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Added support for dim equals 2 in activation functions #9655

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Apr 9, 2018
Merged

Added support for dim equals 2 in activation functions #9655

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c410774
Add support for dim equals 2 in activation functions
kbinias Apr 4, 2018
76550d8
Reformat code
kbinias Apr 4, 2018
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38 changes: 25 additions & 13 deletions paddle/fluid/operators/activation_mkldnn_op.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -13,8 +13,8 @@
limitations under the License. */

#include "mkldnn.hpp"
#include "mkldnn_activation_op.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/mkldnn_activation_op.h"

namespace paddle {
namespace operators {
Expand All @@ -40,18 +40,24 @@ void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
const T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());

// get memory dim
PADDLE_ENFORCE(src->dims().size() == 4,
"Input dim must be with 4, i.e. NCHW");
PADDLE_ENFORCE(src->dims().size() == 2 || src->dims().size() == 4,
"Input dim must be with 2 or 4");
std::vector<int> src_tz = framework::vectorize2int(src->dims());

// create memory description
// TODO(kbinias-intel): support more formats
auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);

// create memory primitives
auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src_data);
auto dst_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)dst_data);
auto src_memory =
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(src_data)));
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I see that we need const_cast here because we made the type of src_data const auto* in L37. Therefore, I think that instead of removing the const decorator here, we should remove the const from the definition in L37. Am I right?

The change in my mind is something like the following:

  1. in L37, instead of

    const auto *src_data = src->template data<T>();

    we should have

    auto *src_data = src->template data<T>();

  2. Here, we could have

    mkldnn::memory src_mem = mkldnn::memory({data_md, mkldnn_engine},
       static_cast<void*>(src_data));

auto dst_memory =
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The type auto here is a little misleading. We are following Google C++ style in PaddlePaddle, so I'd thought that mkldnn::memory is a function, whose type is unknown until we check the documentation. This made me confused with that exact type auto is. How about

mkldnn::memory dst_memory = mkldnn::memory(...)

mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(dst_data)));

auto forward_desc = mkldnn::eltwise_forward::desc(
mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
Expand Down Expand Up @@ -91,15 +97,21 @@ void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
std::vector<int> src_tz = framework::vectorize2int(x->dims());

// create memory description
auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);
auto data_md = src_tz.size() == 2
? platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nc)
: platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
mkldnn::memory::format::nchw);

// create memory primitives
auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src);
auto src_memory = mkldnn::memory(
{data_md, mkldnn_engine}, static_cast<void *>(const_cast<float *>(src)));
auto diff_src_memory =
mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_src);
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(diff_src)));
auto diff_dst_memory =
mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_dst);
mkldnn::memory({data_md, mkldnn_engine},
static_cast<void *>(const_cast<float *>(diff_dst)));

auto backward_desc =
mkldnn::eltwise_backward::desc(algorithm, data_md, data_md, alpha, beta);
Expand Down
44 changes: 36 additions & 8 deletions python/paddle/fluid/tests/unittests/test_activation_op.py
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Expand Up @@ -535,9 +535,37 @@ def test_check_grad(self):


#--------------------test MKLDNN--------------------
class TestMKLDNNRelu(TestRelu):
class TestMKLDNNReluDim2(TestRelu):
def setUp(self):
super(TestMKLDNNRelu, self).setUp()
super(TestMKLDNNReluDim2, self).setUp()

self.attrs = {"use_mkldnn": True}


class TestMKLDNNTanhDim2(TestTanh):
def setUp(self):
super(TestMKLDNNTanhDim2, self).setUp()

self.attrs = {"use_mkldnn": True}


class TestMKLDNNSqrtDim2(TestSqrt):
def setUp(self):
super(TestMKLDNNSqrtDim2, self).setUp()

self.attrs = {"use_mkldnn": True}


class TestMKLDNNAbsDim2(TestAbs):
def setUp(self):
super(TestMKLDNNAbsDim2, self).setUp()

self.attrs = {"use_mkldnn": True}


class TestMKLDNNReluDim4(TestRelu):
def setUp(self):
super(TestMKLDNNReluDim4, self).setUp()

x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs
Expand All @@ -549,9 +577,9 @@ def setUp(self):
self.attrs = {"use_mkldnn": True}


class TestMKLDNNTanh(TestTanh):
class TestMKLDNNTanhDim4(TestTanh):
def setUp(self):
super(TestMKLDNNTanh, self).setUp()
super(TestMKLDNNTanhDim4, self).setUp()

self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
Expand All @@ -560,9 +588,9 @@ def setUp(self):
self.attrs = {"use_mkldnn": True}


class TestMKLDNNSqrt(TestSqrt):
class TestMKLDNNSqrtDim4(TestSqrt):
def setUp(self):
super(TestMKLDNNSqrt, self).setUp()
super(TestMKLDNNSqrtDim4, self).setUp()

self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
Expand All @@ -571,9 +599,9 @@ def setUp(self):
self.attrs = {"use_mkldnn": True}


class TestMKLDNNAbs(TestAbs):
class TestMKLDNNAbsDim4(TestAbs):
def setUp(self):
super(TestMKLDNNAbs, self).setUp()
super(TestMKLDNNAbsDim4, self).setUp()

x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs
Expand Down