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new 2c5d7f7 Optimize transpose operator with MKL-DNN (#14545)
2c5d7f7 is described below
commit 2c5d7f768bdd1599c35f1a3cd1266efd051a9986
Author: Tao Lv <tao.a...@intel.com>
AuthorDate: Thu Apr 11 06:41:32 2019 +0800
Optimize transpose operator with MKL-DNN (#14545)
* add mkldnn transpose
* general transpose
* support mkldnn format
* fix lint
* address comments
* add unit test
* add comments
* retrigger CI
---
src/operator/nn/mkldnn/mkldnn_base-inl.h | 2 +
src/operator/nn/mkldnn/mkldnn_ops-inl.h | 6 ++
src/operator/nn/mkldnn/mkldnn_transpose.cc | 161 +++++++++++++++++++++++++++++
src/operator/tensor/matrix_op-inl.h | 15 +++
src/operator/tensor/matrix_op.cc | 34 ++++++
tests/python/mkl/test_mkldnn.py | 15 +++
6 files changed, 233 insertions(+)
diff --git a/src/operator/nn/mkldnn/mkldnn_base-inl.h
b/src/operator/nn/mkldnn/mkldnn_base-inl.h
index 0a89c0f..a460e33 100644
--- a/src/operator/nn/mkldnn/mkldnn_base-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_base-inl.h
@@ -175,12 +175,14 @@ struct ConvolutionParam;
struct DeconvolutionParam;
struct SoftmaxParam;
struct SoftmaxOutputParam;
+struct TransposeParam;
bool SupportMKLDNNAct(const ActivationParam& param);
bool SupportMKLDNNAct(const ActivationParam& param, const NDArray &input);
bool SupportMKLDNNConv(const ConvolutionParam& params, const NDArray &input);
bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray
&input);
bool SupportMKLDNNSoftmax(const SoftmaxParam& param);
bool SupportMKLDNNSoftmaxOutput(const SoftmaxOutputParam ¶m);
+bool SupportMKLDNNTranspose(const TransposeParam& param, const NDArray &data);
} // namespace op
static int GetTypeSize(int dtype) {
diff --git a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
index 39f2632..f3f61b4 100644
--- a/src/operator/nn/mkldnn/mkldnn_ops-inl.h
+++ b/src/operator/nn/mkldnn/mkldnn_ops-inl.h
@@ -113,6 +113,12 @@ void MKLDNNActivationBackward(const nnvm::NodeAttrs&
attrs, const OpContext &ctx
void MKLDNNSum(const mkldnn::memory &arr1, const mkldnn::memory &arr2,
const mkldnn::memory &out);
+void MKLDNNTransposeForward(const nnvm::NodeAttrs& attrs,
+ const OpContext &ctx,
+ const NDArray &data,
+ const OpReqType &req,
+ const NDArray &output);
+
} // namespace op
} // namespace mxnet
#endif // MXNET_USE_MKLDNN == 1
diff --git a/src/operator/nn/mkldnn/mkldnn_transpose.cc
b/src/operator/nn/mkldnn/mkldnn_transpose.cc
new file mode 100644
index 0000000..0986d06
--- /dev/null
+++ b/src/operator/nn/mkldnn/mkldnn_transpose.cc
@@ -0,0 +1,161 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, 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.
+ */
+
+/*!
+ * \file mkldnn_transpose.cc
+ * \brief Implement transpose operator via MKL-DNN reorder primitive
+ * \author Tao Lv
+*/
+
+#if MXNET_USE_MKLDNN == 1
+
+#include <mkldnn.hpp>
+#include "../../tensor/matrix_op-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool SupportMKLDNNTranspose(const TransposeParam& param,
+ const NDArray &data) {
+ auto data_ndim = data.shape().ndim();
+
+ if (data_ndim > 4 || data.dtype() != mshadow::kFloat32)
+ return false;
+
+ return true;
+}
+
+typedef ParamOpSign<TransposeParam> MKLDNNTransposeSignature;
+
+class MKLDNNTransposeForward {
+ std::shared_ptr<mkldnn::memory> data_;
+ std::shared_ptr<mkldnn::memory> out_;
+ std::shared_ptr<mkldnn::memory::primitive_desc> dst_pd_;
+ std::shared_ptr<mkldnn::reorder> transpose_;
+
+ public:
+ MKLDNNTransposeForward(const TransposeParam& param,
+ const NDArray &data) {
+ auto shape = data.shape();
+ auto data_ndim = shape.ndim();
+ auto axes_ndim = param.axes.ndim();
+ auto axes = mxnet::TShape(data_ndim);
+ if (axes_ndim == 0) {
+ for (size_t i = 0; i < data_ndim; i++) {
+ axes[i] = data_ndim - i - 1;
+ }
+ } else {
+ axes = param.axes;
+ }
+
+ auto engine = CpuEngine::Get()->get_engine();
+ auto in_mem = data.GetMKLDNNData();
+ auto src_pd = in_mem->get_primitive_desc();
+ data_ = std::make_shared<mkldnn::memory>(src_pd, nullptr);
+
+ // destination
+ // Not all formats are well defined with a certain name in MKL-DNN.
+ // For example, transpose(NCHW, (0, 2, 1, 3)) -> NHCW, which is not
explicitly defined in
+ // MKL-DNN. To support general transposing, we need create destination
format from scratch.
+ mkldnn_memory_desc_t dst_fmt;
+ dst_fmt.primitive_kind = mkldnn_memory;
+ dst_fmt.ndims = data_ndim;
+ dst_fmt.data_type = mkldnn_f32;
+ dst_fmt.format = mkldnn_blocked;
+
+ for (size_t i = 0; i < data_ndim; i++)
+ dst_fmt.dims[i] = shape[i];
+
+ unsigned int total_stride = 1;
+ for (int i = data_ndim - 1; i >= 0; i--) {
+ dst_fmt.layout_desc.blocking.padding_dims[i] = shape[i];
+ dst_fmt.layout_desc.blocking.block_dims[i] = 1;
+ dst_fmt.layout_desc.blocking.offset_padding_to_data[i]= 0;
+ // strides[0]: stride between the first elements of adjacent blocks.
+ dst_fmt.layout_desc.blocking.strides[0][axes[i]] = total_stride;
+ // strides[1]: strides between elements in the same block.
+ dst_fmt.layout_desc.blocking.strides[1][axes[i]] = 1;
+
+ total_stride *= shape[axes[i]];
+ }
+
+ dst_fmt.layout_desc.blocking.offset_padding = 0;
+ dst_pd_ = std::make_shared<mkldnn::memory::primitive_desc>(dst_fmt,
engine);
+ out_ = std::make_shared<mkldnn::memory>(*dst_pd_, nullptr);
+
+ transpose_ = std::make_shared<mkldnn::reorder>(*data_, *out_);
+ }
+
+ void SetNewMem(const NDArray &data, const NDArray &output) {
+ if (data.IsMKLDNNData()) {
+ this->data_->set_data_handle(data.GetMKLDNNData()->get_data_handle());
+ } else {
+ MSHADOW_TYPE_SWITCH(data.dtype(), DTYPE, {
+ this->data_->set_data_handle(data.data().dptr<DTYPE>());
+ });
+ }
+
+ CHECK(!output.IsMKLDNNData());
+ MSHADOW_TYPE_SWITCH(output.dtype(), DTYPE, {
+ this->out_->set_data_handle(output.data().dptr<DTYPE>());
+ });
+ }
+
+ const mkldnn::reorder &GetFwd() const {
+ return *transpose_;
+ }
+};
+
+static MKLDNNTransposeForward &GetTransposeForward(const TransposeParam& param,
+ const NDArray &data) {
+#if DMLC_CXX11_THREAD_LOCAL
+ static thread_local std::unordered_map<MKLDNNTransposeSignature,
+ MKLDNNTransposeForward, OpHash> fwds;
+#else
+ static MX_THREAD_LOCAL std::unordered_map<MKLDNNTransposeSignature,
+ MKLDNNTransposeForward, OpHash>
fwds;
+#endif
+ MKLDNNTransposeSignature key(param);
+ key.AddSign(data);
+
+ auto it = fwds.find(key);
+ if (it == fwds.end()) {
+ MKLDNNTransposeForward fwd(param, data);
+ it = AddToCache(&fwds, key, fwd);
+ }
+ return it->second;
+}
+
+void MKLDNNTransposeForward(const nnvm::NodeAttrs& attrs,
+ const OpContext &ctx,
+ const NDArray &data,
+ const OpReqType &req,
+ const NDArray &output) {
+ const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
+
+ auto stream = MKLDNNStream::Get();
+ auto fwd = GetTransposeForward(param, data);
+
+ fwd.SetNewMem(data, output);
+ stream->RegisterPrim(fwd.GetFwd());
+ stream->Submit();
+}
+} // namespace op
+} // namespace mxnet
+#endif
diff --git a/src/operator/tensor/matrix_op-inl.h
b/src/operator/tensor/matrix_op-inl.h
index 5eecda6..fa10815 100644
--- a/src/operator/tensor/matrix_op-inl.h
+++ b/src/operator/tensor/matrix_op-inl.h
@@ -238,6 +238,10 @@ struct TransposeParam : public
dmlc::Parameter<TransposeParam> {
DMLC_DECLARE_FIELD(axes).set_default(mxnet::TShape())
.describe("Target axis order. By default the axes will be inverted.");
}
+
+ bool operator==(const TransposeParam &other) const {
+ return this->axes == other.axes;
+ }
};
template<typename xpu>
@@ -2841,4 +2845,15 @@ inline uint32_t SplitNumOutputs(const NodeAttrs& attrs) {
} // namespace op
} // namespace mxnet
+namespace std {
+template<>
+struct hash<mxnet::op::TransposeParam> {
+ size_t operator()(const mxnet::op::TransposeParam& val) {
+ size_t ret = 0;
+ ret = dmlc::HashCombine(ret, val.axes);
+ return ret;
+ }
+};
+} // namespace std
+
#endif // MXNET_OPERATOR_TENSOR_MATRIX_OP_INL_H_
diff --git a/src/operator/tensor/matrix_op.cc b/src/operator/tensor/matrix_op.cc
index 3bca330..1431fef 100644
--- a/src/operator/tensor/matrix_op.cc
+++ b/src/operator/tensor/matrix_op.cc
@@ -339,6 +339,35 @@ Example::
})
.add_argument("data", "NDArray-or-Symbol", "Input array.");
+#if MXNET_USE_MKLDNN == 1
+static void TransposeComputeExCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ const TransposeParam& param = nnvm::get<TransposeParam>(attrs.parsed);
+ CHECK_EQ(req[0], kWriteTo) << "Transpose does not support inplace";
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+
+ if (SupportMKLDNNTranspose(param, inputs[0])) {
+ MKLDNNTransposeForward(attrs, ctx, inputs[0], req[0], outputs[0]);
+ return;
+ }
+ FallBackCompute(Transpose<cpu>, attrs, ctx, inputs, req, outputs);
+}
+
+inline static bool TransposeStorageType(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(transpose)
.describe(R"code(Permutes the dimensions of an array.
@@ -393,6 +422,11 @@ Examples::
}
})
.set_attr<FCompute>("FCompute<cpu>", Transpose<cpu>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", TransposeComputeExCPU)
+.set_attr<FInferStorageType>("FInferStorageType", TransposeStorageType)
+#endif
.add_argument("data", "NDArray-or-Symbol", "Source input")
.add_arguments(TransposeParam::__FIELDS__());
diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 01ba03c..0610b60 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -473,6 +473,21 @@ def test_non_mkldnn_fcomputeex():
exec1 = custom.bind(mx.cpu(), args={'data': mx.nd.ones([10,3,96,96]),
'conv_weight': mx.nd.ones([8,3,5,5])})
exec1.forward()[0].wait_to_read()
+@with_seed()
+def test_conv_transpose():
+ axes = [(0,2,1,3), (0,2,3,1), (1,2,3,0), (3,2,1,0)]
+ a = np.random.rand(10, 16, 50, 50)
+ b = np.random.rand(32, 16, 3, 3)
+ x = mx.nd.array(a)
+ w = mx.nd.array(b)
+ y = mx.nd.Convolution(data=x, weight=w, kernel=(3, 3), num_group=1,
num_filter=32, no_bias=True)
+ for axis in axes:
+ t = mx.nd.transpose(y, axis)
+ t.wait_to_read()
+ s = y.asnumpy()
+ n = np.transpose(s, axis)
+ np.allclose(t.asnumpy(), n)
+
if __name__ == '__main__':
install.test_mkldnn_install()
