1 files changed, 204 insertions, 0 deletions
diff --git a/compiler/locomotiv/src/Node/BiasAdd.test.cpp b/compiler/locomotiv/src/Node/BiasAdd.test.cpp
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+++ b/compiler/locomotiv/src/Node/BiasAdd.test.cpp
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+/*
+ * Copyright (c) 2019 Samsung Electronics Co., Ltd. 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.
+ */
+
+#include "NodeExecution.h"
+
+#include "locomotiv/NodeData.h"
+#include "NodeDataImpl.h"
+#include "NodeDomain.h"
+
+#include <nncc/core/ADT/tensor/Shape.h>
+#include <nncc/core/ADT/tensor/Buffer.h>
+#include <nncc/core/ADT/tensor/LexicalLayout.h>
+#include <nncc/core/ADT/tensor/Index.h>
+#include <nncc/core/ADT/tensor/IndexEnumerator.h>
+
+#include <gtest/gtest.h>
+
+using nncc::core::ADT::tensor::Shape;
+using nncc::core::ADT::tensor::LexicalLayout;
+using nncc::core::ADT::tensor::make_buffer;
+using nncc::core::ADT::tensor::IndexEnumerator;
+
+/*
+test case generated from the following:
+
+  inp = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
+                    shape=[1, 3, 3, 2], dtype=tf.float32)
+  bias = tf.constant([1.1, 2.1], shape=[2], dtype=tf.float32)
+  out = tf.nn.bias_add(inp, bias)
+
+  with tf.Session() as sess:
+      print(sess.run(out))
+ */
+
+TEST(NodeExecution_TensorBiasAdd, f32)
+{
+  float in_val[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  float bias_val[] = {1.1, 2.1};
+  float out_val[] = {2.1,  4.1,  4.1,  6.1,  6.1,  8.1,  8.1,  10.1, 10.1,
+                     12.1, 12.1, 14.1, 14.1, 16.1, 16.1, 18.1, 18.1, 20.1};
+
+  // make BiasAdd(Pull, Const)
+  auto g = loco::make_graph();
+  Shape input_shape{1, 3, 3, 2}; // NHWC
+
+  auto inp = g->nodes()->create<loco::Pull>();
+  {
+    inp->dtype(loco::DataType::FLOAT32);
+    inp->shape({1, 3, 3, 2});
+  }
+
+  auto bias = g->nodes()->create<loco::BiasEncode>();
+  {
+    // nothing to do
+  }
+
+  auto bias_add = g->nodes()->create<loco::BiasAdd<loco::Domain::Tensor>>();
+  {
+    bias_add->value(inp);
+    bias_add->bias(bias);
+    bias_add->axis(3); // axis(3) means C in NHWC
+  }
+
+  // Make and assign data to pull node
+  auto inp_buf = make_buffer<float, LexicalLayout>(input_shape);
+  {
+    int n = 0;
+    for (IndexEnumerator e{inp_buf.shape()}; e.valid(); e.advance())
+    {
+      inp_buf.at(e.current()) = in_val[n++];
+    }
+  }
+
+  auto bias_buf = make_buffer<float, LexicalLayout>(Shape{2});
+  {
+    int n = 0;
+    for (IndexEnumerator e{bias_buf.shape()}; e.valid(); e.advance())
+    {
+      bias_buf.at(e.current()) = bias_val[n++];
+    }
+  }
+
+  auto inp_data = locomotiv::make_data(inp_buf);
+  locomotiv::annot_data(inp, std::move(inp_data));
+  locomotiv::annot_domain(inp, loco::Domain::Tensor);
+
+  auto bias_data = locomotiv::make_data(bias_buf);
+  locomotiv::annot_data(bias, std::move(bias_data));
+  locomotiv::annot_domain(bias, loco::Domain::Bias);
+
+  locomotiv::NodeExecution::get().run(bias_add);
+
+  auto bias_add_data = locomotiv::annot_data(bias_add);
+
+  // comparing the result
+  ASSERT_NE(bias_add_data, nullptr);
+  ASSERT_EQ(bias_add_data->dtype(), loco::DataType::FLOAT32);
+  ASSERT_EQ(*(bias_add_data->shape()), Shape({1, 3, 3, 2}));
+
+  uint32_t n = 0;
+  for (IndexEnumerator e{*(bias_add_data->shape())}; e.valid(); e.advance())
+  {
+    ASSERT_FLOAT_EQ(bias_add_data->as_f32_bufptr()->at(e.current()), out_val[n++]);
+  }
+
+  ASSERT_EQ(locomotiv::annot_domain(bias_add), loco::Domain::Tensor);
+}
+
+/*
+test case generated from the following:
+
+  inp = tf.constant([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18],
+                    shape=[1, 3, 3, 2], dtype=tf.float32)
+  bias = tf.constant([1.1, 2.1], shape=[2], dtype=tf.float32)
+  out = tf.nn.bias_add(inp, bias)
+
+  with tf.Session() as sess:
+      print(sess.run(out))
+ */
+
+TEST(NodeExecution_FeatureBiasAdd, f32)
+{
+  float in_val[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18};
+  float bias_val[] = {1.1, 2.1};
+  float out_val[] = {2.1,  4.1,  4.1,  6.1,  6.1,  8.1,  8.1,  10.1, 10.1,
+                     12.1, 12.1, 14.1, 14.1, 16.1, 16.1, 18.1, 18.1, 20.1};
+
+  // make FeatureBiasAdd(FeatureEncode, BiasEncode)
+  auto g = loco::make_graph();
+  Shape input_shape{1, 3, 3, 2}; // NHWC
+
+  auto feature_encode = g->nodes()->create<loco::FeatureEncode>();
+  {
+    // setting values is ignored for testing
+  }
+
+  auto bias = g->nodes()->create<loco::BiasEncode>();
+  {
+    // nothing to do
+  }
+
+  auto feature_bias_add = g->nodes()->create<loco::BiasAdd<loco::Domain::Feature>>();
+  {
+    feature_bias_add->value(feature_encode);
+    feature_bias_add->bias(bias);
+  }
+
+  // Make and assign data to pull node
+  auto inp_buf = make_buffer<float, LexicalLayout>(input_shape);
+  {
+    int n = 0;
+    for (IndexEnumerator e{inp_buf.shape()}; e.valid(); e.advance())
+    {
+      inp_buf.at(e.current()) = in_val[n++];
+    }
+  }
+
+  auto bias_buf = make_buffer<float, LexicalLayout>(Shape{2});
+  {
+    int n = 0;
+    for (IndexEnumerator e{bias_buf.shape()}; e.valid(); e.advance())
+    {
+      bias_buf.at(e.current()) = bias_val[n++];
+    }
+  }
+
+  auto inp_data = locomotiv::make_data(inp_buf);
+  locomotiv::annot_data(feature_encode, std::move(inp_data));
+  locomotiv::annot_domain(feature_encode, loco::Domain::Feature);
+
+  auto bias_data = locomotiv::make_data(bias_buf);
+  locomotiv::annot_data(bias, std::move(bias_data));
+  locomotiv::annot_domain(bias, loco::Domain::Bias);
+
+  locomotiv::NodeExecution::get().run(feature_bias_add);
+
+  auto bias_add_data = locomotiv::annot_data(feature_bias_add);
+
+  // comparing the result
+  ASSERT_NE(bias_add_data, nullptr);
+  ASSERT_EQ(bias_add_data->dtype(), loco::DataType::FLOAT32);
+  ASSERT_EQ(*(bias_add_data->shape()), Shape({1, 3, 3, 2}));
+
+  uint32_t n = 0;
+  for (IndexEnumerator e{*(bias_add_data->shape())}; e.valid(); e.advance())
+  {
+    ASSERT_FLOAT_EQ(bias_add_data->as_f32_bufptr()->at(e.current()), out_val[n++]);
+  }
+
+  ASSERT_EQ(locomotiv::annot_domain(feature_bias_add), loco::Domain::Feature);
+}