Update from facebook (pytorch#7451)

* [bootcamp] Improve "Shape" operator to support axes specification To improve .shape operator of Caffe2 to support x.shape(tensor, axes), which takes an optional int array "axes" as input. For example, x.shape(tensor, [1, 0]) will return the dimension for axis 1 and 0 following the specified order. For current version, "axes" input allows duplications and can have arbitrary length. * Back out "Add barrier net that runs before training nets" Original commit changeset: b373fdc9c30f. Need additional changes to some callers to support barrier failures. * Change warning to verbose log to reduce log spam The `LOG(WARNING)` was a bit spammy for regular use so lets just make it a `VLOG`. * Extract the shared code from different caffe2_benchmark binaries The OSS benchmark and Internal benchmark will share most functions in the benchmark. * Support MFR in sequence training As titled. * Make knowledge distillation work with using logged prediction feature as teacher label. 1) Add loading raw dense feature as teacher label. 2) Optional calibration function for teacher label 3) Add teacher label into generic unit test 4) Deprecated TTSN workflow version using feature_options to config teacher label * [C2/CUDA]: unjoined cross entropy sigmoid as desc * Add async_scheduling executor into deferrable_net_exec_test Add async_scheduling into tests and fix some exception cases * Fix Event disabled error When disabling event in RNN ops make sure we don't call Finish on disabled event from op's RunAsync * cuda ensure cpu output op can handle both TensorCPU and TensorCUDA as desc. * [C2 Core] Infer input device option in C2 hypothesis_test checkers Improve how we default input blob device options. Previously it defaults as where op lives but it is not necessarily the case. For example: CopyCPUToGPU * [C2 Op]SplitByLengthsOp CPU/GPU implementation [C2 Op]SplitByLengthsOp CPU/GPU implementation * fix undefined symbol error not sure why we're getting undefined symbol even with link_whole = True Need to figure out why but need this workaround for now * Add tools in DAIPlayground platform to help debugging models Add additional tools to allow Plauground override individual method defined in AnyExp. This will allow user to create module that specificly change certain default method behavior. An example included in this diff is deactivating test model and checkpointing. When debugging any model problems, switching off components helps me quickly narrow down the location of the bug. The technique is extensively used in task T27038712 (Steady memory increase in EDPM, eventually resulting in gloo/cuda.cu:34: out of memory) * add shape and type inference for int8 conversion operator * Fix flaky test for group_norm Fix flaky test for group_norm * Fix group_norm_op_test flaky Fix group_norm_op_test flaky * Implementation of composite learning rate policy In many state-of-the-arts deep learning works, people use a simple trick to schedule the learning rate: use a fixed learning rate until error plateaus and then switch to a different fixed learning rate, and so on. In this diff, we implemented a simple version of the composite learning rate. The user gives a set of learning rates policies and corresponding iteration nums, and the optimizer will change the learning rate policy based on the number of iterations so far. For example, the user give two learning rate policies, one is FixedLearningRate and PolyLearningRate, with an iteration number of 1k. Then the first 1k iteration, we use FixedLearningRate. For the following iterations, we use PolyLearningRate. * Split two use cases of CachedReader into two classes, DBFileReader and CachedReader # Use Cases: 1). input: DB file -> output: DatasetReader. Use DBFileReader. 2). input: Reader -> build cache DB file -> output: DatasetReader. Use CachedReader. # Changes to CachedReader: 1). Move db_path to the constructor. Because in mock reader. cache will always be built ahead. # Changes to tests: 1). Make a separate TestCase class for CachedReader and DBFileReader. 2). Make it possible to add more test functions by adding setUp, tearDown and _make_temp_path. 3). Make delete db_path more general. `db_path` could be a file for `log_file_db`, but could also be a directory for `leveldb`. * Back out "On Mobile phones, call GlobalInit with no arguments in predictor in case we need to perform initialization" Original commit changeset: 4489c6133f11 * Fix LARS bug Fixed a bug in the LARS implementation which caused all subsequent blobs not using LARS to have the LARS learning rate multiplier applied to them. * [tum] support sparse init & add uniformFill option as title * Propagate exception for async nets Capture the exception when an exception is thrown in async nets and re-throw it after wait(). This allows exceptions to be propagated up to the caller. This diff was a part of D7752068. We split the diff so that C2 core files changes are in a separate diff. * Automatic update of fbcode/onnx to 69894f207dfcd72d1e70497d387201cec327efbc Previous import was 403ccfbd0161c38f0834413d790bad0874afbf9a Included changes: - **[69894f2](onnx/onnx@69894f2)**: Use op schema.all tensor types in random like definitions (pytorch#865) <Scott McKay> - **[b9d6b90](onnx/onnx@b9d6b90)**: Clarify random like operators (pytorch#846) <Scott McKay> - **[fc6b5fb](onnx/onnx@fc6b5fb)**: Refactor shape inference implementation (pytorch#855) <anderspapitto> - **[b7d8dc8](onnx/onnx@b7d8dc8)**: fix cmake warning message (pytorch#863) <Eric S. Yu> - **[f585c5d](onnx/onnx@f585c5d)**: add pytorch-operator test for tile (pytorch#831) <Wenhao Hu> - **[993fe70](onnx/onnx@993fe70)**: add install step (pytorch#832) <Eric S. Yu> - **[68bc26c](onnx/onnx@68bc26c)**: add type inference for traditional ml ops except classifier ops. (pytorch#857) <Ke Zhang> - **[9cc0cda](onnx/onnx@9cc0cda)**: fix string representation of scalar types (pytorch#858) <G. Ramalingam> - **[1078925](onnx/onnx@1078925)**: fix y in pow test case to scalar (pytorch#852) <Wenhao Hu> - **[c66fb6f](onnx/onnx@c66fb6f)**: Add some math function shape inference (pytorch#845) <anderspapitto> - **[ff667d1](onnx/onnx@ff667d1)**: Refactor return type and docs for ONNXIFI_BACKEND_DIRECTX_ID (pytorch#853) <Marat Dukhan> - **[11c6876](onnx/onnx@11c6876)**: clear initializer names when clear initializer (pytorch#849) <Wenhao Hu> - **[73c34ae](onnx/onnx@73c34ae)**: Clarify FeatureVectorizer description. (pytorch#843) <Scott McKay> - **[1befb9b](onnx/onnx@1befb9b)**: Remove useless text in docs (pytorch#850) <Lu Fang> - **[e84788f](onnx/onnx@e84788f)**: Fix SELU attributes' default values (pytorch#839) <Lu Fang> - **[ebac046](onnx/onnx@ebac046)**: Add tile test case (pytorch#823) <Wenhao Hu> - **[8b7a925](onnx/onnx@8b7a925)**: a few more shape inference functions (pytorch#772) <anderspapitto> - **[9718f42](onnx/onnx@9718f42)**: Make the coefficient non optional for LinearClassifier (pytorch#836) <Jaliya Ekanayake> - **[ef083d0](onnx/onnx@ef083d0)**: Add save_tensor and load_tensor functions for Protos (pytorch#770) <Lu Fang> - **[45ceb55](onnx/onnx@45ceb55)**: Check if CMAKE_BUILD_TYPE set before project(). (pytorch#812) <Sergii Dymchenko> - **[4b3d2b0](onnx/onnx@4b3d2b0)**: [WIP] reenable shape inference tests (pytorch#834) <anderspapitto> - **[22d17ee](onnx/onnx@22d17ee)**: RNN tests: LSTM, GRU, SimpleRNN (pytorch#739) <Peyman Manikashani> - **[de65b95](onnx/onnx@de65b95)**: dimension denotation (pytorch#443) <Tian Jin> - **[eccc76e](onnx/onnx@eccc76e)**: fix field number issue in onnx operator proto and enable its build (pytorch#829) <Ke Zhang> - **[d582beb](onnx/onnx@d582beb)**: disable shape inference test to unbreak ci (pytorch#830) <Lu Fang> - **[485b787](onnx/onnx@485b787)**: function proto for composite op. (pytorch#802) <Ke Zhang> - **[cd58928](onnx/onnx@cd58928)**: specify defaults for attributes of Affine op (pytorch#820) <G. Ramalingam> - **[7ee2cf9](onnx/onnx@7ee2cf9)**: merge the dummy backend back into the main one (pytorch#743) <anderspapitto> - **[1c03a5a](onnx/onnx@1c03a5a)**: [Proposal] ONNX Interface for Framework Integration (previously ONNX Backend API) header and docs (pytorch#551) <Marat Dukhan> - **[3769a98](onnx/onnx@3769a98)**: Rename real model test case from VGG-16 to ZFNet (pytorch#821) <Lu Fang> * [C2]ReluN Op relu n op. tf reference: https://www.tensorflow.org/api_docs/python/tf/nn/relu6 * Call destructor when assigning a blob value * Add executor overrides Add executor overrides flag to enable migration to async_scheduling executor * Add barrier net that runs before training nets - attempt pytorch#2 Add a synchonize barrier net that is run before training nets. With this net, shards that are faster will wait for other shards before start training. This reduce chances of the faster shards timing out during GLOO AllReduce. Removed explicit data_parallel_model.py.synchronize call in holmes workflow. This change was landed previously but caused errors for some EDPM workflows - See https://fb.facebook.com/groups/1426530000692545/permalink/1906766366002237/ - because EDPM assumes any call to CreateOrCloneCommonWorld and Gloo ops are wrapped in exception handlers but in this case exception thrown in the barrier init net is not handled. To address this issue, we add _CreateOrCloneCommonWorld to the param_init_net instead of a new barrier init net. Since errors for param_init_net run is handled gracefully and re-rendezvous, it should fixes the problem. * Handle empty nets in async_scheduling Make sure we don't get stuck on empty nets * use CUDA_ARCH for conditional compile * [C2 fix] infer function for ensure_cpu_output_op * Update group_norm test to reduce flaky test * Fix lr_multiplier for GPU
weiyangfb · Jun 11, 2018 · b7c6d66 · b7c6d66
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diff --git a/binaries/CMakeLists.txt b/binaries/CMakeLists.txt
@@ -10,6 +10,7 @@ caffe2_binary_target("split_db.cc")
 
 caffe2_binary_target("db_throughput.cc")
 
+
 if (USE_CUDA)
   caffe2_binary_target("inspect_gpus.cc")
   target_link_libraries(inspect_gpus ${CUDA_LIBRARIES})
@@ -45,7 +46,10 @@ if (USE_OPENCV)
 endif()
 
 if (USE_OBSERVERS)
-  caffe2_binary_target("caffe2_benchmark.cc")
+  add_executable(caffe2_benchmark "caffe2_benchmark.cc" "benchmark_helper.cc")
+  target_link_libraries(caffe2_benchmark  ${Caffe2_MAIN_LIBS})
+  target_link_libraries(caffe2_benchmark ${Caffe2_MODULES})
+  install(TARGETS caffe2_benchmark DESTINATION bin)
 endif()
 
 # ---[ tutorials

diff --git a/binaries/benchmark_helper.cc b/binaries/benchmark_helper.cc
@@ -0,0 +1,250 @@
+/**
+ * Copyright (c) 2016-present, Facebook, 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.
+ */
+
+#include <string>
+
+#include "binaries/benchmark_helper.h"
+#include "caffe2/core/blob_serialization.h"
+#ifdef __CUDA_ARCH__
+#include "caffe2/core/context_gpu.h"
+#endif
+#include "caffe2/core/init.h"
+#include "caffe2/core/logging.h"
+#include "caffe2/core/net.h"
+#include "caffe2/core/operator.h"
+#include "caffe2/utils/string_utils.h"
+#include "observers/net_observer_reporter_print.h"
+#include "observers/observer_config.h"
+#include "observers/perf_observer.h"
+
+using std::shared_ptr;
+using std::string;
+using std::unique_ptr;
+using std::vector;
+
+void observerConfig() {
+  caffe2::ClearGlobalNetObservers();
+  caffe2::AddGlobalNetObserverCreator([](caffe2::NetBase* subject) {
+    return caffe2::make_unique<caffe2::PerfNetObserver>(subject);
+  });
+  caffe2::ObserverConfig::setReporter(
+      caffe2::make_unique<caffe2::NetObserverReporterPrint>());
+}
+
+bool backendCudaSet(const string& backend) {
+  bool run_on_gpu = false;
+  if (backend == "cuda") {
+#ifdef __CUDA_ARCH__
+    if (caffe2::HasCudaGPU()) {
+      run_on_gpu = true;
+    } else {
+      CAFFE_THROW("NO GPU support on this host machine");
+    }
+#else
+    CAFFE_THROW("NO GPU support");
+#endif
+  }
+  return run_on_gpu;
+}
+
+void setDeviceType(caffe2::NetDef* net_def, caffe2::DeviceType& run_dev) {
+  for (int j = 0; j < net_def->op_size(); j++) {
+    caffe2::OperatorDef* op = net_def->mutable_op(j);
+    op->mutable_device_option()->set_device_type(run_dev);
+  }
+}
+
+void setOperatorEngine(caffe2::NetDef* net_def, const string& backend) {
+  if (backend != "builtin") {
+    string engine = backend == "nnpack" ? "NNPACK"
+                                        : backend == "eigen" ? "EIGEN"
+                                                             : backend == "mkl"
+                ? "MKLDNN"
+                : backend == "cuda" ? "CUDA"
+                                    : backend == "default" ? "" : "NONE";
+    CAFFE_ENFORCE(engine != "NONE", "Backend is not supported");
+    for (int i = 0; i < net_def->op_size(); i++) {
+      caffe2::OperatorDef* op_def = net_def->mutable_op(i);
+      op_def->set_engine(engine);
+    }
+  }
+}
+
+void loadInput(
+    shared_ptr<caffe2::Workspace> workspace,
+    const bool run_on_gpu,
+    const string& input,
+    const string& input_file,
+    const string& input_dims,
+    const string& input_type) {
+  // Load input.
+  if (input.size()) {
+    vector<string> input_names = caffe2::split(',', input);
+    if (input_file.size()) {
+      vector<string> input_files = caffe2::split(',', input_file);
+      CAFFE_ENFORCE_EQ(
+          input_names.size(),
+          input_files.size(),
+          "Input name and file should have the same number.");
+      for (int i = 0; i < input_names.size(); ++i) {
+        caffe2::BlobProto blob_proto;
+        CAFFE_ENFORCE(caffe2::ReadProtoFromFile(input_files[i], &blob_proto));
+        workspace->CreateBlob(input_names[i])->Deserialize(blob_proto);
+      }
+    } else if (input_dims.size() || input_type.size()) {
+      CAFFE_ENFORCE_GE(
+          input_dims.size(),
+          0,
+          "Input dims must be specified when input tensors are used.");
+      CAFFE_ENFORCE_GE(
+          input_type.size(),
+          0,
+          "Input type must be specified when input tensors are used.");
+
+      vector<string> input_dims_list = caffe2::split(';', input_dims);
+      CAFFE_ENFORCE_EQ(
+          input_names.size(),
+          input_dims_list.size(),
+          "Input name and dims should have the same number of items.");
+      vector<string> input_type_list = caffe2::split(';', input_type);
+      CAFFE_ENFORCE_EQ(
+          input_names.size(),
+          input_type_list.size(),
+          "Input name and type should have the same number of items.");
+      for (size_t i = 0; i < input_names.size(); ++i) {
+        vector<string> input_dims_str = caffe2::split(',', input_dims_list[i]);
+        vector<int> input_dims;
+        for (const string& s : input_dims_str) {
+          input_dims.push_back(caffe2::stoi(s));
+        }
+        caffe2::Blob* blob = workspace->GetBlob(input_names[i]);
+        if (blob == nullptr) {
+          blob = workspace->CreateBlob(input_names[i]);
+        }
+        if (run_on_gpu) {
+          LOG(INFO) << "Running on GPU.";
+#ifdef __CUDA_ARCH__
+          caffe2::TensorCUDA* tensor = blob->GetMutable<caffe2::TensorCUDA>();
+          CHECK_NOTNULL(tensor);
+          tensor->Resize(input_dims);
+          if (input_type_list[i] == "uint8_t") {
+            tensor->mutable_data<uint8_t>();
+          } else if (input_type_list[i] == "float") {
+            tensor->mutable_data<float>();
+          } else {
+            CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
+          }
+#else
+          CAFFE_THROW("Not support GPU on mobile.");
+#endif
+        } else {
+          caffe2::TensorCPU* tensor = blob->GetMutable<caffe2::TensorCPU>();
+          CHECK_NOTNULL(tensor);
+          tensor->Resize(input_dims);
+          if (input_type_list[i] == "uint8_t") {
+            tensor->mutable_data<uint8_t>();
+          } else if (input_type_list[i] == "float") {
+            tensor->mutable_data<float>();
+          } else {
+            CAFFE_THROW("Unsupported input type: ", input_type_list[i]);
+          }
+        }
+      }
+    } else {
+      CAFFE_THROW(
+          "You requested input tensors, but neither input_file nor "
+          "input_dims is set.");
+    }
+  }
+}
+
+void runNetwork(
+    shared_ptr<caffe2::Workspace> workspace,
+    caffe2::NetDef& net_def,
+    const bool run_individual,
+    const int warmup,
+    const int iter) {
+  if (!net_def.has_name()) {
+    net_def.set_name("benchmark");
+  }
+
+  caffe2::NetBase* net = workspace->CreateNet(net_def);
+  CHECK_NOTNULL(net);
+
+  LOG(INFO) << "Starting benchmark.";
+  caffe2::ObserverConfig::initSampleRate(1, 1, 1, run_individual, warmup);
+  LOG(INFO) << "Running warmup runs.";
+  for (int i = 0; i < warmup; ++i) {
+    CAFFE_ENFORCE(net->Run(), "Warmup run ", i, " has failed.");
+  }
+
+  LOG(INFO) << "Main runs.";
+  CAFFE_ENFORCE(
+      iter >= 0,
+      "Number of main runs should be non negative, provided ",
+      iter,
+      ".");
+  for (int i = 0; i < iter; ++i) {
+    caffe2::ObserverConfig::initSampleRate(1, 1, 1, 0, warmup);
+    CAFFE_ENFORCE(net->Run(), "Main run ", i, " has failed.");
+    if (run_individual) {
+      caffe2::ObserverConfig::initSampleRate(1, 1, 1, 1, warmup);
+      CAFFE_ENFORCE(net->Run(), "Main run ", i, " with operator has failed.");
+    }
+  }
+}
+
+void writeOutput(
+    shared_ptr<caffe2::Workspace> workspace,
+    const bool run_on_gpu,
+    const string& output,
+    const string& output_folder,
+    const bool text_output) {
+  string output_prefix = output_folder.size() ? output_folder + "/" : "";
+  if (output.size()) {
+    vector<string> output_names = caffe2::split(',', output);
+    if (output == "*") {
+      output_names = workspace->Blobs();
+    }
+    for (const string& name : output_names) {
+      CAFFE_ENFORCE(
+          workspace->HasBlob(name),
+          "You requested a non-existing blob: ",
+          name);
+      if (text_output) {
+        if (run_on_gpu) {
+#ifdef __CUDA_ARCH__
+          writeTextOutput<caffe2::CUDAContext, caffe2::TensorCUDA>(
+              workspace->GetBlob(name)->GetMutable<caffe2::TensorCUDA>(),
+              output_prefix,
+              name);
+#else
+          CAFFE_THROW("Not support GPU.");
+#endif
+        } else {
+          writeTextOutput<caffe2::CPUContext, caffe2::TensorCPU>(
+              workspace->GetBlob(name)->GetMutable<caffe2::TensorCPU>(),
+              output_prefix,
+              name);
+        }
+      } else {
+        string serialized = workspace->GetBlob(name)->Serialize(name);
+        string output_filename = output_prefix + name;
+        caffe2::WriteStringToFile(serialized, output_filename.c_str());
+      }
+    }
+  }
+}
diff --git a/binaries/benchmark_helper.h b/binaries/benchmark_helper.h
@@ -0,0 +1,91 @@
+/**
+ * Copyright (c) 2016-present, Facebook, 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.
+ */
+#pragma once
+
+#include <string>
+
+#include "caffe2/core/blob_serialization.h"
+#include "caffe2/core/init.h"
+#include "caffe2/core/logging.h"
+#include "caffe2/core/net.h"
+#include "caffe2/core/operator.h"
+#include "caffe2/utils/string_utils.h"
+
+using std::shared_ptr;
+using std::string;
+using std::vector;
+
+template <typename ContextType, typename TensorType>
+void writeTextOutput(
+    TensorType* tensor,
+    const string& output_prefix,
+    const string& name) {
+  string output_name = output_prefix + "/" + name + ".txt";
+  caffe2::TensorSerializer<ContextType> ser;
+  caffe2::BlobProto blob_proto;
+  ser.Serialize(
+      *tensor, output_name, blob_proto.mutable_tensor(), 0, tensor->size());
+  blob_proto.set_name(output_name);
+  blob_proto.set_type("Tensor");
+  CAFFE_ENFORCE(blob_proto.has_tensor());
+  caffe2::TensorProto tensor_proto = blob_proto.tensor();
+  vector<float> data;
+  switch (tensor_proto.data_type()) {
+    case caffe2::TensorProto::FLOAT: {
+      std::copy(
+          tensor_proto.float_data().begin(),
+          tensor_proto.float_data().end(),
+          std::back_inserter(data));
+      break;
+    }
+    case caffe2::TensorProto::INT32: {
+      std::copy(
+          tensor_proto.int32_data().begin(),
+          tensor_proto.int32_data().end(),
+          std::back_inserter(data));
+      break;
+    }
+    default:
+      CAFFE_THROW("Unimplemented Blob type.");
+  }
+  std::ofstream output_file(output_name);
+  std::ostream_iterator<float> output_iterator(output_file, "\n");
+  std::copy(data.begin(), data.end(), output_iterator);
+}
+
+void observerConfig();
+bool backendCudaSet(const string&);
+void setDeviceType(caffe2::NetDef*, caffe2::DeviceType&);
+void setOperatorEngine(caffe2::NetDef*, const string&);
+void loadInput(
+    shared_ptr<caffe2::Workspace>,
+    const bool,
+    const string&,
+    const string&,
+    const string&,
+    const string&);
+void writeOutput(
+    shared_ptr<caffe2::Workspace>,
+    const bool,
+    const string&,
+    const string&,
+    const bool);
+void runNetwork(
+    shared_ptr<caffe2::Workspace>,
+    caffe2::NetDef&,
+    const bool,
+    const int,
+    const int);