chore: import upstream snapshot with attribution

test/cpp/inference/api/ipu_word2vec_sample.cc

@@ -0,0 +1,82 @@
+/* Copyright (c) 2022 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. */
+
+/*
+ * This file contains a simple demo for how to take a model for inference with
+ * IPUs.
+ * Model: wget -q
+ * http://paddle-inference-dist.bj.bcebos.com/word2vec.inference.model.tar.gz
+ */
+
+#include <iostream>
+#include <numeric>
+#include <string>
+#include <vector>
+
+#include "glog/logging.h"
+#include "paddle/common/flags.h"
+#include "paddle/fluid/inference/api/paddle_inference_api.h"
+
+PD_DEFINE_string(infer_model, "", "Directory of the inference model.");
+
+using paddle_infer::Config;
+using paddle_infer::CreatePredictor;
+using paddle_infer::Predictor;
+
+void inference(std::string model_path,
+               bool use_ipu,
+               std::vector<float> *out_data) {
+  //# 1. Create Predictor with a config.
+  Config config;
+  config.SetModel(FLAGS_infer_model);
+  if (use_ipu) {
+    // ipu_device_num, ipu_micro_batch_size
+    config.EnableIpu(1, 4);
+  }
+  auto predictor = CreatePredictor(config);
+
+  //# 2. Prepare input/output tensor.
+  auto input_names = predictor->GetInputNames();
+  std::vector<int64_t> data{1, 2, 3, 4};
+  // For simplicity, we set all the slots with the same data.
+  for (auto input_name : input_names) {
+    auto input_tensor = predictor->GetInputHandle(input_name);
+    input_tensor->Reshape({4, 1});
+    input_tensor->CopyFromCpu(data.data());
+  }
+
+  //# 3. Run
+  predictor->Run();
+
+  //# 4. Get output.
+  auto output_names = predictor->GetOutputNames();
+  auto output_tensor = predictor->GetOutputHandle(output_names[0]);
+  std::vector<int> output_shape = output_tensor->shape();
+  int out_num = std::accumulate(
+      output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
+  out_data->resize(out_num);
+  output_tensor->CopyToCpu(out_data->data());
+}
+
+int main(int argc, char *argv[]) {
+  ::paddle::flags::ParseCommandLineFlags(&argc, &argv);
+  std::vector<float> ipu_result;
+  std::vector<float> cpu_result;
+  inference(FLAGS_infer_model, true, &ipu_result);
+  inference(FLAGS_infer_model, false, &cpu_result);
+  for (size_t i = 0; i < ipu_result.size(); i++) {
+    CHECK_NEAR(ipu_result[i], cpu_result[i], 1e-6);
+  }
+  LOG(INFO) << "Finished";
+}