Langjian/distributed json

CMakeLists.txt

@@ -226,6 +226,12 @@ 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

@@ -408,6 +408,11 @@ def main():
         "  |-- venv-tf-py3 (Virtualenv directory to be used)\n",
         action="store_true")
 
+    parser.add_argument(
+        '--distributed_build',
+        help="Builds a distributed version of the nGraph components\n",
+        action="store_true")
+
     arguments = parser.parse_args()
 
     if (arguments.debug_build):
@@ -494,7 +499,6 @@ def main():
 
         ngraph_cmake_flags = [
             "-DNGRAPH_INSTALL_PREFIX=" + artifacts_location,
-            "-DNGRAPH_DISTRIBUTED_ENABLE=FALSE",
             "-DNGRAPH_USE_CXX_ABI=" + cxx_abi,
             "-DNGRAPH_UNIT_TEST_ENABLE=NO",
             "-DNGRAPH_DEX_ONLY=TRUE",
@@ -512,6 +516,11 @@ def main():
         if (arguments.debug_build):
             ngraph_cmake_flags.extend(["-DCMAKE_BUILD_TYPE=Debug"])
 
+        if (arguments.distributed_build): 
+            ngraph_cmake_flags.extend(["-DNGRAPH_DISTRIBUTED_ENABLE=TRUE"])
+        else:
+            ngraph_cmake_flags.extend(["-DNGRAPH_DISTRIBUTED_ENABLE=FALSE"])
+
         build_ngraph("./ngraph", ngraph_cmake_flags, verbosity)
 
     # Next build CMAKE options for the bridge
@@ -529,6 +538,11 @@ def main():
     if (arguments.debug_build):
         ngraph_tf_cmake_flags.extend(["-DCMAKE_BUILD_TYPE=Debug"])
 
+    if (arguments.distributed_build):
+        ngraph_tf_cmake_flags.extend(["-DNGRAPH_DISTRIBUTED_ENABLE=TRUE"])
+    else:
+        ngraph_tf_cmake_flags.extend(["-DNGRAPH_DISTRIBUTED_ENABLE=FALSE"])
+
     # Now build the bridge
     ng_tf_whl = build_ngraph_tf(artifacts_location, "../", venv_dir,
                                 ngraph_tf_cmake_flags, verbosity)

examples/mnist/mnist_softmax_distributed.py

@@ -0,0 +1,148 @@
+# 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
+Reference to the original source code:
+https://github.com/tensorflow/tensorflow/blob/r1.2/tensorflow/examples/tutorials/mnist/mnist_softmax.py
+Add distributed fetaure with horovod
+1. hvd.init()
+2. Add distributed wrapper from hvd.DistributedOptimizer
+3. Broadcast the variables from root rank to the rest processors: hvd.BroadcastGlobalVariablesHook(0)
+4. Print the output for root rank only
+"""
+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)
+
+    # 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
+    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)
+# run command for this distributed script
+# mpirun -np 2 python mnist_softmax_distributed.py --data_dir=/mnt/data/mnist

src/ngraph_encapsulate_op.cc

@@ -36,6 +36,10 @@
 
 #include "ngraph/runtime/interpreter/int_backend.hpp"
 
+#if defined NGRAPH_DISTRIBUTED
+#include "ngraph/distributed.hpp"
+#endif
+
 using namespace std;
 namespace ng = ngraph;
 
@@ -260,8 +264,18 @@ class NGraphEncapsulateOp : public OpKernel {
 
       // Serialize to nGraph if needed
       if (std::getenv("NGRAPH_ENABLE_SERIALIZE") != nullptr) {
+        std::string file_name =
+            "tf_function_" + ctx->op_kernel().name() + ".json";
         NgraphSerialize("tf_function_" + ctx->op_kernel().name() + ".json",
                         ng_function);
+#if defined NGRAPH_DISTRIBUTED
+        ngraph::Distributed dist;
+        int Rank_ID;
+        Rank_ID = dist.get_rank();
+        NgraphSerialize("tf_function_" + ctx->op_kernel().name() + "_" +
+                            to_string(Rank_ID) + ".json",
+                        ng_function);
+#endif
       }
 
       m_ng_functions[signature] = ng_function;

src/ngraph_mark_for_clustering.cc

@@ -253,7 +253,7 @@ Status MarkForClustering(Graph* graph) {
       };
       confirmation_function_map["Greater"] = SimpleConfirmationFunction();
       confirmation_function_map["GreaterEqual"] = SimpleConfirmationFunction();
-#ifdef NGRAPH_DISTRIBUTED
+#if defined NGRAPH_DISTRIBUTED
       confirmation_function_map["HorovodAllreduce"] =
           SimpleConfirmationFunction();
 #endif
@@ -388,7 +388,7 @@ Status MarkForClustering(Graph* graph) {
       type_constraint_map["FusedBatchNormGrad"]["T"] = NGraphNumericDTypes();
       type_constraint_map["Greater"]["T"] = NGraphDTypes();
       type_constraint_map["GreaterEqual"]["T"] = NGraphDTypes();
-#ifdef NGRAPH_DISTRIBUTED
+#if defined NGRAPH_DISTRIBUTED
       type_constraint_map["HorovodAllreduce"]["T"] = NGraphNumericDTypes();
 #endif
       type_constraint_map["Identity"]["T"] = NGraphDTypes();

src/ngraph_rewrite_pass.cc

@@ -29,6 +29,10 @@
 
 #include <iomanip>
 
+#if defined NGRAPH_DISTRIBUTED
+#include "ngraph/distributed.hpp"
+#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;
+#if defined NGRAPH_DISTRIBUTED
+    ngraph::Distributed dist;
+    int Rank_ID = dist.get_rank();
+    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;
+#if defined NGRAPH_DISTRIBUTED
+    ngraph::Distributed dist;
+    int Rank_ID = dist.get_rank();
+    ss << "_" << std::setfill('0') << std::setw(4) << Rank_ID;
+#endif
     return ss.str();
   }