提交 a04d9981 编写于 作者: M minqiyang

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_python35_CI_random_fail

......@@ -104,6 +104,7 @@ visualDL --logdir=scratch_log --port=8080
# 访问 http://127.0.0.1:8080
```
如果出现`TypeError: __init__() got an unexpected keyword argument 'file'`, 是因为protobuf不是3.5以上,运行`pip install --upgrade protobuf`就能解决。
如果在虚拟环境下仍然遇到安装问题,请尝试以下方法。
......
......@@ -43,6 +43,7 @@ paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list',
paddle.fluid.global_scope ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.scope_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.Trainer.__init__ ArgSpec(args=['self', 'train_func', 'optimizer_func', 'param_path', 'place', 'parallel', 'checkpoint_config'], varargs=None, keywords=None, defaults=(None, None, False, None))
paddle.fluid.Trainer.save_inference_model ArgSpec(args=['self', 'param_path', 'feeded_var_names', 'target_var_indexes'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.save_params ArgSpec(args=['self', 'param_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Trainer.test ArgSpec(args=['self', 'reader', 'feed_order'], varargs=None, keywords=None, defaults=None)
......
......@@ -11,6 +11,7 @@
// 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 "paddle/fluid/framework/ir/fc_lstm_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/lod_tensor.h"
......
......@@ -6,6 +6,7 @@ cc_library(analysis SRCS pass_manager.cc node.cc data_flow_graph.cc graph_traits
analyzer.cc
helper.cc
# passes
analysis_pass.cc
fluid_to_data_flow_graph_pass.cc
data_flow_graph_to_fluid_pass.cc
dfg_graphviz_draw_pass.cc
......@@ -105,6 +106,6 @@ if (NOT EXISTS ${TEXT_CLASSIFICATION_INSTALL_DIR} AND WITH_TESTING AND WITH_INFE
inference_download_and_uncompress(${TEXT_CLASSIFICATION_INSTALL_DIR} ${TEXT_CLASSIFICATION_MODEL_URL} "text-classification-Senta.tar.gz")
endif()
inference_analysis_test(test_text_classification SRCS test_text_classification.cc
inference_analysis_test(test_text_classification SRCS analyzer_text_classification_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api analysis_predictor
ARGS --infer_model=${TEXT_CLASSIFICATION_INSTALL_DIR}/text-classification-Senta)
......@@ -12,4 +12,4 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
......@@ -28,10 +28,10 @@ namespace paddle {
namespace inference {
namespace analysis {
class Pass {
class AnalysisPass {
public:
Pass() = default;
virtual ~Pass() = default;
AnalysisPass() = default;
virtual ~AnalysisPass() = default;
// Mutable Pass.
virtual bool Initialize(Argument *argument) { return false; }
// Readonly Pass.
......@@ -42,23 +42,16 @@ class Pass {
virtual bool Finalize() { return false; }
// Get a Pass appropriate to print the Node this pass operates on.
virtual Pass *CreatePrinterPass(std::ostream &os,
virtual AnalysisPass *CreatePrinterPass(std::ostream &os,
const std::string &banner) const {
return nullptr;
}
// Create a debugger Pass that draw the DFG by graphviz toolkit.
virtual Pass *CreateGraphvizDebugerPass() const { return nullptr; }
virtual AnalysisPass *CreateGraphvizDebugerPass() const { return nullptr; }
virtual void Run() { LOG(FATAL) << "not valid"; }
// Run on a single Node.
virtual void Run(Node *x) { LOG(FATAL) << "not valid"; }
// Run on a single Function.
virtual void Run(Function *x) { LOG(FATAL) << "not valid"; }
// Run on a single FunctionBlock.
virtual void Run(FunctionBlock *x) { LOG(FATAL) << "not valid"; }
// Run on a single DataFlowGraph.
virtual void Run(DataFlowGraph *x) { LOG(FATAL) << "not valid"; }
virtual void Run(DataFlowGraph *x) = 0;
// Human-readable short representation.
virtual std::string repr() const = 0;
......@@ -66,29 +59,8 @@ class Pass {
virtual std::string description() const { return "No DOC"; }
};
// NodePass process on any Node types.
class NodePass : public Pass {
public:
virtual void Run(Node *node) = 0;
};
// NodePass process on any Function node types.
class FunctionPass : public Pass {
public:
virtual void Run(Function *node) = 0;
};
// NodePass process on any FunctionBlock node types.
class FunctionBlockPass : public Pass {
public:
virtual void Run(FunctionBlock *node) = 0;
};
// GraphPass processes on any GraphType.
class DataFlowGraphPass : public Pass {
public:
virtual void Run(DataFlowGraph *graph) = 0;
};
class DataFlowGraphPass : public AnalysisPass {};
} // namespace analysis
} // namespace inference
......
......@@ -15,6 +15,7 @@
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <string>
#include <vector>
#include "paddle/fluid/inference/analysis/data_flow_graph_to_fluid_pass.h"
#include "paddle/fluid/inference/analysis/dfg_graphviz_draw_pass.h"
#include "paddle/fluid/inference/analysis/fluid_to_data_flow_graph_pass.h"
......@@ -58,7 +59,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
std::string description() const override { return "DFG pass manager."; }
private:
void AddPass(const std::string& name, Pass* pass) {
void AddPass(const std::string& name, AnalysisPass* pass) {
VLOG(3) << "Adding pass " << name;
Register(name, pass);
AddGraphvizDebugerPass(pass);
......@@ -87,7 +88,7 @@ class DfgPassManagerImpl final : public DfgPassManager {
}
// Add the graphviz debuger pass if the parent pass has one.
void AddGraphvizDebugerPass(Pass* pass) {
void AddGraphvizDebugerPass(AnalysisPass* pass) {
auto* debuger_pass = pass->CreateGraphvizDebugerPass();
if (debuger_pass) {
Register(debuger_pass->repr(), debuger_pass);
......
......@@ -36,8 +36,11 @@ limitations under the License. */
*/
#include <gflags/gflags.h>
#include <string>
#include <vector>
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/flags.h"
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/pass_manager.h"
namespace paddle {
......
......@@ -12,13 +12,14 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/analysis/analyzer.h"
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of PADDLE_ENFORCE to avoid importing other paddle header files.
#include <gtest/gtest.h>
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/inference/analysis/analyzer.h"
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/api/timer.h"
DEFINE_string(infer_model, "", "Directory of the inference model.");
......@@ -100,10 +101,3 @@ void Main(int batch_size) {
TEST(text_classification, basic) { Main(FLAGS_batch_size); }
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(seq_concat_fc_fuse_pass);
USE_PASS(fc_lstm_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
USE_PASS(attention_lstm_fuse_pass);
......@@ -263,7 +263,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
};
} // namespace
Pass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
AnalysisPass *DataFlowGraphToFluidPass::CreateGraphvizDebugerPass() const {
return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
FLAGS_IA_graphviz_log_root,
"data_flow_graph_to_fluid_graphviz_debugger"));
......
......@@ -21,8 +21,8 @@
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
......@@ -42,7 +42,7 @@ class DataFlowGraphToFluidPass final : public DataFlowGraphPass {
return "Transform a DFG to a Fluid ProgramDesc";
}
Pass *CreateGraphvizDebugerPass() const override;
AnalysisPass *CreateGraphvizDebugerPass() const override;
protected:
// Add a Fluid Op into the ProgramDesc.
......
......@@ -21,8 +21,8 @@ limitations under the License. */
#include <fstream>
#include <string>
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/dot.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
......
......@@ -66,7 +66,7 @@ class DFG_DebuggerPass : public DFG_GraphvizDrawPass {
};
}
Pass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
AnalysisPass *FluidToDataFlowGraphPass::CreateGraphvizDebugerPass() const {
return new DFG_DebuggerPass(DFG_GraphvizDrawPass::Config(
FLAGS_IA_graphviz_log_root, "fluid-to-dfg-debuger"));
}
......
......@@ -22,8 +22,8 @@
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/data_flow_graph.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
......@@ -46,7 +46,7 @@ class FluidToDataFlowGraphPass final : public DataFlowGraphPass {
return "transform a fluid ProgramDesc to a data flow graph.";
}
Pass *CreateGraphvizDebugerPass() const override;
AnalysisPass *CreateGraphvizDebugerPass() const override;
private:
framework::proto::ProgramDesc const *desc_;
......
......@@ -14,15 +14,17 @@
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/flags.h"
#include "paddle/fluid/inference/analysis/ir_pass_manager.h"
#include "paddle/fluid/inference/analysis/pass.h"
namespace paddle {
namespace inference {
namespace analysis {
using namespace framework;
static const char kFluidToIrPassesAttr[] = "__fluid_to_ir_passes__";
......@@ -48,7 +50,8 @@ class FluidToIrPass final : public DataFlowGraphPass {
ANALYSIS_ARGUMENT_CHECK_FIELD(argument->fluid_model_program_path);
// Load program.
auto program = LoadProgramDesc(*argument->fluid_model_program_path);
argument->origin_program_desc.reset(new proto::ProgramDesc(program));
argument->origin_program_desc.reset(
new framework::proto::ProgramDesc(program));
// Create main data flow graph.
if (!argument->main_dfg) {
argument->main_dfg.reset(new DataFlowGraph);
......@@ -78,12 +81,13 @@ class FluidToIrPass final : public DataFlowGraphPass {
IRPassManager ir_passes(argument_->Get<ProgramDesc>("ir_program_desc"),
nullptr);
// Pass the scope from analysis to IR if needed.
if (argument_->Has(ir::kParamScopeAttr)) {
if (argument_->Has(framework::ir::kParamScopeAttr)) {
// Here the address is passed, attention that IR doesn't own the scope, so
// the real scope in analysis should live during the IR phase.
ir_passes.graph().Set(
ir::kParamScopeAttr,
new Scope *(&argument_->Get<Scope>(ir::kParamScopeAttr)));
framework::ir::kParamScopeAttr,
new framework::Scope *(&argument_->Get<framework::Scope>(
framework::ir::kParamScopeAttr)));
}
if (FLAGS_IA_enable_ir) {
......@@ -95,12 +99,12 @@ class FluidToIrPass final : public DataFlowGraphPass {
PADDLE_ENFORCE(argument_->main_dfg.get());
argument_->main_dfg->Build(ir_passes.graph());
// inherit the arguments from ir.
if (ir_passes.graph().Has(ir::kFuseStatisAttr)) {
if (ir_passes.graph().Has(framework::ir::kFuseStatisAttr)) {
argument_->Set(
ir::kFuseStatisAttr,
framework::ir::kFuseStatisAttr,
new std::unordered_map<std::string, int>(
ir_passes.graph().Get<std::unordered_map<std::string, int>>(
ir::kFuseStatisAttr)));
framework::ir::kFuseStatisAttr)));
}
}
......@@ -112,7 +116,7 @@ class FluidToIrPass final : public DataFlowGraphPass {
private:
// Load parameters from a single file or from a directory.
bool LoadParams(Scope *scope, const std::string &dir,
bool LoadParams(framework::Scope *scope, const std::string &dir,
const std::string &prog_file, const std::string &param_file);
private:
......
......@@ -19,7 +19,7 @@
#pragma once
#include <string>
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
namespace paddle {
namespace inference {
......
......@@ -40,17 +40,6 @@ void DfgPassManager::RunAll() {
}
}
void NodePassManager::RunAll() {
PADDLE_ENFORCE(argument_);
PADDLE_ENFORCE(argument_->main_dfg.get());
auto trait = GraphTraits<DataFlowGraph>(*argument_->main_dfg).nodes_in_DFS();
for (auto& node : trait) {
for (auto& pass : data_) {
pass->Run(&node);
}
}
}
} // namespace analysis
} // namespace inference
} // namespace paddle
......@@ -33,7 +33,7 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
namespace paddle {
namespace inference {
......@@ -43,7 +43,7 @@ namespace analysis {
* PassManager is the base class for all pass managers, a pass manager has
* several Pass-es registered, and execute them in the linear order.
*/
class PassManager : public OrderedRegistry<Pass> {
class PassManager : public OrderedRegistry<AnalysisPass> {
public:
PassManager() = default;
// Call all the passes' Initialize methods. The desc and data_flow_graph are
......@@ -89,18 +89,6 @@ class DfgPassManager : public PassManager {
virtual ~DfgPassManager() = default;
};
/*
* A pass manager that process a Node each time.
*/
class NodePassManager : public PassManager {
public:
NodePassManager() = default;
void RunAll() override;
virtual ~NodePassManager() = default;
};
} // namespace analysis
} // namespace inference
} // namespace paddle
......@@ -34,28 +34,6 @@ class TestDfgPassManager final : public DfgPassManager {
std::string description() const override { return "test doc"; }
};
class TestNodePassManager final : public NodePassManager {
public:
virtual ~TestNodePassManager() = default;
std::string repr() const override { return "test-node-pass-manager"; }
std::string description() const override { return "test doc"; }
};
class TestNodePass final : public NodePass {
public:
virtual ~TestNodePass() = default;
bool Initialize(Argument* argument) override { return true; }
void Run(Node* node) override {
LOG(INFO) << "- Processing node " << node->repr();
}
std::string repr() const override { return "test-node"; }
std::string description() const override { return "some doc"; }
};
TEST(PassManager, DFG_pass_manager) {
TestDfgPassManager manager;
DFG_GraphvizDrawPass::Config config("./", "dfg.dot");
......@@ -71,19 +49,6 @@ TEST(PassManager, DFG_pass_manager) {
manager.RunAll();
}
TEST(PassManager, Node_pass_manager) {
Argument argument(FLAGS_inference_model_dir);
// Pre-process: initialize the DFG with the ProgramDesc first.
FluidToDataFlowGraphPass pass0;
pass0.Initialize(&argument);
pass0.Run(argument.main_dfg.get());
TestNodePassManager manager;
manager.Register("test-node-pass", new TestNodePass);
ASSERT_TRUE(manager.Initialize(&argument));
manager.RunAll();
}
} // namespace analysis
} // namespace inference
} // namespace paddle
......@@ -68,7 +68,7 @@ class DfgDebuggerPass : public DFG_GraphvizDrawPass {
}
};
Pass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
AnalysisPass *TensorRTSubgraphNodeMarkPass::CreateGraphvizDebugerPass() const {
DFG_GraphvizDrawPass::Config config(FLAGS_IA_graphviz_log_root,
"tensorrt_marked_node");
return new DfgDebuggerPass(config);
......
......@@ -20,7 +20,7 @@
#pragma once
#include <string>
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
namespace paddle {
......@@ -48,7 +48,7 @@ class TensorRTSubgraphNodeMarkPass : public DataFlowGraphPass {
return "tensorrt sub-graph mark pass";
}
Pass* CreateGraphvizDebugerPass() const override;
AnalysisPass* CreateGraphvizDebugerPass() const override;
bool Finalize() override;
private:
......
......@@ -15,8 +15,8 @@ limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/inference/analysis/analysis_pass.h"
#include "paddle/fluid/inference/analysis/node.h"
#include "paddle/fluid/inference/analysis/pass.h"
#include "paddle/fluid/inference/analysis/subgraph_splitter.h"
namespace paddle {
......
......@@ -44,19 +44,7 @@ function(inference_api_test TARGET_NAME)
endfunction(inference_api_test)
cc_library(paddle_inference_api SRCS api.cc api_impl.cc helper.cc DEPS lod_tensor)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api
analysis
ir_pass_manager
pass
fc_fuse_pass
fc_lstm_fuse_pass
seq_concat_fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detector
infer_clean_graph_pass
attention_lstm_fuse_pass
)
cc_library(analysis_predictor SRCS analysis_predictor.cc DEPS paddle_inference_api analysis)
cc_test(test_paddle_inference_api
SRCS api_tester.cc
......
......@@ -30,14 +30,7 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
"Input(WeightX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasInput("WeightH"),
"Input(WeightH) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XX"), "Output(XX) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Hidden"),
"Output(Hidden) of GRU should not be null.");
......@@ -80,15 +73,20 @@ void FusionGRUOp::InferShape(framework::InferShapeContext* ctx) const {
}
framework::DDim out_dims({x_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
ctx->ShareLoD("X", "Hidden");
int xx_width;
if (ctx->Attrs().Get<bool>("use_seq")) {
xx_width = wx_dims[1];
} else {
xx_width = x_dims[1] > wx_dims[1] ? wx_dims[1] : x_dims[1];
PADDLE_ENFORCE(ctx->HasOutput("ReorderedH0"),
"Output(ReorderedH0) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedInput"),
"Output(BatchedInput) of GRU should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchedOut"),
"Output(BatchedOut) of GRU should not be null.");
ctx->SetOutputDim("BatchedInput", {x_dims[0], wx_dims[1]});
ctx->SetOutputDim("BatchedOut", out_dims);
}
ctx->SetOutputDim("XX", {x_dims[0], xx_width});
ctx->ShareLoD("X", "XX");
......
......@@ -121,6 +121,12 @@ static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
if (dlPath.find("nccl") != std::string::npos) {
std::cout
<< "You may need to install 'nccl2' from NVIDIA official website: "
<< "https://developer.nvidia.com/nccl/nccl-download"
<< "before install PaddlePaddle" << std::endl;
}
dlPath = dso_name;
dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
}
......
......@@ -500,7 +500,7 @@ EOF
EOF
if [[ ${WITH_GPU} == "ON" ]]; then
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.1.2-1+cuda${CUDA_MAJOR} libnccl-dev=2.1.2-1+cuda${CUDA_MAJOR} &&"
NCCL_DEPS="apt-get install -y --allow-downgrades libnccl2=2.2.13-1+cuda${CUDA_MAJOR} libnccl-dev=2.2.13-1+cuda${CUDA_MAJOR} &&"
else
NCCL_DEPS=""
fi
......
......@@ -104,7 +104,7 @@ def batch_images_from_tar(data_file,
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
protocol=2)
file_id += 1
data = []
labels = []
......@@ -113,9 +113,7 @@ def batch_images_from_tar(data_file,
output['label'] = labels
output['data'] = data
pickle.dump(
output,
open('%s/batch_%d' % (out_path, file_id), 'wb'),
protocol=pickle.HIGHEST_PROTOCOL)
output, open('%s/batch_%d' % (out_path, file_id), 'wb'), protocol=2)
with open(meta_file, 'a') as meta:
for file in os.listdir(out_path):
......
......@@ -47,14 +47,14 @@ def train_program():
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
return avg_loss
return [avg_loss, y_predict]
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.001)
def train(use_cuda, train_program, params_dirname):
def train(use_cuda, train_program, params_dirname, inference_model_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
trainer = fluid.Trainer(
......@@ -74,6 +74,8 @@ def train(use_cuda, train_program, params_dirname):
'''
if params_dirname is not None:
trainer.save_params(params_dirname)
trainer.save_inference_model(inference_model_dirname,
['x'], [1])
trainer.stop()
trainer.train(
......@@ -99,15 +101,55 @@ def infer(use_cuda, inference_program, params_dirname=None):
print("infer results: ", results[0])
def infer_by_saved_model(use_cuda, save_dirname=None):
if save_dirname is None:
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(save_dirname, exe)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size = 10
test_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=batch_size)
test_data = next(test_reader())
test_feat = numpy.array(
[data[0] for data in test_data]).astype("float32")
test_label = numpy.array(
[data[1] for data in test_data]).astype("float32")
assert feed_target_names[0] == 'x'
results = exe.run(inference_program,
feed={feed_target_names[0]: numpy.array(test_feat)},
fetch_list=fetch_targets)
print("infer shape: ", results[0].shape)
print("infer results: ", results[0])
print("ground truth: ", test_label)
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
# Directory for saving the trained model
params_dirname = "fit_a_line.inference.model"
params_dirname = "fit_a_line.model"
inference_model_dirname = "fit_a_line.inference_model"
train(use_cuda, train_program, params_dirname)
train(use_cuda, train_program, params_dirname, inference_model_dirname)
infer(use_cuda, inference_program, params_dirname)
infer_by_saved_model(use_cuda, inference_model_dirname)
class TestFitALine(unittest.TestCase):
......
......@@ -55,6 +55,7 @@ class TestDistRunnerBase(object):
pserver_prog = t.get_pserver_program(args.current_endpoint)
startup_prog = t.get_startup_program(args.current_endpoint,
pserver_prog)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
......@@ -147,6 +148,8 @@ def runtime_main(test_class):
import paddle.compat as cpt
import socket
from contextlib import closing
class TestDistBase(unittest.TestCase):
......@@ -156,13 +159,19 @@ class TestDistBase(unittest.TestCase):
def setUp(self):
self._trainers = 2
self._pservers = 2
self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
self._ps_endpoints = "127.0.0.1:%s,127.0.0.1:%s" % (
self._find_free_port(), self._find_free_port())
self._python_interp = "python"
self._sync_mode = True
self._mem_opt = False
self._use_reduce = False
self._setup_config()
def _find_free_port(self):
with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s:
s.bind(('', 0))
return s.getsockname()[1]
def start_pserver(self, model_file, check_error_log):
ps0_ep, ps1_ep = self._ps_endpoints.split(",")
ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
......
......@@ -53,12 +53,11 @@ class TestFusionLSTMOp(OpTest):
self.M = 8
self.D = 16
self.has_initial_state = False
self.use_peepholes = False
self.is_reverse = False
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.use_peepholes = False
self.use_seq = False
self.set_conf()
T = sum(self.lod[0])
......@@ -108,7 +107,6 @@ class TestFusionLSTMOp(OpTest):
}
self.attrs = {
'use_peepholes': self.use_peepholes,
'use_seq': self.use_seq,
'is_reverse': self.is_reverse,
'gate_activation': self.act_gate,
'cell_activation': self.act_cell,
......@@ -178,50 +176,18 @@ class TestFusionLSTMOpPeepholesReverse(TestFusionLSTMOp):
self.is_reverse = True
class TestFusionLSTMOpPoopholesBS1(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_peepholes = True
self.lod = [[3]]
self.D = 16
class TestFusionLSTMOpSeqInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
class TestFusionLSTMOpSeqReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.is_reverse = True
class TestFusionLSTMOpSeqInitReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.has_initial_state = True
self.is_reverse = True
class TestFusionLSTMOpSeqPeepholes(TestFusionLSTMOp):
class TestFusionLSTMOpPeepholesBS1(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
class TestFusionLSTMOpSeqPeepholesInit(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.has_initial_state = True
class TestFusionLSTMOpSeqPeepholesReverse(TestFusionLSTMOp):
def set_conf(self):
self.use_seq = True
self.use_peepholes = True
self.is_reverse = True
self.lod = [[2]]
self.D = 8
if __name__ == '__main__':
......
......@@ -85,6 +85,7 @@ class TestFetchOp(unittest.TestCase):
assert not math.isnan(np.sum(ret[i])) and \
not math.isinf(np.sum(ret[i]))
@unittest.skip(reason="CI timeout")
def test_fetch_op(self):
tst_reader = paddle.batch(flowers.test(use_xmap=False), batch_size=16)
tst_reader_iter = tst_reader()
......@@ -139,6 +140,7 @@ class TestFeedParallel(unittest.TestCase):
if batch_id == 2:
break
@unittest.skip(reason="CI timeout")
def test_feed_op(self):
os.environ['CPU_NUM'] = str(4)
if core.is_compiled_with_cuda():
......
......@@ -431,6 +431,28 @@ class Trainer(object):
exe = executor.Executor(self.place)
io.save_persistables(exe, dirname=param_path)
def save_inference_model(self, param_path, feeded_var_names,
target_var_indexes):
"""
Save model for cpp inference into :code:`param_path`.
Args:
param_path(str): The path to save parameters.
feeded_var_names(list(str)): The name of the vars that you
need to feed in before run program.
target_var_indexes(list(int)): the index of target var that
you need to return in trainer.train_func.
Returns:
None
"""
with self._prog_and_scope_guard():
exe = executor.Executor(self.place)
target_vars = [
self.train_func_outputs[index] for index in target_var_indexes
]
io.save_inference_model(param_path, feeded_var_names, target_vars,
exe)
@contextlib.contextmanager
def _prog_and_scope_guard(self):
with framework.program_guard(
......
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