+    trainers = int(sys.argv[5])
+    is_dist = True if sys.argv[6] == "TRUE" else False
+    main(
+        role=role,
+        endpoints=endpoints,
+        trainer_id=trainer_id,
+        current_endpoint=current_endpoint,
+        trainers=trainers,
+        is_dist=is_dist)