Langjian/distributed json

CMakeLists.txt

@@ -221,6 +221,13 @@ message(STATUS "NGRAPH_PLAIDML_ENABLE:      ${NGRAPH_PLAIDML_ENABLE}")
 message(STATUS "NGRAPH_TARGET_ARCH:         ${NGRAPH_TARGET_ARCH}")
 message(STATUS "NGRAPH_TUNE_ARCH:           ${NGRAPH_TUNE_ARCH}")
 
+if(NGRAPH_DISTRIBUTED_ENABLE)
+    find_package(MPI REQUIRED)
+    add_definitions(-DNGRAPH_DISTRIBUTED)
+    include_directories(SYSTEM ${MPI_C_INCLUDE_PATH} ${MPI_CXX_INCLUDE_PATH})
+    link_directories(${MPI_C_LIBRARIES} ${MPI_CXX_LIBRARIES})
+endif()
+
 # Find and build ngraph - if not using pre-built one
 if (NOT USE_PRE_BUILT_NGRAPH)
     ExternalProject_Add(

build_ngtf.py

@@ -456,7 +456,7 @@ def main():
 
     ngraph_cmake_flags = [
         "-DNGRAPH_INSTALL_PREFIX=" + artifacts_location,
-        "-DNGRAPH_DISTRIBUTED_ENABLE=FALSE",
+        "-DNGRAPH_DISTRIBUTED_ENABLE=TRUE",
         "-DNGRAPH_USE_CXX_ABI=" + cxx_abi,
         "-DNGRAPH_UNIT_TEST_ENABLE=NO",
         "-DNGRAPH_DEX_ONLY=TRUE",
@@ -484,6 +484,7 @@ def main():
         "-DNGRAPH_TUNE_ARCH=native",
         "-DNGRAPH_ARTIFACTS_DIR=" + artifacts_location,
         "-DUNIT_TEST_ENABLE=ON",
+        "-DNGRAPH_DISTRIBUTED_ENABLE=TRUE",
         "-DTF_SRC_DIR=" + tf_src_dir, "-DUNIT_TEST_TF_CC_DIR=" + os.path.join(
             artifacts_location, "tensorflow")
     ]

examples/mnist/mnist_softmax_distributed.py

@@ -0,0 +1,126 @@
+# Copyright 2015 The TensorFlow 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.
+# ==============================================================================
+
+"""A very simple MNIST classifier.
+
+See extensive documentation at
+https://www.tensorflow.org/get_started/mnist/beginners
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+import time
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+import tensorflow as tf
+import ngraph_bridge
+import horovod.tensorflow as hvd
+learn = tf.contrib.learn
+
+FLAGS = None
+
+hvd.init()
+
+def main(_):
+  run_mnist(_)
+
+def run_mnist(_):
+  # Import data
+  mnist = learn.datasets.mnist.read_data_sets(FLAGS.data_dir+'MNIST-data-%d' % hvd.rank(),one_hot=True)
+  #mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+
+  # Create the model
+  with tf.name_scope("mnist_placholder"):
+    x = tf.placeholder(tf.float32, [None, 784])
+    W = tf.Variable(tf.zeros([784, 10]))
+    b = tf.Variable(tf.zeros([10]))
+    y = tf.matmul(x, W) + b
+
+  # Define loss and optimizer
+    y_ = tf.placeholder(tf.float32, [None, 10])
+
+  # The raw formulation of cross-entropy,
+  #
+  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
+  #                                 reduction_indices=[1]))
+  #
+  # can be numerically unstable.
+  #
+  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
+  # outputs of 'y', and then average across the batch.
+  cross_entropy = tf.reduce_mean(
+      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
+  #global_step = tf.train.get_or_create_global_step()
+  global_step = tf.contrib.framework.get_or_create_global_step()
+  opt = tf.train.GradientDescentOptimizer(0.5)
+  # Add MPI Distributed Optimizer
+  #import pdb; pdb.set_trace()
+  with tf.name_scope("horovod_opt"):
+    opt = hvd.DistributedOptimizer(opt) 
+  train_step = opt.minimize(cross_entropy, global_step=global_step)
+
+  # The StopAtStepHook handles stopping after running given steps. 
+  hooks=[
+    hvd.BroadcastGlobalVariablesHook(0),
+    tf.train.StopAtStepHook(last_step=10)]
+
+  # Test trained model
+  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) 
+  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
+
+  # Enable soft placement and tracing as needed
+  config = tf.ConfigProto(
+    allow_soft_placement=True, 
+    log_device_placement=True,
+    inter_op_parallelism_threads=1)
+
+  #config.graph_options.optimizer_options.global_jit_level = jit_level
+  run_metadata = tf.RunMetadata()
+
+  #init_op = tf.global_variables_initializer()
+  print("Variables initialized ...")
+
+  # The MonitoredTrainingSession takes care of session initialization
+  with tf.train.MonitoredTrainingSession(hooks=hooks,
+                                         config=config) as mon_sess:
+    start = time.time()
+    train_writer = tf.summary.FileWriter(FLAGS.log_dir, mon_sess.graph)
+    while not mon_sess.should_stop():  
+      # Train
+      batch_xs, batch_ys = mnist.train.next_batch(100)
+      mon_sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
+
+      # Test trained model
+#      if not mon_sess.should_stop():
+#        print("Accuracy: ", mon_sess.run(accuracy, feed_dict={x: mnist.test.images,
+#                                            y_: mnist.test.labels}))
+
+    end = time.time()
+
+  if hvd.rank() == 0:
+    print("Training time: %f seconds" % (end-start))
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
+                      help='Directory for storing input data')
+  parser.add_argument('--log_dir',type=str, default='/tmp/tensorflow/mnist/logs/mnist_with_summaries',
+                      help='Summaries log directory')
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

src/ngraph_encapsulate_op.cc

@@ -39,6 +39,10 @@
 
 #include "ngraph/runtime/interpreter/int_backend.hpp"
 
+#ifdef NGRAPH_DISTRIBUTED
+#include <mpi.h>>
+#endif
+
 using namespace std;
 namespace ng = ngraph;
 
@@ -265,6 +269,12 @@ class NGraphEncapsulateOp : public OpKernel {
       if (std::getenv("NGRAPH_ENABLE_SERIALIZE") != nullptr) {
         std::string file_name =
             "tf_function_" + ctx->op_kernel().name() + ".json";
+#ifdef NGRAPH_DISTRIBUTED
+        int Rank_ID;
+        MPI_Comm_rank(MPI_COMM_WORLD, &Rank_ID);
+        file_name = "tf_function_" + ctx->op_kernel().name() + "_" +
+                    to_string(Rank_ID) + ".json";
+#endif
         NGRAPH_VLOG(0) << "Serializing graph to: " << file_name;
         std::string js = ngraph::serialize(ng_function, 4);
         std::ofstream f;

src/ngraph_rewrite_pass.cc

@@ -29,6 +29,10 @@
 
 #include <iomanip>
 
+#ifdef NGRAPH_DISTRIBUTED
+#include <mpi.h>>
+#endif
+
 using namespace std;
 
 namespace tensorflow {
@@ -102,13 +106,23 @@ class NGraphRewritePass : public GraphOptimizationPass {
   static std::string GraphFilenamePrefix(std::string kind, int idx) {
     std::stringstream ss;
     ss << kind << "_" << std::setfill('0') << std::setw(4) << idx;
+#ifdef NGRAPH_DISTRIBUTED
+    int Rank_ID;
+    MPI_Comm_rank(MPI_COMM_WORLD, &Rank_ID);
+    ss << "_" << std::setfill('0') << std::setw(4) << Rank_ID;
+#endif
     return ss.str();
   }
   static std::string GraphFilenamePrefix(std::string kind, int idx,
                                          int sub_idx) {
     std::stringstream ss;
     ss << GraphFilenamePrefix(kind, idx) << "_" << std::setfill('0')
        << std::setw(4) << sub_idx;
+#ifdef NGRAPH_DISTRIBUTED
+    int Rank_ID;
+    MPI_Comm_rank(MPI_COMM_WORLD, &Rank_ID);
+    ss << "_" << std::setfill('0') << std::setw(4) << Rank_ID;
+#endif
     return ss.str();
   }