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Add unit tests.
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@zheng-da
zheng-da committed May 7, 2018
1 parent 672594b commit 400c1b2
Showing 1 changed file with 173 additions and 5 deletions.
178 changes: 173 additions & 5 deletions tests/cpp/operator/mkldnn.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
#if MXNET_USE_MKLDNN == 1

#include "gtest/gtest.h"
#include "mxnet/imperative.h"
#include "../../src/operator/nn/mkldnn/mkldnn_base-inl.h"

using namespace mxnet;
Expand Down Expand Up @@ -97,12 +98,18 @@ static void InitArray(NDArray *arr) {
}

// Init arrays with the specified layout.
static void InitMKLDNNArray(NDArray *arr, const mkldnn::memory::primitive_desc &pd) {
static void InitMKLDNNArray(NDArray *arr, const mkldnn::memory::primitive_desc &pd,
bool is_rand = false) {
const TBlob &blob = arr->data();
mshadow::default_real_t *data = blob.dptr<mshadow::default_real_t>();
size_t size = blob.Size();
for (size_t i = 0; i < size; i++)
data[i] = i;
if (is_rand) {
for (size_t i = 0; i < size; i++)
data[i] = std::rand();
} else {
for (size_t i = 0; i < size; i++)
data[i] = i;
}
arr->MKLDNNDataReorderAsync(pd);
arr->WaitToRead();
}
Expand Down Expand Up @@ -206,7 +213,7 @@ static std::vector<mkldnn::memory::format> GetMKLDNNFormat(size_t num_dims, int
}

struct TestArrayShapes {
std::vector<TShape> shapes;
std::vector<nnvm::TShape> shapes;
std::vector<mkldnn::memory::primitive_desc> pds;
};

Expand Down Expand Up @@ -239,7 +246,7 @@ static TestArrayShapes GetTestArrayShapes() {
{
// 4D
TShape s1(4);
s1[0] = 1; s1[1] = 96; s1[2] = 54; s1[3] = 54;
s1[0] = 10; s1[1] = 96; s1[2] = 54; s1[3] = 54;
shapes.push_back(s1);
pds.push_back(GetMemPD(s1, dtype, mkldnn::memory::format::nchw));

Expand Down Expand Up @@ -332,4 +339,165 @@ TEST(MKLDNN_NDArray, GetDataReorder) {
}
}

struct OpAttrs {
nnvm::NodeAttrs attrs;
std::vector<DispatchMode> dispatches;
};

OpAttrs GetCopyOp() {
OpAttrs attrs;
attrs.attrs.op = Op::Get("_copy");
attrs.dispatches.resize(2);
attrs.dispatches[0] = DispatchMode::kFCompute;
attrs.dispatches[1] = DispatchMode::kFComputeEx;
return attrs;
}

OpAttrs GetLeakyReluOp() {
OpAttrs attrs;
attrs.attrs.op = Op::Get("LeakyReLU");
attrs.dispatches.resize(1);
attrs.dispatches[0] = DispatchMode::kFCompute;
return attrs;
}

/*
* We want to get a few types of NDArrays for testing:
* 1. Normal NDArray
* 2. Normal NDArray with MKLDNN layout (output from an MKLDNN operator)
* 3. Normal NDArray with MKLDNN layout whose MKLDNN memory may have different
* dimensions from the NDArray (result of MKLDNNDataReorderAsync). However, this
* type of NDArrays only exists for weight arrays. I don't think we should
* pass them to all operators.
* In the inference mode, the MKLDNN memory in the weight array will be
* reordered to 5 dimensions.
* 4. Reshaped/sliced NDArray
* 5. Reshaped/sliced NDArray with MKLDNN layout (reshape/slice from Normal NDArray
* with MKLDNN layout)
* 6. Reshaped/sliced NDArray with MKLDNN layout whose MKLDNN memory may have
* different dimensions from the NDArray (result of MKLDNNDataReorderAsync).
* However, this type of NDArrays only exists for weight arrays. I don't think
* we should pass them to all operators.
* In the inference mode, the MKLDNN memory in the weight array will be
* reordered to 5 dimensions.
*
*/
std::vector<NDArray> GetTestInputArrays() {
TestArrayShapes tas = GetTestArrayShapes();
std::vector<nnvm::TShape> shapes = tas.shapes;
std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;

std::vector<NDArray> in_arrs;
for (auto shape : shapes) {
in_arrs.emplace_back(shape, Context());
InitArray(&in_arrs.back());
for (auto pd : pds) {
if (shape.Size() != pd.get_size() / sizeof(mshadow::default_real_t))
continue;

in_arrs.emplace_back(shape, Context());
InitMKLDNNArray(&in_arrs.back(), pd);

// Get a sliced version.
NDArray arr(shape, Context());
InitMKLDNNArray(&arr, pd);
arr = arr.Slice(1, arr.shape()[0] - 1);
in_arrs.emplace_back(arr);
}
}
return in_arrs;
}

/*
* We want to get a few types of NDArrays for testing:
* 1. Normal NDArray
* 2. Normal NDArray with MKLDNN layout (output from an MKLDNN operator)
* 3. Normal NDArray with MKLDNN layout whose MKLDNN memory may have different
* dimensions from the NDArray (result of MKLDNNDataReorderAsync). However, this
* type of NDArrays only exists for weight arrays. I don't think we should
* pass them to all operators.
* In the inference mode, the MKLDNN memory in the weight array will be
* reordered to 5 dimensions.
* 4. Reused NDArray (this is created by the MXNet executor). This type of
* NDArrays can only be used as output arrays.
*/
std::vector<NDArray> GetTestOutputArrays(const TShape &shape,
const std::vector<mkldnn::memory::primitive_desc> &pds) {
std::vector<NDArray> in_arrs;
in_arrs.emplace_back(shape, Context());
InitArray(&in_arrs.back());

// Get a reused version.
nnvm::TShape s(1);
s[0] = shape.Size();
NDArray arr(s, Context());
arr = arr.AsArray(shape, arr.dtype());
InitArray(&arr);
in_arrs.emplace_back(arr);

for (auto pd : pds) {
if (shape.Size() != pd.get_size() / sizeof(mshadow::default_real_t))
continue;

in_arrs.emplace_back(shape, Context());
InitMKLDNNArray(&in_arrs.back(), pd, true);

// Get a reused version.
nnvm::TShape s(1);
s[0] = shape.Size();
arr = NDArray(s, Context());
arr = arr.AsArray(shape, arr.dtype());
InitMKLDNNArray(&arr, pd, true);
in_arrs.emplace_back(arr);
}
return in_arrs;
}

void TestUnaryOp(const OpAttrs &attrs) {
std::vector<NDArray*> inputs(1);
std::vector<NDArray*> outputs(1);
std::vector<OpReqType> req(1);
std::vector<DispatchMode> dispatches = attrs.dispatches;

TestArrayShapes tas = GetTestArrayShapes();
std::vector<mkldnn::memory::primitive_desc> pds = tas.pds;

std::vector<NDArray> in_arrs = GetTestInputArrays();
for (auto in_arr : in_arrs) {
for (auto dispatch : dispatches) {
std::vector<NDArray> out_arrs = GetTestOutputArrays(in_arr.shape(), pds);
for (auto out_arr : out_arrs) {
req[0] = kWriteTo;
inputs[0] = &in_arr;
outputs[0] = &out_arr;
Imperative::Get()->InvokeOp(Context(), attrs.attrs, inputs,
outputs, req, dispatch, mxnet::OpStatePtr());
out_arr.WaitToRead();
EXPECT_EQ(out_arr.IsDefaultData(), true);
}
}
}

for (auto dispatch : dispatches) {
in_arrs = GetTestInputArrays();
for (auto arr : in_arrs) {
// If the array is a view, we shouldn't write data to it.
if (arr.IsView())
continue;

req[0] = kWriteInplace;
inputs[0] = &arr;
outputs[0] = &arr;
Imperative::Get()->InvokeOp(Context(), attrs.attrs, inputs, outputs, req,
dispatch, mxnet::OpStatePtr());
arr.WaitToRead();
}
}
}

TEST(IMPERATIVE, UnaryOp) {
OpAttrs attrs = GetCopyOp();
TestUnaryOp(attrs);
}

#endif

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