diff --git a/core/general-client/src/general_model.cpp b/core/general-client/src/general_model.cpp
index 55571cef4c7511ed98dadb45d2b7a55103101cf7..1f16ce5a42710aa531a2dbfd2ae4d518a80fd6ad 100644
--- a/core/general-client/src/general_model.cpp
+++ b/core/general-client/src/general_model.cpp
@@ -214,7 +214,7 @@ int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
VLOG(2) << "predict done.";
client_infer_end = timeline.TimeStampUS();
postprocess_start = client_infer_end;
- // severaal model output
+ // multi-model output
uint32_t model_num = res.outputs_size();
predict_res._models.resize(model_num);
for (uint32_t m_idx = 0; m_idx < model_num; ++m_idx) {
@@ -242,9 +242,10 @@ int PredictorClient::predict(const std::vector<std::vector<float>> &float_feed,
output.insts(0).tensor_array(idx).float_data(i);
}
}
+ //TODO
+ postprocess_end = timeline.TimeStampUS();
}
}
- postprocess_end = timeline.TimeStampUS();
}
if (FLAGS_profile_client) {
@@ -414,11 +415,11 @@ int PredictorClient::batch_predict(
output.insts(bi).tensor_array(idx).float_data(i);
}
}
+ idx += 1;
}
- idx += 1;
}
+ postprocess_end = timeline.TimeStampUS();
}
- postprocess_end = timeline.TimeStampUS();
}
if (FLAGS_profile_client) {
diff --git a/core/general-server/op/bak/general_copy_op.cpp b/core/general-server/op/bak/general_copy_op.cpp
deleted file mode 100644
index a7f7d2904343bb9203fada6872994ed8916346bd..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_copy_op.cpp
+++ /dev/null
@@ -1,95 +0,0 @@
-// Copyright (c) 2019 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.
-
-#include "core/general-server/op/general_copy_op.h"
-#include <algorithm>
-#include <iostream>
-#include <memory>
-#include <sstream>
-#include "core/general-server/op/general_infer_helper.h"
-#include "core/predictor/framework/infer.h"
-#include "core/predictor/framework/memory.h"
-#include "core/util/include/timer.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-using baidu::paddle_serving::Timer;
-using baidu::paddle_serving::predictor::MempoolWrapper;
-using baidu::paddle_serving::predictor::general_model::Tensor;
-using baidu::paddle_serving::predictor::general_model::Request;
-using baidu::paddle_serving::predictor::general_model::FeedInst;
-using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
-
-int GeneralCopyOp::inference() {
- // reade request from client
- const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name());
- VLOG(2) << "precedent name: " << pre_name();
- const TensorVector *in = &input_blob->tensor_vector;
- VLOG(2) << "input size: " << in->size();
- int batch_size = input_blob->GetBatchSize();
- int input_var_num = 0;
-
- GeneralBlob *res = mutable_data<GeneralBlob>();
- TensorVector *out = &res->tensor_vector;
-
- VLOG(2) << "input batch size: " << batch_size;
- res->SetBatchSize(batch_size);
-
- if (!res) {
- LOG(ERROR) << "Failed get op tls reader object output";
- }
-
- Timer timeline;
- int64_t start = timeline.TimeStampUS();
-
- VLOG(2) << "Going to init lod tensor";
- for (int i = 0; i < in->size(); ++i) {
- paddle::PaddleTensor lod_tensor;
- CopyLod(&in->at(i), &lod_tensor);
- lod_tensor.dtype = in->at(i).dtype;
- lod_tensor.name = in->at(i).name;
- VLOG(2) << "lod tensor [" << i << "].name = " << lod_tensor.name;
- out->push_back(lod_tensor);
- }
-
- VLOG(2) << "pack done.";
-
- for (int i = 0; i < out->size(); ++i) {
- int64_t *src_ptr = static_cast<int64_t *>(in->at(i).data.data());
- out->at(i).data.Resize(out->at(i).lod[0].back() * sizeof(int64_t));
- out->at(i).shape = {out->at(i).lod[0].back(), 1};
- int64_t *tgt_ptr = static_cast<int64_t *>(out->at(i).data.data());
- for (int j = 0; j < out->at(i).lod[0].back(); ++j) {
- tgt_ptr[j] = src_ptr[j];
- }
- }
-
- VLOG(2) << "output done.";
-
- timeline.Pause();
- int64_t end = timeline.TimeStampUS();
- CopyBlobInfo(input_blob, res);
- AddBlobInfo(res, start);
- AddBlobInfo(res, end);
-
- VLOG(2) << "read data from client success";
- return 0;
-}
-
-DEFINE_OP(GeneralCopyOp);
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/bak/general_copy_op.h b/core/general-server/op/bak/general_copy_op.h
deleted file mode 100644
index 89627ffb9e4d15bbcbfa6c7fc3a608ada03dad6e..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_copy_op.h
+++ /dev/null
@@ -1,47 +0,0 @@
-// Copyright (c) 2019 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.
-
-#pragma once
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h" // NOLINT
-#endif
-#include <string>
-#include "core/general-server/general_model_service.pb.h"
-#include "core/general-server/op/general_infer_helper.h"
-#include "core/predictor/framework/resource.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-class GeneralCopyOp
- : public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
- public:
- typedef std::vector<paddle::PaddleTensor> TensorVector;
-
- DECLARE_OP(GeneralCopyOp);
-
- int inference();
-};
-
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/bak/general_dist_kv_infer_op.cpp b/core/general-server/op/bak/general_dist_kv_infer_op.cpp
deleted file mode 100755
index ac4e7bb23e9410aede4fd353099d3c90ce91bcd3..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_dist_kv_infer_op.cpp
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright (c) 2020 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.
-
-#include "core/general-server/op/general_dist_kv_infer_op.h"
-#include <algorithm>
-#include <iostream>
-#include <memory>
-#include <sstream>
-#include <unordered_map>
-#include <utility>
-#include "core/cube/cube-api/include/cube_api.h"
-#include "core/predictor/framework/infer.h"
-#include "core/predictor/framework/memory.h"
-#include "core/predictor/framework/resource.h"
-#include "core/util/include/timer.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-using baidu::paddle_serving::Timer;
-using baidu::paddle_serving::predictor::MempoolWrapper;
-using baidu::paddle_serving::predictor::general_model::Tensor;
-using baidu::paddle_serving::predictor::general_model::Response;
-using baidu::paddle_serving::predictor::general_model::Request;
-using baidu::paddle_serving::predictor::general_model::FetchInst;
-using baidu::paddle_serving::predictor::InferManager;
-using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
-
-int GeneralDistKVInferOp::inference() {
- VLOG(2) << "Going to run inference";
- const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name());
- VLOG(2) << "Get precedent op name: " << pre_name();
- GeneralBlob *output_blob = mutable_data<GeneralBlob>();
-
- if (!input_blob) {
- LOG(ERROR) << "Failed mutable depended argument, op:" << pre_name();
- return -1;
- }
-
- const TensorVector *in = &input_blob->tensor_vector;
- TensorVector *out = &output_blob->tensor_vector;
- int batch_size = input_blob->GetBatchSize();
- VLOG(2) << "input batch size: " << batch_size;
- std::vector<uint64_t> keys;
- std::vector<rec::mcube::CubeValue> values;
- int sparse_count = 0;
- int dense_count = 0;
- std::vector<std::pair<int64_t *, size_t>> dataptr_size_pairs;
- size_t key_len = 0;
- for (size_t i = 0; i < in->size(); ++i) {
- if (in->at(i).dtype != paddle::PaddleDType::INT64) {
- ++dense_count;
- continue;
- }
- ++sparse_count;
- size_t elem_num = 1;
- for (size_t s = 0; s < in->at(i).shape.size(); ++s) {
- elem_num *= in->at(i).shape[s];
- }
- key_len += elem_num;
- int64_t *data_ptr = static_cast<int64_t *>(in->at(i).data.data());
- dataptr_size_pairs.push_back(std::make_pair(data_ptr, elem_num));
- }
- keys.resize(key_len);
- int key_idx = 0;
- for (size_t i = 0; i < dataptr_size_pairs.size(); ++i) {
- std::copy(dataptr_size_pairs[i].first,
- dataptr_size_pairs[i].first + dataptr_size_pairs[i].second,
- keys.begin() + key_idx);
- key_idx += dataptr_size_pairs[i].second;
- }
- rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
- std::vector<std::string> table_names = cube->get_table_names();
- if (table_names.size() == 0) {
- LOG(ERROR) << "cube init error or cube config not given.";
- return -1;
- }
- int ret = cube->seek(table_names[0], keys, &values);
-
- if (values.size() != keys.size() || values[0].buff.size() == 0) {
- LOG(ERROR) << "cube value return null";
- }
- size_t EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
- TensorVector sparse_out;
- sparse_out.resize(sparse_count);
- TensorVector dense_out;
- dense_out.resize(dense_count);
- int cube_val_idx = 0;
- int sparse_idx = 0;
- int dense_idx = 0;
- std::unordered_map<int, int> in_out_map;
- baidu::paddle_serving::predictor::Resource &resource =
- baidu::paddle_serving::predictor::Resource::instance();
- std::shared_ptr<PaddleGeneralModelConfig> model_config =
- resource.get_general_model_config();
- for (size_t i = 0; i < in->size(); ++i) {
- if (in->at(i).dtype != paddle::PaddleDType::INT64) {
- dense_out[dense_idx] = in->at(i);
- ++dense_idx;
- continue;
- }
-
- sparse_out[sparse_idx].lod.resize(in->at(i).lod.size());
- for (size_t x = 0; x < sparse_out[sparse_idx].lod.size(); ++x) {
- sparse_out[sparse_idx].lod[x].resize(in->at(i).lod[x].size());
- std::copy(in->at(i).lod[x].begin(),
- in->at(i).lod[x].end(),
- sparse_out[sparse_idx].lod[x].begin());
- }
- sparse_out[sparse_idx].dtype = paddle::PaddleDType::FLOAT32;
- sparse_out[sparse_idx].shape.push_back(
- sparse_out[sparse_idx].lod[0].back());
- sparse_out[sparse_idx].shape.push_back(EMBEDDING_SIZE);
- sparse_out[sparse_idx].name = model_config->_feed_name[i];
- sparse_out[sparse_idx].data.Resize(sparse_out[sparse_idx].lod[0].back() *
- EMBEDDING_SIZE * sizeof(float));
- float *dst_ptr = static_cast<float *>(sparse_out[sparse_idx].data.data());
- for (int x = 0; x < sparse_out[sparse_idx].lod[0].back(); ++x) {
- float *data_ptr = dst_ptr + x * EMBEDDING_SIZE;
- memcpy(data_ptr,
- values[cube_val_idx].buff.data(),
- values[cube_val_idx].buff.size());
- cube_val_idx++;
- }
- ++sparse_idx;
- }
- TensorVector infer_in;
- infer_in.insert(infer_in.end(), dense_out.begin(), dense_out.end());
- infer_in.insert(infer_in.end(), sparse_out.begin(), sparse_out.end());
-
- output_blob->SetBatchSize(batch_size);
-
- VLOG(2) << "infer batch size: " << batch_size;
-
- Timer timeline;
- int64_t start = timeline.TimeStampUS();
- timeline.Start();
-
- if (InferManager::instance().infer(
- GENERAL_MODEL_NAME, &infer_in, out, batch_size)) {
- LOG(ERROR) << "Failed do infer in fluid model: " << GENERAL_MODEL_NAME;
- return -1;
- }
-
- int64_t end = timeline.TimeStampUS();
- CopyBlobInfo(input_blob, output_blob);
- AddBlobInfo(output_blob, start);
- AddBlobInfo(output_blob, end);
- return 0;
-}
-DEFINE_OP(GeneralDistKVInferOp);
-
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/bak/general_dist_kv_infer_op.h b/core/general-server/op/bak/general_dist_kv_infer_op.h
deleted file mode 100644
index 2dee5bca6f9e12dbb8b36a6c39aa0a8e77763d23..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_dist_kv_infer_op.h
+++ /dev/null
@@ -1,46 +0,0 @@
-// Copyright (c) 2020 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.
-
-#pragma once
-#include <string>
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h" // NOLINT
-#endif
-#include "core/general-server/general_model_service.pb.h"
-#include "core/general-server/op/general_infer_helper.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-class GeneralDistKVInferOp
- : public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
- public:
- typedef std::vector<paddle::PaddleTensor> TensorVector;
-
- DECLARE_OP(GeneralDistKVInferOp);
-
- int inference();
-};
-
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/bak/general_dist_kv_quant_infer_op.cpp b/core/general-server/op/bak/general_dist_kv_quant_infer_op.cpp
deleted file mode 100644
index 583384b79ed5ec69d14cb31b7c8239c3f786c33d..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_dist_kv_quant_infer_op.cpp
+++ /dev/null
@@ -1,198 +0,0 @@
-// Copyright (c) 2020 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.
-
-#include "core/general-server/op/general_dist_kv_quant_infer_op.h"
-#include <algorithm>
-#include <iostream>
-#include <memory>
-#include <sstream>
-#include <unordered_map>
-#include <utility>
-#include "core/cube/cube-api/include/cube_api.h"
-#include "core/predictor/framework/infer.h"
-#include "core/predictor/framework/memory.h"
-#include "core/predictor/framework/resource.h"
-#include "core/predictor/tools/quant.h"
-#include "core/util/include/timer.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-using baidu::paddle_serving::Timer;
-using baidu::paddle_serving::predictor::MempoolWrapper;
-using baidu::paddle_serving::predictor::general_model::Tensor;
-using baidu::paddle_serving::predictor::general_model::Response;
-using baidu::paddle_serving::predictor::general_model::Request;
-using baidu::paddle_serving::predictor::general_model::FetchInst;
-using baidu::paddle_serving::predictor::InferManager;
-using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
-
-int GeneralDistKVQuantInferOp::inference() {
- VLOG(2) << "Going to run inference";
- const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name());
- VLOG(2) << "Get precedent op name: " << pre_name();
- GeneralBlob *output_blob = mutable_data<GeneralBlob>();
-
- if (!input_blob) {
- LOG(ERROR) << "Failed mutable depended argument, op:" << pre_name();
- return -1;
- }
-
- const TensorVector *in = &input_blob->tensor_vector;
- TensorVector *out = &output_blob->tensor_vector;
- int batch_size = input_blob->GetBatchSize();
- VLOG(2) << "input batch size: " << batch_size;
- std::vector<uint64_t> keys;
- std::vector<rec::mcube::CubeValue> values;
- int sparse_count = 0;
- int dense_count = 0;
- std::vector<std::pair<int64_t *, size_t>> dataptr_size_pairs;
- size_t key_len = 0;
- for (size_t i = 0; i < in->size(); ++i) {
- if (in->at(i).dtype != paddle::PaddleDType::INT64) {
- ++dense_count;
- continue;
- }
- ++sparse_count;
- size_t elem_num = 1;
- for (size_t s = 0; s < in->at(i).shape.size(); ++s) {
- elem_num *= in->at(i).shape[s];
- }
- key_len += elem_num;
- int64_t *data_ptr = static_cast<int64_t *>(in->at(i).data.data());
- dataptr_size_pairs.push_back(std::make_pair(data_ptr, elem_num));
- }
- keys.resize(key_len);
- int key_idx = 0;
- for (size_t i = 0; i < dataptr_size_pairs.size(); ++i) {
- std::copy(dataptr_size_pairs[i].first,
- dataptr_size_pairs[i].first + dataptr_size_pairs[i].second,
- keys.begin() + key_idx);
- key_idx += dataptr_size_pairs[i].second;
- }
- rec::mcube::CubeAPI *cube = rec::mcube::CubeAPI::instance();
- std::vector<std::string> table_names = cube->get_table_names();
- if (table_names.size() == 0) {
- LOG(ERROR) << "cube init error or cube config not given.";
- return -1;
- }
- int ret = cube->seek(table_names[0], keys, &values);
-
- if (values.size() != keys.size() || values[0].buff.size() == 0) {
- LOG(ERROR) << "cube value return null";
- }
-
- TensorVector sparse_out;
- sparse_out.resize(sparse_count);
- TensorVector dense_out;
- dense_out.resize(dense_count);
- int cube_val_idx = 0;
- int sparse_idx = 0;
- int dense_idx = 0;
- std::unordered_map<int, int> in_out_map;
- baidu::paddle_serving::predictor::Resource &resource =
- baidu::paddle_serving::predictor::Resource::instance();
- std::shared_ptr<PaddleGeneralModelConfig> model_config =
- resource.get_general_model_config();
- int cube_quant_bits = resource.get_cube_quant_bits();
- size_t EMBEDDING_SIZE = 0;
- if (cube_quant_bits == 0) {
- EMBEDDING_SIZE = values[0].buff.size() / sizeof(float);
- } else {
- EMBEDDING_SIZE = values[0].buff.size() - 2 * sizeof(float);
- }
-
- for (size_t i = 0; i < in->size(); ++i) {
- if (in->at(i).dtype != paddle::PaddleDType::INT64) {
- dense_out[dense_idx] = in->at(i);
- ++dense_idx;
- continue;
- }
-
- sparse_out[sparse_idx].lod.resize(in->at(i).lod.size());
- for (size_t x = 0; x < sparse_out[sparse_idx].lod.size(); ++x) {
- sparse_out[sparse_idx].lod[x].resize(in->at(i).lod[x].size());
- std::copy(in->at(i).lod[x].begin(),
- in->at(i).lod[x].end(),
- sparse_out[sparse_idx].lod[x].begin());
- }
- sparse_out[sparse_idx].dtype = paddle::PaddleDType::FLOAT32;
- sparse_out[sparse_idx].shape.push_back(
- sparse_out[sparse_idx].lod[0].back());
- sparse_out[sparse_idx].shape.push_back(EMBEDDING_SIZE);
- sparse_out[sparse_idx].name = model_config->_feed_name[i];
- sparse_out[sparse_idx].data.Resize(sparse_out[sparse_idx].lod[0].back() *
- EMBEDDING_SIZE * sizeof(float));
- // END HERE
- float *dst_ptr = static_cast<float *>(sparse_out[sparse_idx].data.data());
- for (int x = 0; x < sparse_out[sparse_idx].lod[0].back(); ++x) {
- float *data_ptr = dst_ptr + x * EMBEDDING_SIZE;
- if (cube_quant_bits == 0) {
- memcpy(data_ptr,
- values[cube_val_idx].buff.data(),
- values[cube_val_idx].buff.size());
- } else {
- // min (float), max (float), num, num, num... (Byte)
- size_t num_of_float =
- values[cube_val_idx].buff.size() - 2 * sizeof(float);
- float *float_ptr = new float[num_of_float];
- char *src_ptr = new char[values[cube_val_idx].buff.size()];
- memcpy(src_ptr,
- values[cube_val_idx].buff.data(),
- values[cube_val_idx].buff.size());
- float *minmax = reinterpret_cast<float *>(src_ptr);
- dequant(src_ptr + 2 * sizeof(float),
- float_ptr,
- minmax[0],
- minmax[1],
- num_of_float,
- cube_quant_bits);
- memcpy(data_ptr, float_ptr, sizeof(float) * num_of_float);
- delete float_ptr;
- delete src_ptr;
- }
- cube_val_idx++;
- }
- ++sparse_idx;
- }
- TensorVector infer_in;
- infer_in.insert(infer_in.end(), dense_out.begin(), dense_out.end());
- infer_in.insert(infer_in.end(), sparse_out.begin(), sparse_out.end());
-
- output_blob->SetBatchSize(batch_size);
-
- VLOG(2) << "infer batch size: " << batch_size;
-
- Timer timeline;
- int64_t start = timeline.TimeStampUS();
- timeline.Start();
-
- if (InferManager::instance().infer(
- GENERAL_MODEL_NAME, &infer_in, out, batch_size)) {
- LOG(ERROR) << "Failed do infer in fluid model: " << GENERAL_MODEL_NAME;
- return -1;
- }
-
- int64_t end = timeline.TimeStampUS();
- CopyBlobInfo(input_blob, output_blob);
- AddBlobInfo(output_blob, start);
- AddBlobInfo(output_blob, end);
- return 0;
-}
-DEFINE_OP(GeneralDistKVQuantInferOp);
-
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/bak/general_dist_kv_quant_infer_op.h b/core/general-server/op/bak/general_dist_kv_quant_infer_op.h
deleted file mode 100644
index e153311a2a2e2df1bd12720e2ce6cbe9ddb31ec0..0000000000000000000000000000000000000000
--- a/core/general-server/op/bak/general_dist_kv_quant_infer_op.h
+++ /dev/null
@@ -1,46 +0,0 @@
-// Copyright (c) 2020 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.
-
-#pragma once
-#include <string>
-#include <vector>
-#ifdef BCLOUD
-#ifdef WITH_GPU
-#include "paddle/paddle_inference_api.h"
-#else
-#include "paddle/fluid/inference/api/paddle_inference_api.h"
-#endif
-#else
-#include "paddle_inference_api.h" // NOLINT
-#endif
-#include "core/general-server/general_model_service.pb.h"
-#include "core/general-server/op/general_infer_helper.h"
-
-namespace baidu {
-namespace paddle_serving {
-namespace serving {
-
-class GeneralDistKVQuantInferOp
- : public baidu::paddle_serving::predictor::OpWithChannel<GeneralBlob> {
- public:
- typedef std::vector<paddle::PaddleTensor> TensorVector;
-
- DECLARE_OP(GeneralDistKVQuantInferOp);
-
- int inference();
-};
-
-} // namespace serving
-} // namespace paddle_serving
-} // namespace baidu
diff --git a/core/general-server/op/general_infer_op.cpp b/core/general-server/op/general_infer_op.cpp
index b5ce6e3e93b45d8be7ec351e9c01a9baeaea13f5..ca00834cf8de0b01ada4a7996b9617d2370a2cc5 100644
--- a/core/general-server/op/general_infer_op.cpp
+++ b/core/general-server/op/general_infer_op.cpp
@@ -37,68 +37,39 @@ using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
int GeneralInferOp::inference() {
VLOG(2) << "Going to run inference";
- // const GeneralBlob *input_blob =
- // get_depend_argument<GeneralBlob>(pre_name());
- VLOG(2) << "try to get output_blob";
- GeneralBlob *output_blob = mutable_data<GeneralBlob>();
- fprintf(stderr, "[output] blob address %x\n", output_blob);
- TensorVector *out = &output_blob->tensor_vector;
-
- const std::vector<std::string> pre_node_names = pre_names();
- VLOG(2) << "pre node names size: " << pre_node_names.size();
+ if (pre_node_names.size() != 1) {
+ LOG(ERROR) << "This op(" << op_name() <<") can only have one predecessor op, but received " << pre_node_names.size();
+ return -1;
+ }
+ const std::string pre_name = pre_node_names[0];
- TensorVector input;
- int batch_size = 0;
- const GeneralBlob *input_blob;
- for (uint32_t i = 0; i < pre_node_names.size(); ++i) {
- VLOG(2) << "pre names[" << i << "]: " << pre_node_names[i];
- input_blob = get_depend_argument<GeneralBlob>(pre_node_names[i]);
- if (!input_blob) {
- LOG(ERROR) << "Failed mutable depended argument, op:"
- << pre_node_names[i];
- return -1;
- }
- fprintf(stderr, "[input] blob address %x\n", input_blob);
+ const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
+ VLOG(2) << "Get precedent op name: " << pre_name;
+ GeneralBlob *output_blob = mutable_data<GeneralBlob>();
- batch_size = input_blob->GetBatchSize();
- VLOG(2) << "batch size of input: " << batch_size;
- for (uint32_t j = 0; j < input_blob->tensor_vector.size(); ++j) {
- VLOG(2) << "input tensor[" << j
- << "]: " << input_blob->tensor_vector[j].name;
- input.push_back(input_blob->tensor_vector[j]);
- VLOG(2) << "add an input tensor name: "
- << input_blob->tensor_vector[j].name;
- }
+ if (!input_blob) {
+ LOG(ERROR) << "Failed mutable depended argument, op:" << pre_name;
+ return -1;
}
- const TensorVector *in = &input;
+ const TensorVector *in = &input_blob->tensor_vector;
+ TensorVector *out = &output_blob->tensor_vector;
+ int batch_size = input_blob->GetBatchSize();
+ VLOG(2) << "input batch size: " << batch_size;
- batch_size = 1;
- VLOG(2) << "infer batch size: " << batch_size;
output_blob->SetBatchSize(batch_size);
+ VLOG(2) << "infer batch size: " << batch_size;
+
Timer timeline;
int64_t start = timeline.TimeStampUS();
timeline.Start();
- VLOG(2) << "input of op " << op_name();
- for (uint32_t i = 0; i < in->size(); ++i) {
- VLOG(2) << in->at(i).name;
- }
-
- VLOG(2) << "get engine name: " << engine_name().c_str();
- if (InferManager::instance().infer(
- GeneralInferOp::engine_name().c_str(), in, out, batch_size)) {
- LOG(ERROR) << "Failed do infer in fluid model: "
- << GeneralInferOp::engine_name();
+ if (InferManager::instance().infer(GENERAL_MODEL_NAME, in, out, batch_size)) {
+ LOG(ERROR) << "Failed do infer in fluid model: " << GENERAL_MODEL_NAME;
return -1;
}
- VLOG(2) << "output of op " << op_name();
- for (uint32_t i = 0; i < out->size(); ++i) {
- VLOG(2) << out->at(i).name;
- }
-
int64_t end = timeline.TimeStampUS();
CopyBlobInfo(input_blob, output_blob);
AddBlobInfo(output_blob, start);
diff --git a/core/general-server/op/general_reader_op.cpp b/core/general-server/op/general_reader_op.cpp
index ee0a716a34bb60b64a3c7c5dfac58d7ccebf4c0b..8695da2591a30725d5b2390ad287f9ceae40052b 100644
--- a/core/general-server/op/general_reader_op.cpp
+++ b/core/general-server/op/general_reader_op.cpp
@@ -80,7 +80,6 @@ int GeneralReaderOp::inference() {
std::vector<int64_t> capacity;
GeneralBlob *res = mutable_data<GeneralBlob>();
- fprintf(stderr, "[reader] out blob address %x\n", res);
TensorVector *out = &res->tensor_vector;
res->SetBatchSize(batch_size);
diff --git a/core/general-server/op/general_response_op.cpp b/core/general-server/op/general_response_op.cpp
index a597fc1f4ee0ba4f8eec9ab9e6cd1dd854892c89..ddfeeb656ecad82084b9d118bbaa490249cdf7af 100644
--- a/core/general-server/op/general_response_op.cpp
+++ b/core/general-server/op/general_response_op.cpp
@@ -46,7 +46,7 @@ int GeneralResponseOp::inference() {
Response *res = mutable_data<Response>();
Timer timeline;
- // double resionse_time = 0.0;
+ // double response_time = 0.0;
// timeline.Start();
int64_t start = timeline.TimeStampUS();
diff --git a/core/general-server/op/general_text_response_op.cpp b/core/general-server/op/general_text_response_op.cpp
index 6e7502c896758295842935ae179320966456861a..40243c6df565ed9b3cee226e6c32cc425cae1203 100644
--- a/core/general-server/op/general_text_response_op.cpp
+++ b/core/general-server/op/general_text_response_op.cpp
@@ -36,20 +36,21 @@ using baidu::paddle_serving::predictor::InferManager;
using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
int GeneralTextResponseOp::inference() {
- const std::vector<std::string> pre_node_names = pre_names();
- VLOG(2) << "pre node names size: " << pre_node_names.size();
+ VLOG(2) << "Going to run inference";
+ //TODO: multi-predecessor
+ if (pre_node_names.size() != 1) {
+ LOG(ERROR) << "This op(" << op_name() <<") can only have one predecessor op, but received " << pre_node_names.size();
+ return -1;
+ }
+ const std::string pre_name = pre_node_names[0];
- const GeneralBlob *input_blob =
- get_depend_argument<GeneralBlob>(pre_node_names[0]);
+ const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
if (!input_blob) {
- LOG(ERROR) << "Failed mutable depended argument, op: " << pre_node_names[0];
+ LOG(ERROR) << "Failed mutable depended argument, op: " << pre_name;
return -1;
}
- LOG(ERROR) << "Error!";
- return -1;
- /*
const TensorVector *in = &input_blob->tensor_vector;
int batch_size = input_blob->GetBatchSize();
@@ -131,7 +132,7 @@ int GeneralTextResponseOp::inference() {
// TODO(guru4elephant): find more elegant way to do this
res->add_profile_time(start);
res->add_profile_time(end);
- }*/
+ }
return 0;
}
diff --git a/core/predictor/framework/channel.h b/core/predictor/framework/channel.h
index 2c9b41cb33ca48ae4896f4c90dee74736bc80091..a48368329469f36ab7881972e6a7059ab8066b5d 100644
--- a/core/predictor/framework/channel.h
+++ b/core/predictor/framework/channel.h
@@ -152,13 +152,9 @@ class OpChannel : public Channel {
// functions of derived class
- T* data() {
- LOG(INFO) << "get data from channel.";
- return &_data; }
+ T* data() { return &_data; }
- const T* data() const {
- LOG(INFO) << "get data from channel.";
- return &_data; }
+ const T* data() const { return &_data; }
Channel& operator=(const T& obj) {
_data = obj;
diff --git a/core/predictor/framework/dag.cpp b/core/predictor/framework/dag.cpp
index 6d666a4a6cd260a8eb2fc90ae1d1ad688b5755bc..ad3e9305345c2eb05672f7bf83a76579110137d9 100644
--- a/core/predictor/framework/dag.cpp
+++ b/core/predictor/framework/dag.cpp
@@ -18,9 +18,6 @@
#include "core/predictor/common/inner_common.h"
#include "core/predictor/framework/predictor_metric.h" // PredictorMetric
#include "core/predictor/op/op.h"
-#define BLOG(fmt, ...) \
- printf( \
- "[%s:%s]:%d " fmt "\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
namespace baidu {
namespace paddle_serving {
@@ -213,14 +210,11 @@ int Dag::topo_sort() {
uint32_t pnid = Dag::node_by_name(it->first)->id -
1; // 0 is reserved for begginer-op
in_egde[pnid].push_back(nid);
- BLOG("inegde[%d]: %d", pnid, nid);
+ LOG(INFO) << "inegde[" << pnid << "]: " << nid;
}
}
for (int i = 0; i < in_degree.size(); ++i) {
- BLOG("(%s) in_degree[%d]: %d",
- _index_nodes[i]->name.c_str(),
- i,
- in_degree[i]);
+ LOG(INFO) << "(" << _index_nodes[i]->name << ") in_degree[" << i << "]: " << in_degree[i];
}
int sorted_num = 0;
DagStage* stage = new (std::nothrow) DagStage();
@@ -232,10 +226,9 @@ int Dag::topo_sort() {
ss << _stages.size();
stage->name = ss.str();
stage->full_name = full_name() + NAME_DELIMITER + stage->name;
- BLOG("stage->full_name: %s", stage->full_name.c_str());
for (uint32_t nid = 0; nid < nodes_size; ++nid) {
if (in_degree[nid] == 0) {
- BLOG("nid: %d", nid);
+ LOG(INFO) << "nid:" << nid;
++sorted_num;
stage->nodes.push_back(_index_nodes[nid]);
// assign stage number after stage created
@@ -259,16 +252,15 @@ int Dag::topo_sort() {
ss << _stages.size();
stage->name = ss.str();
stage->full_name = full_name() + NAME_DELIMITER + stage->name;
- BLOG("stage->full_name: %s", stage->full_name.c_str());
for (uint32_t pi = 0; pi < pre_nodes.size(); ++pi) {
uint32_t pnid = pre_nodes[pi]->id - 1;
- BLOG("pnid: %d", pnid);
+ LOG(INFO) << "pnid: " << pnid;
for (uint32_t ei = 0; ei < in_egde[pnid].size(); ++ei) {
uint32_t nid = in_egde[pnid][ei];
--in_degree[nid];
- BLOG("nid: %d, indeg: %d", nid, in_degree[nid]);
+ LOG(INFO) << "nid: " << nid << ", indeg: " << in_degree[nid];
if (in_degree[nid] == 0) {
- BLOG("nid: %d", nid);
+ LOG(INFO) << "nid: " << nid;
++sorted_num;
stage->nodes.push_back(_index_nodes[nid]);
// assign stage number after stage created
@@ -297,7 +289,6 @@ int Dag::topo_sort() {
// ss << _stages.size();
// stage->name = ss.str();
// stage->full_name = full_name() + NAME_DELIMITER + stage->name;
- // BLOG("stage->full_name: %s", stage->full_name.c_str());
//_stages.push_back(stage);
//// assign stage number after stage created
diff --git a/core/predictor/framework/dag_view.cpp b/core/predictor/framework/dag_view.cpp
index fc763eb0366407d74c6137e1dae89ce89cc19f38..bde8084b41fee00bc95d2a35444a15258d2a12a8 100644
--- a/core/predictor/framework/dag_view.cpp
+++ b/core/predictor/framework/dag_view.cpp
@@ -21,19 +21,12 @@
#include <string>
#include "core/predictor/common/inner_common.h"
#include "core/predictor/framework/op_repository.h"
-#define BLOG(fmt, ...) \
- printf("[%s:%s]:%d " fmt "\n", \
- __FILE__, \
- __FUNCTION__, \
- __LINE__, \
- ##__VA_ARGS__);
namespace baidu {
namespace paddle_serving {
namespace predictor {
int DagView::init(Dag* dag, const std::string& service_name) {
- BLOG("DagView::init.");
_name = dag->name();
_full_name = service_name + NAME_DELIMITER + dag->name();
_bus = butil::get_object<Bus>();
@@ -85,7 +78,7 @@ int DagView::init(Dag* dag, const std::string& service_name) {
op->set_full_name(service_name + NAME_DELIMITER + node->full_name);
// Set the name of the Op as the key of the matching engine.
- BLOG("op->set_engine_name(%s)", node->name.c_str());
+ VLOG(2) << "op->set_engine_name(" << node->name.c_str() << ")";
op->set_engine_name(node->name);
vnode->conf = node;
@@ -114,7 +107,6 @@ int DagView::init(Dag* dag, const std::string& service_name) {
_view.push_back(vstage);
}
- BLOG("DagView::finish.");
return ERR_OK;
}
@@ -144,7 +136,6 @@ int DagView::deinit() {
int DagView::execute(butil::IOBufBuilder* debug_os) {
uint32_t stage_size = _view.size();
for (uint32_t si = 0; si < stage_size; si++) {
- BLOG("start to execute stage[%u] %s", si, _view[si]->full_name.c_str());
TRACEPRINTF("start to execute stage[%u]", si);
int errcode = execute_one_stage(_view[si], debug_os);
TRACEPRINTF("finish to execute stage[%u]", si);
@@ -163,16 +154,13 @@ int DagView::execute_one_stage(ViewStage* vstage,
butil::IOBufBuilder* debug_os) {
butil::Timer stage_time(butil::Timer::STARTED);
uint32_t node_size = vstage->nodes.size();
- BLOG("vstage->nodes.size(): %d", node_size);
+ VLOG(2) << "vstage->nodes.size(): " << node_size;
for (uint32_t ni = 0; ni < node_size; ni++) {
ViewNode* vnode = vstage->nodes[ni];
DagNode* conf = vnode->conf;
Op* op = vnode->op;
- BLOG("start to execute op[%s]", op->name());
- BLOG("Op engine name: %s", op->engine_name().c_str());
TRACEPRINTF("start to execute op[%s]", op->name());
int errcode = op->process(debug_os != NULL);
- BLOG("finish to execute op[%s]", op->name());
TRACEPRINTF("finish to execute op[%s]", op->name());
if (errcode < 0) {
LOG(ERROR) << "Execute failed, Op:" << op->debug_string();
diff --git a/core/predictor/framework/infer.h b/core/predictor/framework/infer.h
index eace22da79d84c5986155f29129e491ec993b418..3523df2f95ef87f67cd404f6b7c8acdc202baeea 100644
--- a/core/predictor/framework/infer.h
+++ b/core/predictor/framework/infer.h
@@ -23,9 +23,6 @@
#include "core/predictor/framework/bsf.h"
#include "core/predictor/framework/factory.h"
#include "core/predictor/framework/infer_data.h"
-#define BLOG(fmt, ...) \
- printf( \
- "[%s:%s]:%d " fmt "\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
namespace baidu {
namespace paddle_serving {
@@ -768,9 +765,7 @@ class InferManager {
}
size_t engine_num = model_toolkit_conf.engines_size();
for (size_t ei = 0; ei < engine_num; ++ei) {
- BLOG("model_toolkit_conf.engines(%d).name: %s",
- ei,
- model_toolkit_conf.engines(ei).name().c_str());
+ LOG(INFO) << "model_toolkit_conf.engines(" << ei << ").name: " << model_toolkit_conf.engines(ei).name();
std::string engine_name = model_toolkit_conf.engines(ei).name();
VersionedInferEngine* engine = new (std::nothrow) VersionedInferEngine();
if (!engine) {
@@ -851,10 +846,8 @@ class InferManager {
void* out,
uint32_t batch_size = -1) {
auto it = _map.find(model_name);
- BLOG("find model_name: %s", model_name);
if (it == _map.end()) {
LOG(WARNING) << "Cannot find engine in map, model name:" << model_name;
- BLOG("Cannot find engine in map, model name: %s", model_name);
return -1;
}
return it->second->infer(in, out, batch_size);
diff --git a/core/predictor/framework/server.cpp b/core/predictor/framework/server.cpp
index 182387c7b827f68765380800f69574248deaa611..25b4079509b5346277609648a44f8d361187708d 100644
--- a/core/predictor/framework/server.cpp
+++ b/core/predictor/framework/server.cpp
@@ -28,7 +28,6 @@
#include "core/predictor/framework/manager.h"
#include "core/predictor/framework/resource.h"
#include "core/predictor/framework/service_manager.h"
-#define BLOG(fmt, ...) printf("[%s:%s]:%d "fmt"\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
namespace baidu {
namespace paddle_serving {
@@ -86,7 +85,6 @@ int ServerManager::start_and_wait() {
boost::unordered_map<std::string, Service*>::iterator it;
for (it = _format_services.begin(); it != _format_services.end(); it++) {
- BLOG("\n\nservice name: %s", it->first.c_str());
if (_server.AddService(it->second, brpc::SERVER_DOESNT_OWN_SERVICE) != 0) {
LOG(ERROR) << "Failed to add service of format:" << it->first << "!";
return -1;
diff --git a/core/predictor/framework/service.cpp b/core/predictor/framework/service.cpp
index be82050efda64bd7626d828065574e0bb5ea6cb7..95c7db9f96a6e78522190e3f522d38669423475b 100644
--- a/core/predictor/framework/service.cpp
+++ b/core/predictor/framework/service.cpp
@@ -30,9 +30,6 @@
#include "core/predictor/framework/predictor_metric.h" // PredictorMetric
#include "core/predictor/framework/resource.h"
#include "core/predictor/framework/server.h"
-#define BLOG(fmt, ...) \
- printf( \
- "[%s:%s]:%d " fmt "\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
namespace baidu {
namespace paddle_serving {
@@ -139,7 +136,6 @@ const std::string& InferService::name() const { return _infer_service_format; }
int InferService::inference(const google::protobuf::Message* request,
google::protobuf::Message* response,
butil::IOBufBuilder* debug_os) {
- BLOG("\n=====> start to inference");
TRACEPRINTF("start to inference");
// when funtion call begins, framework will reset
// thread local variables&resources automatically.
@@ -165,7 +161,6 @@ int InferService::inference(const google::protobuf::Message* request,
return ERR_INTERNAL_FAILURE;
}
TRACEPRINTF("start to execute workflow[%s]", workflow->name().c_str());
- BLOG("start to execute workflow[%s]", workflow->name().c_str());
int errcode = _execute_workflow(workflow, request, response, debug_os);
TRACEPRINTF("finish to execute workflow[%s]", workflow->name().c_str());
if (errcode < 0) {
@@ -225,7 +220,6 @@ int InferService::_execute_workflow(Workflow* workflow,
// call actual inference interface
int errcode = dv->execute(debug_os);
- BLOG("execute_workflow");
if (errcode < 0) {
LOG(ERROR) << "Failed execute dag for workflow:" << workflow->name();
return errcode;
diff --git a/core/predictor/framework/workflow.cpp b/core/predictor/framework/workflow.cpp
index 1e2e66a95433833477089a6679d33f802b3196d1..16c4a6e9f475bf575f84bd24764d6348ac65120c 100644
--- a/core/predictor/framework/workflow.cpp
+++ b/core/predictor/framework/workflow.cpp
@@ -16,9 +16,6 @@
#include <string>
#include "core/predictor/common/inner_common.h"
#include "core/predictor/framework/predictor_metric.h" // PredictorMetric
-#define BLOG(fmt, ...) \
- printf( \
- "[%s:%s]:%d " fmt "\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
namespace baidu {
namespace paddle_serving {
@@ -54,7 +51,6 @@ DagView* Workflow::fetch_dag_view(const std::string& service_name) {
}
void Workflow::return_dag_view(DagView* view) {
- BLOG("Workflow::return_dag_vie");
view->deinit();
if (_type == "Sequence") {
butil::return_object<DagView>(view);
diff --git a/core/predictor/op/op.cpp b/core/predictor/op/op.cpp
index e4d359f2bb848bce808b3f3d2c71d24ecbc33efe..59ef6aed71977a3f762ff4fbe9480db19cb4057e 100644
--- a/core/predictor/op/op.cpp
+++ b/core/predictor/op/op.cpp
@@ -25,12 +25,6 @@
#include "core/predictor/common/utils.h"
#include "core/predictor/framework/channel.h"
#include "core/predictor/framework/dag.h"
-#define BLOG(fmt, ...) \
- printf("[%s:%s]:%d " fmt "\n", \
- __FILE__, \
- __FUNCTION__, \
- __LINE__, \
- ##__VA_ARGS__);
namespace baidu {
namespace paddle_serving {
@@ -140,7 +134,6 @@ int Op::process(bool debug) {
}
// 2. current inference
- BLOG("Op: %s->inference()", _name.c_str());
if (inference() != 0) {
return ERR_OP_INFER_FAILURE;
}
diff --git a/core/predictor/op/op.h b/core/predictor/op/op.h
index b5df8971a5e9e053a701f6dc539b2904a3e5e26b..43572b6759dea16231e7665cee0c4677d292b85f 100644
--- a/core/predictor/op/op.h
+++ b/core/predictor/op/op.h
@@ -20,8 +20,6 @@
#include "core/predictor/framework/op_repository.h"
#include "core/predictor/framework/predictor_metric.h" // PredictorMetric
#include <cstdlib>
-#define BLOG(fmt, ...) printf("[%s:%s]:%d "fmt"\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
-#include<stdexcept>
namespace baidu {
namespace paddle_serving {
@@ -231,14 +229,13 @@ class OpWithChannel : public Op {
Channel* mutable_channel() {
if (_channel != NULL) {
- LOG(INFO) << "op->mutable_data: _channel != NULL";
return _channel;
}
- LOG(INFO) << "try to get_object: _channel";
+ //TODO: some bug in using butil::get_object
//_channel = butil::get_object<ChannelType>();
- //LOG(INFO) << butil::describe_objects<ChannelType>();
_channel = new ChannelType();
+
if (!_channel) {
LOG(ERROR) << "Failed mutable channel of type:" << typeid(T).name();
return NULL;
@@ -250,11 +247,11 @@ class OpWithChannel : public Op {
const Channel* get_channel() const { return _channel; }
int release_channel() {
- LOG(INFO) << "=====> _chaneel deinit";
if (_channel) {
_channel->deinit();
delete _channel;
}
+ //TODO: some bug in using butil::get_object
/*if (_channel) {*/
//_channel->deinit();
//butil::return_object<ChannelType>(_channel);
diff --git a/core/predictor/src/pdserving.cpp b/core/predictor/src/pdserving.cpp
index 5b67500218dec6a799521471aa10362974699c19..157d52cee1adaea0524ebde01f75a90a6b2adc2f 100644
--- a/core/predictor/src/pdserving.cpp
+++ b/core/predictor/src/pdserving.cpp
@@ -32,7 +32,6 @@
#include "core/predictor/framework/server.h"
#include "core/predictor/framework/service.h"
#include "core/predictor/framework/workflow.h"
-#define BLOG(fmt, ...) printf("[%s:%s]:%d "fmt"\n", __FILE__, __FUNCTION__, __LINE__, ##__VA_ARGS__)
using baidu::paddle_serving::predictor::ServerManager;
using baidu::paddle_serving::predictor::WorkflowManager;
@@ -218,7 +217,6 @@ int main(int argc, char** argv) {
FLAGS_stderrthreshold = 3;
#endif
- BLOG("\nServerManager::instance().start_and_wait()\n");
if (ServerManager::instance().start_and_wait() != 0) {
LOG(ERROR) << "Failed start server and wait!";
return -1;
diff --git a/ensemble-demo/client.py b/ensemble-demo/client.py
deleted file mode 100644
index dc097d74a9dd4b207d9fc36e48665f851682b407..0000000000000000000000000000000000000000
--- a/ensemble-demo/client.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# Copyright (c) 2020 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.
-# pylint: disable=doc-string-missing
-from paddle_serving_client import Client
-from imdb_reader import IMDBDataset
-import sys
-import time
-
-client = Client()
-client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
-client.connect(["127.0.0.1:9393"])
-
-# you can define any english sentence or dataset here
-# This example reuses imdb reader in training, you
-# can define your own data preprocessing easily.
-imdb_dataset = IMDBDataset()
-imdb_dataset.load_resource('imdb.vocab')
-
-for i in range(500):
- line = 'i am very sad | 0'
- word_ids, label = imdb_dataset.get_words_and_label(line)
- feed = {"words": word_ids}
- fetch = ["acc", "cost", "prediction"]
- fetch_map = client.predict(feed=feed, fetch=fetch)
- print("{} {}".format(i, fetch_map["prediction"][1]))
- # time.sleep(1)
- # exit(0)
-print('0.633530199528')
diff --git a/ensemble-demo/get_data.sh b/ensemble-demo/get_data.sh
deleted file mode 100644
index 81d8d5d3b018f133c41e211d1501cf3cd9a3d8a4..0000000000000000000000000000000000000000
--- a/ensemble-demo/get_data.sh
+++ /dev/null
@@ -1,4 +0,0 @@
-wget --no-check-certificate https://fleet.bj.bcebos.com/text_classification_data.tar.gz
-wget --no-check-certificate https://paddle-serving.bj.bcebos.com/imdb-demo/imdb_model.tar.gz
-tar -zxvf text_classification_data.tar.gz
-tar -zxvf imdb_model.tar.gz
diff --git a/ensemble-demo/imdb_reader.py b/ensemble-demo/imdb_reader.py
deleted file mode 100644
index a4ef3e163a50b0dc244ac2653df1e38d7f91699b..0000000000000000000000000000000000000000
--- a/ensemble-demo/imdb_reader.py
+++ /dev/null
@@ -1,92 +0,0 @@
-# Copyright (c) 2018 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.
-# pylint: disable=doc-string-missing
-
-import sys
-import os
-import paddle
-import re
-import paddle.fluid.incubate.data_generator as dg
-
-py_version = sys.version_info[0]
-
-
-class IMDBDataset(dg.MultiSlotDataGenerator):
- def load_resource(self, dictfile):
- self._vocab = {}
- wid = 0
- if py_version == 2:
- with open(dictfile) as f:
- for line in f:
- self._vocab[line.strip()] = wid
- wid += 1
- else:
- with open(dictfile, encoding="utf-8") as f:
- for line in f:
- self._vocab[line.strip()] = wid
- wid += 1
- self._unk_id = len(self._vocab)
- self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
- self.return_value = ("words", [1, 2, 3, 4, 5, 6]), ("label", [0])
-
- def get_words_only(self, line):
- sent = line.lower().replace("<br />", " ").strip()
- words = [x for x in self._pattern.split(sent) if x and x != " "]
- feas = [
- self._vocab[x] if x in self._vocab else self._unk_id for x in words
- ]
- return feas
-
- def get_words_and_label(self, line):
- send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
- " ").strip()
- label = [int(line.split('|')[-1])]
-
- words = [x for x in self._pattern.split(send) if x and x != " "]
- feas = [
- self._vocab[x] if x in self._vocab else self._unk_id for x in words
- ]
- return feas, label
-
- def infer_reader(self, infer_filelist, batch, buf_size):
- def local_iter():
- for fname in infer_filelist:
- with open(fname, "r") as fin:
- for line in fin:
- feas, label = self.get_words_and_label(line)
- yield feas, label
-
- import paddle
- batch_iter = paddle.batch(
- paddle.reader.shuffle(
- local_iter, buf_size=buf_size),
- batch_size=batch)
- return batch_iter
-
- def generate_sample(self, line):
- def memory_iter():
- for i in range(1000):
- yield self.return_value
-
- def data_iter():
- feas, label = self.get_words_and_label(line)
- yield ("words", feas), ("label", label)
-
- return data_iter
-
-
-if __name__ == "__main__":
- imdb = IMDBDataset()
- imdb.load_resource("imdb.vocab")
- imdb.run_from_stdin()
diff --git a/ensemble-demo/server.py b/ensemble-demo/server.py
deleted file mode 100644
index 3cc145905a05320a680007cd62653c533355a446..0000000000000000000000000000000000000000
--- a/ensemble-demo/server.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright (c) 2020 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.
-# pylint: disable=doc-string-missing
-
-import os
-import sys
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpSeqMaker
-from paddle_serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-g1_infer_op = op_maker.create('general_infer', node_name='g1')
-g2_infer_op = op_maker.create('general_infer', node_name='g2')
-add_op = op_maker.create('general_add')
-response_op = op_maker.create('general_response')
-
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(g1_infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(g2_infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(add_op, dependent_nodes=[g1_infer_op, g2_infer_op])
-op_seq_maker.add_op(response_op, dependent_nodes=[add_op])
-
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-# server.load_model_config(sys.argv[1])
-model_configs = {'g1': 'imdb_bow_model', 'g2': 'imdb_bow_model'}
-# model_configs = {'g1': 'imdb_bow_model', 'g2': 'imdb_cnn_model'}
-server.load_model_config(model_configs)
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()
diff --git a/ensemble-demo/server.seq.py b/ensemble-demo/server.seq.py
deleted file mode 100644
index d8f7275fbf5bd5ba22ad4a83752f998cd0e0eb65..0000000000000000000000000000000000000000
--- a/ensemble-demo/server.seq.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# Copyright (c) 2020 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.
-# pylint: disable=doc-string-missing
-
-import os
-import sys
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpSeqMaker
-from paddle_serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-infer_op = op_maker.create('general_infer')
-response_op = op_maker.create('general_response')
-
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(response_op, dependent_nodes=[infer_op])
-
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-# server.load_model_config(sys.argv[1])
-model_configs = {'general_infer_op': 'imdb_bow_model'}
-server.load_model_config(model_configs)
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()
diff --git a/python/examples/fit_a_line/test_client.py b/python/examples/fit_a_line/test_client.py
index 9b04d0afc152be45f9af2fde9f82234c352e3952..442ed230bc3d75c9ec3b5eac160b3a53ac31cd83 100644
--- a/python/examples/fit_a_line/test_client.py
+++ b/python/examples/fit_a_line/test_client.py
@@ -29,4 +29,3 @@ test_reader = paddle.batch(
for data in test_reader():
fetch_map = client.predict(feed={"x": data[0][0]}, fetch=["price"])
print("{} {}".format(fetch_map["price"][0], data[0][1][0]))
- exit(0)
diff --git a/python/examples/fit_a_line/test_server.py b/python/examples/fit_a_line/test_server.py
index a4aa057011781b86531da0e9997b3bda0fe61641..3293be0610e497aa91fbf902e0045bdf907f0efc 100644
--- a/python/examples/fit_a_line/test_server.py
+++ b/python/examples/fit_a_line/test_server.py
@@ -21,20 +21,16 @@ from paddle_serving_server import Server
op_maker = OpMaker()
read_op = op_maker.create('general_reader')
-g1_infer_op = op_maker.create('general_infer', node_name='g1')
-g2_infer_op = op_maker.create('general_infer', node_name='g2')
+general_infer_op = op_maker.create('general_infer')
response_op = op_maker.create('general_response')
op_seq_maker = OpSeqMaker()
op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(g1_infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(g2_infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(response_op, dependent_nodes=[g1_infer_op, g2_infer_op])
+op_seq_maker.add_op(general_infer_op)
+op_seq_maker.add_op(response_op)
server = Server()
server.set_op_sequence(op_seq_maker.get_op_sequence())
-# server.load_model_config(sys.argv[1])
-model_configs = {'g1': 'uci_housing_model', 'g2': 'uci_housing_model'}
-server.load_model_config(model_configs)
+server.load_model_config(sys.argv[1])
server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
server.run_server()
diff --git a/python/paddle_serving_server/__init__.py b/python/paddle_serving_server/__init__.py
index baa20c0fd9213d6eb6191ea2ff9f953c0886940d..c4e4aaf2d182ecbf7d5cf30de1455d744b2b7428 100644
--- a/python/paddle_serving_server/__init__.py
+++ b/python/paddle_serving_server/__init__.py
@@ -34,8 +34,7 @@ class OpMaker(object):
"general_single_kv": "GeneralSingleKVOp",
"general_dist_kv_infer": "GeneralDistKVInferOp",
"general_dist_kv_quant_infer": "GeneralDistKVQuantInferOp",
- "general_copy": "GeneralCopyOp",
- "general_add": "GeneralAddOp"
+ "general_copy": "GeneralCopyOp"
}
# currently, inputs and outputs are not used