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WIP unit tests (#25)
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* WIP unit tests

* some backward items initialized
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@cjolivier01 @zheng-da
cjolivier01 authored and zheng-da committed Feb 7, 2018
1 parent 03d0283 commit d637af1
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Showing 6 changed files with 415 additions and 149 deletions.
28 changes: 24 additions & 4 deletions CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -138,7 +138,14 @@ if(USE_VTUNE)
endif()

if(USE_MKL_IF_AVAILABLE)
if(USE_MKLDNN)
add_subdirectory(3rdparty/mkldnn)
include_directories(3rdparty/mkldnn/include)
list(APPEND mxnet_LINKER_LIBS mkldnn)
set(MKL_FOUND TRUE)
else()
find_package(MKL)
endif()
if(MKL_FOUND)
include_directories(${MKL_INCLUDE_DIR})
include_directories(${CMAKE_CURRENT_SOURCE_DIR}/src/operator/mkl)
Expand Down Expand Up @@ -199,9 +206,8 @@ if(NOT MSVC AND NOT APPLE)
set(BEGIN_WHOLE_ARCHIVE -Wl,--whole-archive)
set(END_WHOLE_ARCHIVE -Wl,--no-whole-archive)
elseif(CMAKE_CXX_COMPILER_ID MATCHES "Clang")
set(BEGIN_WHOLE_ARCHIVE -Wl,-force_load) # force_load loads all symbols of the next library
#set(BEGIN_WHOLE_ARCHIVE -Wl,-all_load) # loads all symbols from all libraries
#set(END_WHOLE_ARCHIVE -Wl,-noall_load)
# using regular Clang or AppleClang
set(BEGIN_WHOLE_ARCHIVE -Wl,-force_load)
endif()

if(UNIX)
Expand Down Expand Up @@ -270,6 +276,9 @@ if(USE_OPENMP)
add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/openmp)
list(REMOVE_ITEM mxnet_LINKER_LIBS iomp5)
list(APPEND mxnet_LINKER_LIBS omp)
if(UNIX)
list(APPEND mxnet_LINKER_LIBS pthread)
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
else()
Expand Down Expand Up @@ -559,7 +568,18 @@ endif()

if(USE_OPENCV)
add_executable(im2rec "tools/im2rec.cc")
target_link_libraries(im2rec ${BEGIN_WHOLE_ARCHIVE} mxnet ${END_WHOLE_ARCHIVE} ${mxnet_LINKER_LIBS} ${OpenCV_LIBS} dmlc)
if(MSVC)
target_link_libraries(im2rec mxnet)
else()
target_link_libraries(im2rec ${BEGIN_WHOLE_ARCHIVE} mxnet_static ${END_WHOLE_ARCHIVE})
endif()
target_link_libraries(im2rec
${mxnet_LINKER_LIBS}
${OpenCV_LIBS}
dmlc
${nnvm_LINKER_LIBS}
${pslite_LINKER_LIBS}
)
endif()

target_link_libraries(mxnet PUBLIC dmlc)
Expand Down
198 changes: 168 additions & 30 deletions tests/cpp/include/test_core_op.h
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
#include <algorithm>
#include <utility>
#include <string>
#include <nnvm/node.h>
#include "./test_op.h"
#include "../../../src/imperative/imperative_utils.h"

Expand Down Expand Up @@ -326,6 +327,75 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
#endif
}

static nnvm::NodePtr GetBackwardDependency(const nnvm::NodePtr& node,
uint32_t num_inputs,
uint32_t num_outputs
//std::vector<bool> *p_save_inputs,
//std::vector<bool> *p_save_outputs
) {

const Op* op = node->op();
if(op) {
if(!op->name.empty()) {
if(op->name == "BatchNorm") {
std::cout << "Imperative::GetBackwardDependency( " << op->name << " )" << std::endl;
}
}
}

static auto& fgradient = nnvm::Op::GetAttr<nnvm::FGradient>("FGradient");
// std::vector<bool>& save_inputs = *p_save_inputs;
// std::vector<bool>& save_outputs = *p_save_outputs;
// save_inputs.resize(num_inputs);
// save_outputs.resize(num_outputs);
// std::fill(save_inputs.begin(), save_inputs.end(), false);
// std::fill(save_outputs.begin(), save_outputs.end(), false);

node->inputs.clear();
node->inputs.reserve(num_inputs);
for (uint32_t i = 0; i < num_inputs; ++i) {
node->inputs.emplace_back(nnvm::NodeEntry{nullptr, i, 0});
}

if (fgradient.count(node->op())) {
std::vector<nnvm::NodeEntry> ograd_entries;
ograd_entries.reserve(num_outputs);
for (uint32_t i = 0; i < num_outputs; ++i) {
ograd_entries.emplace_back(nnvm::NodeEntry{nullptr, i, 1});
}
const std::vector<nnvm::NodeEntry> igrad_entries = fgradient[node->op()](node, ograd_entries);

if(!igrad_entries.empty()) {
return igrad_entries[0].node;
}

// for (const auto& i : igrad_entries) {
// if (i.node == nullptr && i.version == 0) {
// save_inputs[i.index] = true;
// } else if (i.node == node) {
// save_outputs[i.index] = true;
// }
// }
// DFSVisit(igrad_entries, [&](const nnvm::NodePtr& gnode) {
// if (!gnode || gnode == node) return;
// for (const auto& i : gnode->inputs) {
// if (i.node == nullptr && i.version == 0) {
// save_inputs[i.index] = true;
// } else if (i.node == node) {
// save_outputs[i.index] = true;
// }
// }
// });
}
return nullptr;
}

nnvm::NodePtr CalcBackwardPass() const {
nnvm::NodePtr node = nnvm::Node::Create();
node->attrs = attrs_;
return GetBackwardDependency(node, inputs().size(), outputs().size());
}

/*!
* \brief Initialize the execution objects and execution data (only occurs once)
* \param args Parameter arguments
Expand All @@ -334,7 +404,8 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
void Init(const kwargs_t& in_args,
const std::vector<NDArray>& inputs = {},
const std::vector<NDArray>& outputs = {},
const CoreOpExecutor *backward_for_op = nullptr
const CoreOpExecutor *backward_for_op = nullptr,
nnvm::NodePtr bwd_node_ptr = nullptr
) {
if (!initialized_) {
initialized_ = true;
Expand All @@ -353,33 +424,73 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
op_ = nnvm::Op::Get(op_name);
CHECK_NOTNULL(op_);

nnvm::NodePtr bwd_node_ptr;
if(backward_for_op) {
bwd_node_ptr = backward_for_op->CalcBackwardPass();
}

// Set up forward
attrs_ = ParseAttrs(op_, args);

int num_inputs = op_->num_inputs;
if (op_->get_num_inputs)
if (op_->get_num_inputs) {
num_inputs = op_->get_num_inputs(attrs_);
} else if(backward_for_op) {
CHECK_NOTNULL(bwd_node_ptr.get());
num_inputs = static_cast<int>(bwd_node_ptr->inputs.size());
}

// if(backward_for_op) {
// const int num_fwd_outputs = backward_for_op->outputs().size();
// num_inputs = std::max(num_fwd_outputs, num_inputs);
// }

if (!inputs.empty()) {
CHECK_EQ(inputs.size(), static_cast<size_t>(num_inputs));
}

int inferred_num_outputs, num_visible_outputs;
int inferred_num_outputs /*, num_visible_outputs*/;

imperative::SetNumOutputs(op_, attrs_, num_inputs, &inferred_num_outputs,
&num_visible_outputs);
// imperative::SetNumOutputs(op_, attrs_, num_inputs, &inferred_num_outputs,
// &num_visible_outputs);

if (op_->get_num_outputs) {
inferred_num_outputs = op_->get_num_outputs(attrs_);
} else {
inferred_num_outputs = op_->num_outputs;
}

// static auto& finput_names = Op::GetAttr<nnvm::FListInputNames>("FListInputNames");
// if(finput_names.count(op_)) {
// std::vector<std::string> i_names = finput_names[op_](attrs_);
// const int i_name_count = i_names.size();
// num_inputs = std::max(i_name_count, num_inputs);
// }
//using FListInputNames = std::function<std::vector<std::string> (const NodeAttrs& attrs)>;

// static auto& grad_fun_map = Op::GetAttr<nnvm::FGradient>("FGradient");
// if(grad_fun_map.count(op_)) {
// auto grad_fun = grad_fun_map[op_];
// nnvm::NodePtr nodeptr = std::make_shared<nnvm::Node>();
// nodeptr->attrs = attrs_;
// std::vector<nnvm::NodeEntry> out_grads;
// std::vector<nnvm::NodeEntry> entries = grad_fun(nodeptr, out_grads);
// const int grad_count = entries.size();
// num_inputs = std::max(grad_count, num_inputs);
// }

//CHECK_GE(inferred_num_outputs, num_visible_outputs);
// Generic, all shapes the same. Probably this will need to be adjusted for more complex
// operators such as dot
std::vector<TShape> shapes;
for (size_t i = 0, n = std::max(num_visible_outputs, num_inputs); i < n; ++i) {
shapes.emplace_back(i < input_shapes_.size() ? input_shapes_[i]
: input_shapes_[input_shapes_.size() - 1]);
std::vector<TShape> input_shapes;
for (size_t i = 0, n = num_inputs; i < n; ++i) {
input_shapes.emplace_back(i < input_shapes_.size() ? input_shapes_[i]
: input_shapes_[input_shapes_.size() - 1]);
}
std::vector<NDArray *> inputs_p, outputs_p;

if (!outputs.empty()) {
CHECK_EQ(outputs.size(), static_cast<size_t>(num_visible_outputs));
CHECK_EQ(outputs.size(), static_cast<size_t>(inferred_num_outputs));
}

inputs_.reserve(num_inputs);
Expand All @@ -388,35 +499,51 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
outputs_p.reserve(inferred_num_outputs);

for (size_t i = 0; i < static_cast<size_t>(num_inputs); ++i) {
CHECK_LT(i, static_cast<int>(shapes.size()));
inputs_.emplace_back(i < inputs.size() ? inputs[i] : CreateRandArray(shapes[i],
CHECK_LT(i, static_cast<int>(input_shapes.size()));
inputs_.emplace_back(i < inputs.size() ? inputs[i] : CreateRandArray(input_shapes[i],
ctx_.run_ctx.ctx));
inputs_p.emplace_back(&*inputs_.rbegin());
}

for (size_t i = 0; i < static_cast<size_t>(inferred_num_outputs); ++i) {
// If supplied and valid, pass from the supplied outputs vector
// Otherwise use empty for forward pass, or zero-filled for backward pass
outputs_.emplace_back(i < outputs.size()
? outputs[i]
: (backward_for_op ? CreateZeroArray(shapes[i], ctx_.run_ctx.ctx)
: NDArray()));
outputs_p.emplace_back(&*outputs_.rbegin());
// Output arrays
if(outputs_.empty()) {
std::vector<nnvm::TShape> output_shapes;
static auto& finfer_shape = Op::GetAttr<nnvm::FInferShape>("FInferShape");
if (finfer_shape.count(op_)) {
nnvm::FInferShape call_infer_shapes = finfer_shape[op_];
output_shapes.resize(inferred_num_outputs);
call_infer_shapes(attrs_, &input_shapes, &output_shapes);
} else {
// TODO: this should be only if outputs param is empty
output_shapes = input_shapes;
output_shapes.resize(inferred_num_outputs);
}
CHECK_EQ(output_shapes.size(), inferred_num_outputs);
for (size_t i = 0; i < static_cast<size_t>(inferred_num_outputs); ++i) {
// If supplied and valid, pass from the supplied outputs vector
// Otherwise use empty for forward pass, or zero-filled for backward pass
outputs_.emplace_back(i < outputs.size() ? outputs[i]
: (backward_for_op
? CreateZeroArray(output_shapes[i],
ctx_.run_ctx.ctx)
: NDArray()));
outputs_p.emplace_back(&*outputs_.rbegin());
}
}

if (!backward_for_op) {
DispatchMode dispatch_mode = DispatchMode::kUndefined;
imperative::SetShapeType(ctx_.run_ctx.ctx, attrs_, inputs_p, outputs_p, &dispatch_mode);
} else {
// Backward op, so set based upon inputs
CHECK_EQ(static_cast<size_t>(num_visible_outputs), backward_for_op->inputs().size());
for (int i = 0; i < num_visible_outputs; ++i) {
CHECK_LT(static_cast<size_t>(i), shapes.size());
// backward outputs should look like forward inputs
// TODO(cjolivier01): This check fails for dot product...
// Need better inference of backward shapes
// CHECK_EQ(backward_for_op->inputs()[i].shape(), outputs_[i].shape());
}
//CHECK_EQ(static_cast<size_t>(num_visible_outputs), backward_for_op->inputs().size());
// for (int i = 0; i < num_visible_outputs; ++i) {
// CHECK_LT(static_cast<size_t>(i), input_shapes.size());
// // backward outputs should look like forward inputs
// // TODO(cjolivier01): This check fails for dot product...
// // Need better inference of backward shapes
// // CHECK_EQ(backward_for_op->inputs()[i].shape(), outputs_[i].shape());
// }
}

std::vector<OpReqType> req;
Expand Down Expand Up @@ -450,6 +577,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
if (!no_backward) {
CHECK_GE(bwd.size(), 1U)
<< "Can't automatically determine backward op name. Please specify";

for (std::pair<std::shared_ptr<CoreOpExecutor>, std::string> &bw_item : bwd) {
bw_item.first->set_verbose(verbose_);
backward_.emplace_back(bw_item.first);
Expand Down Expand Up @@ -575,6 +703,11 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
return backward_[0]->inputs();
}

const std::vector<NDArray>& bwd_inputs() const {
CHECK_EQ(backward_.size(), 1U);
return backward_[0]->inputs();
}

/*!
* \brief Backward outputs (i.e. input grad)
* \return reference to NDArray vector of backward outputs
Expand All @@ -584,6 +717,11 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>
return backward_[0]->outputs();
}

const std::vector<NDArray>& bwd_outputs() const {
CHECK_EQ(backward_.size(), 1U);
return backward_[0]->outputs();
}

void set_verbose(bool verbose) {
verbose_ = verbose;
}
Expand Down Expand Up @@ -658,7 +796,7 @@ class CoreOpExecutor : public test::op::OperatorDataInitializer<DType>

class CoreOpProp {
public:
void Init(const kwargs_t& kwargs) { kwargs_ = kwargs; }
virtual void Init(const kwargs_t& kwargs) { kwargs_ = kwargs; }
const kwargs_t& GetArgs() const { return kwargs_; }
private:
kwargs_t kwargs_;
Expand Down Expand Up @@ -687,7 +825,7 @@ inline void BasicRunCoreOpBidirectional(const bool isGPU,
const char *op_name,
const char *backward_op_name = "") {
test::op::CoreOpExecutor<DType> op(isGPU, shapes);
op.set_verbose(false);
op.set_verbose(verbose);

op.Init(op.ArgsWithOpName(op_kwargs, op_name, backward_op_name));

Expand Down
10 changes: 10 additions & 0 deletions tests/cpp/include/test_util.h
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Original file line number Diff line number Diff line change
Expand Up @@ -160,6 +160,16 @@ inline void fill(const TBlob& blob, const DType val) {
}
}

template<typename DType>
inline void try_fill(const TBlob *blob, const DType val) {
if(blob) {
DType *p1 = blob->dptr<DType>();
for (size_t i = 0, n = blob->Size(); i < n; ++i) {
*p1++ = val;
}
}
}

template<typename DType>
inline void fill(const TBlob& blob, const DType *valArray) {
DType *p1 = blob.dptr<DType>();
Expand Down
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