Shrestha/upgrade r17 to master (#189)

Upgrade to master:

Added bfloat tests
Fixed version to correctly reflect v0.17.0-rc3

Author: Shrestha Malik

ngraph_bridge/version.cc

@@ -32,7 +32,7 @@
 // candidate such as v0.7.0-rc0
 // The code in master will always have the last released version number
 // with a suffix of '-master'
-#define NG_TF_VERSION_SUFFIX "-rc2"
+#define NG_TF_VERSION_SUFFIX "-rc3"
 
 #define VERSION_STR_HELPER(x) #x
 #define VERSION_STR(x) VERSION_STR_HELPER(x)

test/CMakeLists.txt

@@ -109,6 +109,7 @@ if (NGRAPH_PLAIDML_ENABLE)
 endif()
 
 add_subdirectory(python)
+add_subdirectory(python/bfloat16)
 add_subdirectory(model_level_tests)
 
 if (DEFINED NGRAPH_TF_INSTALL_PREFIX)

test/python/bfloat16/CMakeLists.txt

@@ -0,0 +1,23 @@
+# Copyright 2019 Nervana Systems Inc.
+# 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.
+
+cmake_minimum_required(VERSION 3.4)
+
+file(GLOB files RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.py")
+foreach(file ${files})
+    execute_process(
+    COMMAND ${CMAKE_COMMAND} -E create_symlink
+        ${CMAKE_CURRENT_SOURCE_DIR}/${file}
+        ${CMAKE_CURRENT_BINARY_DIR}/${file}
+)
+endforeach()

test/python/bfloat16/test_conv2d.py

@@ -0,0 +1,107 @@
+# ==============================================================================
+#  Copyright 2019 Intel Corporation
+#
+#  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.
+# ==============================================================================
+"""nGraph TensorFlow bridge Conv2d operation test
+
+"""
+import pytest
+
+import tensorflow as tf
+import numpy as np
+import os
+
+import ngraph_bridge
+
+#Test Ngraph Op Convolution, TF Op:conv2d
+# Implemented based on NNP's unit test TEST(test_assign_layout, convolution_special_case)
+
+np.random.seed(5)
+
+# Colvolution Op is placed on NNP and conerted to
+# bfloat16 only for the special case below, otherwise it falls
+# back to CPU for compute
+# Check to assure:
+# The input rank is 4-D
+# The stride is less than the filter size
+# The Window and Data dilation is {1,1}
+# Filter shape is allowed
+# If any fail, then we should place Op on CPU for compute
+
+#Inputs
+N = 1
+C = 1
+H = 3
+W = 5
+
+filter_size = np.random.rand(1, 1, 1, 2)
+input_size_nhwc = [N, H, W, C]
+input_size_nchw = [N, C, H, W]
+input_nhwc = tf.placeholder(tf.float32, shape=input_size_nhwc, name='x')
+input_nchw = tf.placeholder(tf.float32, shape=input_size_nchw, name='x')
+
+n_np = np.random.rand(*input_size_nchw).astype('f')
+#Tensorflow supports only NHWC, change input shapes from NCHW to NHWC
+t_np = np.transpose(n_np, (0, 2, 3, 1))
+
+
+#TF graph
+def tf_model():
+    stride_nhwc = [1, 2, 2, 1]
+    x = tf.cast(input_nhwc, dtype=tf.bfloat16)
+    filter_cast = tf.cast(filter_size, dtype=tf.bfloat16)
+    m = tf.nn.conv2d(
+        x, filter_cast, stride_nhwc, "SAME", data_format="NHWC", name="m")
+    m = tf.cast(m, dtype=tf.float32)
+    return m, input_nhwc
+
+
+#Ngraph graph
+def ng_model():
+    stride_nchw = [1, 1, 2, 2]
+    m = tf.nn.conv2d(
+        input_nchw,
+        filter_size,
+        stride_nchw,
+        "SAME",
+        data_format="NCHW",
+        name="m")
+    return m, input_nchw
+
+
+config = tf.ConfigProto(
+    allow_soft_placement=True,
+    log_device_placement=False,
+    inter_op_parallelism_threads=1)
+
+
+def test_conv2d():
+    #Test 1: tf_model TF-native
+    with tf.Session(config=config) as sess_tf:
+        ngraph_bridge.disable()
+        tf_out, input_data = tf_model()
+        feed_dict = {input_data: t_np}
+        tf_outval = sess_tf.run(tf_out, feed_dict=feed_dict)
+
+    #Test 2: model2 with ngraph, NNP backend
+    with tf.Session(config=config) as sess_ng:
+        ngraph_bridge.enable()
+        ngraph_bridge.update_config(config)
+        os.environ['NGRAPH_TF_DISABLE_DEASSIGN_CLUSTERS'] = '1'
+        ng_out, input_data = ng_model()
+        feed_dict = {input_data: n_np}
+        ng_outval = sess_ng.run(ng_out, feed_dict=feed_dict)
+
+    assert np.allclose(
+        np.transpose(tf_outval, (0, 3, 1, 2)), ng_outval, rtol=0, atol=1e-02)

test/python/bfloat16/test_conv2dbackpropfilter_nchw.py

@@ -0,0 +1,116 @@
+# ==============================================================================
+#  Copyright 2019 Intel Corporation
+#
+#  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.
+# ==============================================================================
+"""nGraph TensorFlow bridge Conv2d operation test
+
+"""
+import pytest
+
+import tensorflow as tf
+import numpy as np
+import os
+from tensorflow.python.ops import nn_ops
+import ngraph_bridge
+
+#Tests Ngraph Op: ConvolutionBackpropFilters with data format NCHW
+#TF Op: conv2d_backprop_filter
+
+np.random.seed(5)
+#Inputs
+N = 1
+H = 7
+W = 6
+C = 2
+
+I = C
+O = 2
+filt_width = 3
+filt_height = 3
+
+input_sizes_nchw = [N, C, H, W]
+input_sizes_nhwc = [N, H, W, C]
+filter_size_hwio = [filt_height, filt_width, I, O]
+out_backprop_valid = [1, 2, 3, 2]
+out_backprop_same = [1, 2, 4, 3]
+out_backprop_in_sizes = {"VALID": out_backprop_valid, "SAME": out_backprop_same}
+stride_nhwc = [1, 2, 2, 1]
+stride_nchw = [1, 1, 2, 2]
+
+
+#TF graph
+def tf_model(padding):
+    t1 = tf.placeholder(dtype=tf.float32, shape=input_sizes_nhwc, name='t1')
+    t2 = tf.constant(filter_size_hwio, dtype=tf.int32, name='t2')
+    t3 = tf.placeholder(
+        dtype=tf.float32, shape=out_backprop_in_sizes[padding], name='t3')
+
+    #reshaping the out_backprop to NHWC since TF does not support NCHW
+    t3 = tf.transpose(t3, [0, 2, 3, 1])
+
+    #Cast dtype to bfloat16 for TF because NNP casts ng_model inputs
+    t1 = tf.cast(t1, dtype=tf.bfloat16)
+    t3 = tf.cast(t3, dtype=tf.bfloat16)
+
+    filt = nn_ops.conv2d_backprop_filter(
+        t1, t2, t3, stride_nhwc, padding=padding, data_format='NHWC')
+
+    #Cast dtype back to float32 similar to NNP
+    filt = tf.cast(filt, dtype=tf.float32)
+    return filt, t1, t3
+
+
+#Ngraph Graph
+def ng_model(padding):
+    t1 = tf.placeholder(dtype=tf.float32, shape=input_sizes_nchw, name='t1')
+    t2 = tf.constant(filter_size_hwio, dtype=tf.int32, name='t2')
+    t3 = tf.placeholder(
+        dtype=tf.float32, shape=out_backprop_in_sizes[padding], name='t3')
+
+    filt = nn_ops.conv2d_backprop_filter(
+        t1, t2, t3, stride_nchw, padding=padding, data_format='NCHW')
+    return filt, t1, t3
+
+
+config = tf.ConfigProto(
+    allow_soft_placement=True,
+    log_device_placement=False,
+    inter_op_parallelism_threads=1)
+
+
+@pytest.mark.parametrize("padding", ("VALID", "SAME"))
+def test_conv2dbackpropfilter_nchw(padding):
+    n_np_inp = np.random.rand(*input_sizes_nchw).astype('f')
+    n_np_out = np.random.rand(*out_backprop_in_sizes[padding]).astype('f')
+
+    #Reshape to NHWC for TF
+    t_np_inp = np.transpose(n_np_inp, (0, 2, 3, 1))
+    t_np_out = np.transpose(n_np_out, (0, 2, 3, 1))
+
+    with tf.Session(config=config) as sess_tf:
+        ngraph_bridge.disable()
+        tf_out, input_data, out_backprop = tf_model(padding)
+        feed_dict = {input_data: t_np_inp, out_backprop: t_np_out}
+        tf_outval = sess_tf.run(tf_out, feed_dict=feed_dict)
+
+    #Test 2: model2 with ngraph, NNP backend
+    with tf.Session(config=config) as sess_ng:
+        ngraph_bridge.enable()
+        ngraph_bridge.update_config(config)
+        os.environ['NGRAPH_TF_DISABLE_DEASSIGN_CLUSTERS'] = '1'
+        ng_out, input_data, out_backprop = ng_model(padding)
+        feed_dict = {input_data: n_np_inp, out_backprop: n_np_out}
+        ng_outval = sess_ng.run(ng_out, feed_dict=feed_dict)
+
+    assert np.allclose(tf_outval, ng_outval, rtol=0, atol=1e-02)

test/python/bfloat16/test_conv2dbackpropfilter_nhwc.py

@@ -0,0 +1,107 @@
+# ==============================================================================
+#  Copyright 2019 Intel Corporation
+#
+#  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.
+# ==============================================================================
+"""nGraph TensorFlow bridge Conv2d operation test
+
+"""
+import pytest
+
+import tensorflow as tf
+import numpy as np
+import os
+from tensorflow.python.ops import nn_ops
+import ngraph_bridge
+
+#Tests Ngraph Op: ConvolutionBackpropFilters with data format NHWC
+#TF Op: conv2d_backprop_filter
+
+np.random.seed(5)
+#Inputs
+N = 1
+H = 7
+W = 6
+C = 2
+
+I = C
+O = 2
+filt_width = 3
+filt_height = 3
+
+input_sizes_nhwc = [N, H, W, C]
+filter_size_hwio = [filt_height, filt_width, I, O]
+out_backprop_valid = [1, 3, 2, 2]
+out_backprop_same = [1, 4, 3, 2]
+out_backprop_in_sizes = {"VALID": out_backprop_valid, "SAME": out_backprop_same}
+stride = [1, 2, 2, 1]
+
+
+#TF graph
+def tf_model(padding):
+    t1 = tf.placeholder(dtype=tf.float32, shape=input_sizes_nhwc, name='t1')
+    t2 = tf.constant(filter_size_hwio, dtype=tf.int32, name='t1')
+    t3 = tf.placeholder(
+        dtype=tf.float32, shape=out_backprop_in_sizes[padding], name='t3')
+
+    #Cast dtype to bfloat16 for TF because NNP casts ng_model inputs
+    t1 = tf.cast(t1, dtype=tf.bfloat16)
+    t3 = tf.cast(t3, dtype=tf.bfloat16)
+
+    filt = nn_ops.conv2d_backprop_filter(
+        t1, t2, t3, stride, padding=padding, data_format='NHWC')
+
+    #Cast dtype back to float32 similar to NNP
+    filt = tf.cast(filt, dtype=tf.float32)
+    return filt, t1, t3
+
+
+#Ngraph Graph
+def ng_model(padding):
+    t1 = tf.placeholder(dtype=tf.float32, shape=input_sizes_nhwc, name='t1')
+    t2 = tf.constant(filter_size_hwio, dtype=tf.int32, name='t1')
+    t3 = tf.placeholder(
+        dtype=tf.float32, shape=out_backprop_in_sizes[padding], name='t3')
+
+    filt = nn_ops.conv2d_backprop_filter(
+        t1, t2, t3, stride, padding=padding, data_format='NHWC')
+    return filt, t1, t3
+
+
+config = tf.ConfigProto(
+    allow_soft_placement=True,
+    log_device_placement=False,
+    inter_op_parallelism_threads=1)
+
+
+@pytest.mark.parametrize("padding", ("VALID", "SAME"))
+def test_conv2dbackpropfilter_nhwc(padding):
+    np_inp = np.random.rand(*input_sizes_nhwc).astype('f')
+    np_out = np.random.rand(*out_backprop_in_sizes[padding]).astype('f')
+
+    with tf.Session(config=config) as sess_tf:
+        ngraph_bridge.disable()
+        tf_out, input_data, out_backprop = tf_model(padding)
+        feed_dict = {input_data: np_inp, out_backprop: np_out}
+        tf_outval = sess_tf.run(tf_out, feed_dict=feed_dict)
+
+    #Test 2: model2 with ngraph, NNP backend
+    with tf.Session(config=config) as sess_ng:
+        ngraph_bridge.enable()
+        ngraph_bridge.update_config(config)
+        os.environ['NGRAPH_TF_DISABLE_DEASSIGN_CLUSTERS'] = '1'
+        ng_out, input_data, out_backprop = ng_model(padding)
+        feed_dict = {input_data: np_inp, out_backprop: np_out}
+        ng_outval = sess_ng.run(ng_out, feed_dict=feed_dict)
+
+    assert np.allclose(tf_outval, ng_outval, rtol=0, atol=1e-02)