Skip to content
This repository has been archived by the owner on Nov 17, 2023. It is now read-only.

Add reshape op supported by MKL-DNN #12980

Merged
merged 13 commits into from
Dec 13, 2018
Merged

Add reshape op supported by MKL-DNN #12980

Changes from 9 commits
Commits
Show all changes
13 commits
Select commit Hold shift + click to select a range
a5ed940
Add reshape op supported by MKL-DNN
huangzhiyuan Oct 26, 2018
e34944d
fix build issue
Oct 30, 2018
80c2473
fix lint
Oct 30, 2018
6bd9ec4
fix lint
Oct 31, 2018
6798a86
fix lint
Oct 31, 2018
0e008f2
fix lint
Oct 31, 2018
58a12bd
fix lint
Nov 7, 2018
2c2a7f3
Merge remote-tracking branch 'upstream/master' into mkldnn_reshape
Dec 5, 2018
b5033a0
fix lint
Dec 5, 2018
d26fd04
fix white space
Dec 6, 2018
91b9bf9
add unit test
Dec 10, 2018
890682d
merge if blocks
Dec 11, 2018
26075fe
Merge branch 'master' into mkldnn_reshape
Dec 13, 2018
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
92 changes: 75 additions & 17 deletions src/operator/tensor/matrix_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,6 +103,57 @@ DMLC_REGISTER_PARAMETER(StackParam);
DMLC_REGISTER_PARAMETER(SqueezeParam);
DMLC_REGISTER_PARAMETER(DepthToSpaceParam);

#if MXNET_USE_MKLDNN == 1
void MKLDNNReshape(const NDArray &in_data, const NDArray &out_data) {
MSHADOW_TYPE_SWITCH(in_data.dtype(), DType, {
auto this_mem = in_data.GetMKLDNNData();
auto out_dptr = out_data.data().dptr<DType>();
mkldnn::memory::primitive_desc this_pd = this_mem->get_primitive_desc();
mkldnn::memory::desc this_desc = this_pd.desc();
mkldnn::memory::dims dims(this_desc.data.dims,
this_desc.data.dims + this_desc.data.ndims);
auto this_dtype = static_cast<mkldnn::memory::data_type>(this_desc.data.data_type);
auto this_format = static_cast<mkldnn::memory::format>(GetDefaultFormat(this_desc));
mkldnn::memory::desc data_md(dims, this_dtype, this_format);
mkldnn::memory::primitive_desc pd(data_md, this_pd.get_engine());
auto temp_mem = mkldnn::memory(pd, out_dptr);
MKLDNNStream::Get()->RegisterPrim(mkldnn::reorder(*this_mem, temp_mem));
MKLDNNStream::Get()->Submit();

// Removing out_data mkl_mem_ and store data in the default format
const_cast<NDArray &>(out_data).InvalidateMKLDNNData();
});
}

static void ReshapeComputeExCPU(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<NDArray>& inputs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
CHECK_EQ(inputs.size(), 1U);
CHECK_EQ(outputs.size(), 1U);
// If inputs are supposed to be in MKLDNN format and
// MKLDNNsupport the data type or the shape. Then convert
// it to the output format and shape
if (SupportMKLDNNArray(inputs[0].dtype(), inputs[0].shape()) && req[0] != kAddTo) {
MKLDNNReshape(inputs[0], outputs[0]);
return;
}
FallBackCompute(UnaryOp::IdentityCompute<cpu>, attrs, ctx, inputs, req, outputs);
}

inline static bool ReshapeStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int>* in_attrs,
std::vector<int>* out_attrs) {
CHECK_EQ(in_attrs->size(), 1U);
CHECK_EQ(out_attrs->size(), 1U);
return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
out_attrs);
}
#endif

NNVM_REGISTER_OP(Reshape)
.add_alias("reshape")
.describe(R"code(Reshapes the input array.
Expand Down Expand Up @@ -171,9 +222,19 @@ If the argument `reverse` is set to 1, then the special values are inferred from
.set_num_outputs(1)
.set_attr_parser(ParamParser<ReshapeParam>)
.set_attr<nnvm::FInferShape>("FInferShape", ReshapeShape)
#if MXNET_USE_MKLDNN == 1
.set_attr<FInferStorageType>("FInferStorageType", ReshapeStorageType)
#endif
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{"_backward_copy"})
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
#if MXNET_USE_MKLDNN == 1
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

I know that this is not the first op which is implemented this way, but is there a reason for the two different if blocks here ?

Copy link
Contributor Author

@huangzhiyuan huangzhiyuan Dec 11, 2018 •
edited
Loading

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Nothing different, it has been merged into one block here. Thanks for your review! :)

.set_attr<bool>("TIsMKLDNN", true)
.set_attr<FComputeEx>("FComputeEx<cpu>", ReshapeComputeExCPU)
.set_attr<FResourceRequest>("FResourceRequest", [](const NodeAttrs& n) {
return std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
})
#else
.set_attr<nnvm::FInplaceOption>("FInplaceOption",
[](const NodeAttrs& attrs) {
return std::vector<std::pair<int, int> >{{0, 0}};
Expand All @@ -182,6 +243,7 @@ If the argument `reverse` is set to 1, then the special values are inferred from
[](const NodeAttrs& attrs){
return std::vector<bool>{true};
})
#endif
.add_argument("data", "NDArray-or-Symbol", "Input data to reshape.")
.add_arguments(ReshapeParam::__FIELDS__());

Expand Down Expand Up @@ -210,24 +272,18 @@ static void FlattenEx(const nnvm::NodeAttrs& attrs,
#endif
}

#if MXNET_USE_MKLDNN == 1
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Seems this will change the original behavior when MKL-DNN is not enabled. FlattenStorageType is not defined then.

Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This is followed new style with other mkldnn op, that is, defining InferStorageType function within MKLDNN macro. Most other ops are refactored into this style by Luobao, I guess this one is missing.

static inline bool FlattenStorageType(const nnvm::NodeAttrs& attrs,
const int dev_mask,
DispatchMode* dispatch_mode,
std::vector<int> *in_attrs,
std::vector<int> *out_attrs) {
CHECK_EQ(in_attrs->size(), 1);
CHECK_EQ(out_attrs->size(), 1);
bool ret = ElemwiseStorageType<1, 1, false, false, false>(attrs, dev_mask, dispatch_mode,
in_attrs, out_attrs);
#if MXNET_USE_MKLDNN == 1
if (dev_mask == mshadow::cpu::kDevMask
&& in_attrs->at(0) == kDefaultStorage
&& out_attrs->at(0) == kDefaultStorage) {
*dispatch_mode = DispatchMode::kFComputeEx;
}
#endif
return ret;
return MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs,
out_attrs);
}
#endif

NNVM_REGISTER_OP(Flatten)
.add_alias("flatten")
Expand Down Expand Up @@ -261,7 +317,9 @@ Example::
.set_num_outputs(1)
.set_attr<nnvm::FInferShape>("FInferShape", FlattenShape)
.set_attr<nnvm::FInferType>("FInferType", ElemwiseType<1, 1>)
#if MXNET_USE_MKLDNN == 1
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

If MKLDNN supports this op, please add .set_attr<bool>("TIsMKLDNN", true) for it.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

This attribute is in Line 327.

.set_attr<FInferStorageType>("FInferStorageType", FlattenStorageType)
#endif
.set_attr<nnvm::FGradient>("FGradient", ElemwiseGradUseNone{ "_backward_copy" })
.set_attr<FCompute>("FCompute<cpu>", UnaryOp::IdentityCompute<cpu>)
.set_attr<FComputeEx>("FComputeEx<cpu>", FlattenEx)
Expand Down Expand Up @@ -914,7 +972,7 @@ NNVM_REGISTER_OP(depth_to_space)
.describe(R"code(Rearranges(permutes) data from depth into blocks of spatial data.
Similar to ONNX DepthToSpace operator:
https://github.com/onnx/onnx/blob/master/docs/Operators.md#DepthToSpace.
The output is a new tensor where the values from depth dimension are moved in spatial blocks
Copy link
Member

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Thank you for fixing these white spaces at line-end. Does cpplint complain about them? If not, I would like to keep them as is since these changes are not related to this PR.

Copy link
Contributor Author

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Have fixed the white space same as before, could you help review again? thanks :)

The output is a new tensor where the values from depth dimension are moved in spatial blocks
to height and width dimension. The reverse of this operation is ``space_to_depth``.

.. math::
Expand All @@ -925,7 +983,7 @@ to height and width dimension. The reverse of this operation is ``space_to_depth
y = reshape(x \prime \prime, [N, C / (block\_size ^ 2), H * block\_size, W * block\_size])
\end{gather*}

where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
and :math:`y` is the output tensor of layout :math:`[N, C / (block\_size ^ 2), H * block\_size, W * block\_size]`

Example::
Expand Down Expand Up @@ -965,9 +1023,9 @@ Example::
NNVM_REGISTER_OP(space_to_depth)
.describe(R"code(Rearranges(permutes) blocks of spatial data into depth.
Similar to ONNX SpaceToDepth operator:
https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth
https://github.com/onnx/onnx/blob/master/docs/Operators.md#SpaceToDepth

The output is a new tensor where the values from height and width dimension are
The output is a new tensor where the values from height and width dimension are
moved to the depth dimension. The reverse of this operation is ``depth_to_space``.

.. math::
Expand All @@ -978,7 +1036,7 @@ moved to the depth dimension. The reverse of this operation is ``depth_to_space`
y = reshape(x \prime \prime, [N, C * (block\_size ^ 2), H / block\_size, W / block\_size])
\end{gather*}

where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
where :math:`x` is an input tensor with default layout as :math:`[N, C, H, W]`: [batch, channels, height, width]
and :math:`y` is the output tensor of layout :math:`[N, C * (block\_size ^ 2), H / block\_size, W / block\_size]`

Example::
Expand All @@ -987,8 +1045,8 @@ Example::
[12, 18, 13, 19, 14, 20],
[3, 9, 4, 10, 5, 11],
[15, 21, 16, 22, 17, 23]]]]


space_to_depth(x, 2) = [[[[0, 1, 2],
[3, 4, 5]],
[[6, 7, 8],
Expand Down