[GitHub] [incubator-mxnet] agrabows commented on a change in pull request #20757: Improve split operator by oneDNN reorder primitive

[GitBox <gi...@apache.org>]

agrabows commented on a change in pull request #20757:
URL: https://github.com/apache/incubator-mxnet/pull/20757#discussion_r763982776



##########
File path: src/operator/nn/dnnl/dnnl_split-inl.h
##########
@@ -0,0 +1,70 @@
+/*
+ * 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 dnnl_split-inl.h
+ */
+
+#ifndef MXNET_OPERATOR_NN_DNNL_DNNL_SPLIT_INL_H_
+#define MXNET_OPERATOR_NN_DNNL_DNNL_SPLIT_INL_H_
+
+#if MXNET_USE_ONEDNN == 1
+#include <vector>
+
+#include "./dnnl_base-inl.h"
+#include "./dnnl_ops-inl.h"
+
+namespace mxnet {
+namespace op {
+
+using split_fwd_t    = dnnl::reorder;
+using split_fwd_pd_t = dnnl::reorder::primitive_desc;
+
+class DNNLSplitFwd {
+ public:
+  struct Tensors {
+    Tensors(const NDArray& input, const std::vector<NDArray>& outputs);
+
+    const NDArray& input;
+    const std::vector<NDArray>& outputs;
+  };
+
+  static DNNLSplitFwd GetCached(const SplitParam& param,
+                                const Tensors& tensors,
+                                const TShape& split_pts,
+                                const int split_axis);
+
+  DNNLSplitFwd(const Tensors& tensors, const TShape& split_pts, const int split_axis);
+
+  void Execute(const Tensors& tensors,
+               const TShape& split_pts,
+               const int split_axis,
+               const std::vector<OpReqType>& req) const;
+
+ private:
+  std::vector<split_fwd_t> split_fwds;
+  std::vector<split_fwd_pd_t> split_pds;
+};
+
+bool SupportDNNLSplit(const NDArray& input);

Review comment:
       SupportDNNL functions' definitions are in dnnl_base-inl.h file.

##########
File path: src/operator/nn/dnnl/dnnl_split.cc
##########
@@ -0,0 +1,155 @@
+/*
+ * 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 dnnl_split.cc
+ */
+
+#if MXNET_USE_ONEDNN == 1
+
+#include "../../tensor/matrix_op-inl.h"
+#include "./dnnl_split-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool SupportDNNLSplit(const NDArray& input) {
+  static const std::set<int> supported_dtypes = {
+      mshadow::kFloat32, mshadow::kBfloat16, mshadow::kInt32, mshadow::kInt8, mshadow::kUint8};
+  return supported_dtypes.count(input.dtype());
+}
+
+void DNNLSplitForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<NDArray>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<NDArray>& outputs) {
+  const SplitParam& param = dmlc::get<SplitParam>(attrs.parsed);
+  const auto tensors      = DNNLSplitFwd::Tensors(inputs[0], outputs);
+
+  const auto& ishape   = tensors.input.shape();
+  const int split_axis = param.axis >= 0 ? param.axis : param.axis + ishape.ndim();
+  const mxnet::TShape split_pts =
+      (param.sections > 0) ? GetSplitIndices(tensors.input.shape(), split_axis, param.sections) :
+                             param.indices;
+
+  const auto fwd = DNNLSplitFwd::GetCached(param, tensors, split_pts, split_axis);
+  fwd.Execute(tensors, split_pts, split_axis, req);
+}
+
+DNNLSplitFwd::Tensors::Tensors(const NDArray& input, const std::vector<NDArray>& outputs)
+    : input(input), outputs(outputs) {}
+
+typedef ParamOpSign<SplitParam> DNNLSplitSignature;
+
+DNNLSplitFwd DNNLSplitFwd::GetCached(const SplitParam& param,
+                                     const Tensors& tensors,
+                                     const TShape& split_pts,
+                                     const int split_axis) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<DNNLSplitSignature, DNNLSplitFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<DNNLSplitSignature, DNNLSplitFwd, OpHash> fwds;
+#endif
+
+  DNNLSplitSignature key(param);
+  key.AddSign(tensors.input);
+  key.AddSign(tensors.outputs);
+  key.AddSign(split_pts);
+  key.AddSign(split_axis);
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    DNNLSplitFwd fwd(tensors, split_pts, split_axis);
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+DNNLSplitFwd::DNNLSplitFwd(const Tensors& tensors, const TShape& split_pts, const int split_axis) {
+  const auto cpu_engine = CpuEngine::Get()->get_engine();
+  const auto input      = tensors.input.Reorder2Default();
+  const auto& ishape    = input.shape();
+  const auto& dtype     = get_dnnl_type(input.dtype());
+  const auto format_tag = static_cast<dnnl::memory::format_tag>(GetDefaultFormat(ishape.ndim()));
+
+  dnnl::memory::dims strides(ishape.ndim(), 1);
+  // last dim stride = 1, start loop from the penultimate
+  for (int i = ishape.ndim() - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * ishape[i + 1];

Review comment:
       This vector could be an attribute of DNNLSplitFwd class to avoid duplicate loop in Execute() function.

##########
File path: src/operator/nn/dnnl/dnnl_split.cc
##########
@@ -0,0 +1,155 @@
+/*
+ * 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 dnnl_split.cc
+ */
+
+#if MXNET_USE_ONEDNN == 1
+
+#include "../../tensor/matrix_op-inl.h"
+#include "./dnnl_split-inl.h"
+
+namespace mxnet {
+namespace op {
+
+bool SupportDNNLSplit(const NDArray& input) {
+  static const std::set<int> supported_dtypes = {
+      mshadow::kFloat32, mshadow::kBfloat16, mshadow::kInt32, mshadow::kInt8, mshadow::kUint8};
+  return supported_dtypes.count(input.dtype());
+}
+
+void DNNLSplitForward(const nnvm::NodeAttrs& attrs,
+                      const OpContext& ctx,
+                      const std::vector<NDArray>& inputs,
+                      const std::vector<OpReqType>& req,
+                      const std::vector<NDArray>& outputs) {
+  const SplitParam& param = dmlc::get<SplitParam>(attrs.parsed);
+  const auto tensors      = DNNLSplitFwd::Tensors(inputs[0], outputs);
+
+  const auto& ishape   = tensors.input.shape();
+  const int split_axis = param.axis >= 0 ? param.axis : param.axis + ishape.ndim();
+  const mxnet::TShape split_pts =
+      (param.sections > 0) ? GetSplitIndices(tensors.input.shape(), split_axis, param.sections) :
+                             param.indices;
+
+  const auto fwd = DNNLSplitFwd::GetCached(param, tensors, split_pts, split_axis);
+  fwd.Execute(tensors, split_pts, split_axis, req);
+}
+
+DNNLSplitFwd::Tensors::Tensors(const NDArray& input, const std::vector<NDArray>& outputs)
+    : input(input), outputs(outputs) {}
+
+typedef ParamOpSign<SplitParam> DNNLSplitSignature;
+
+DNNLSplitFwd DNNLSplitFwd::GetCached(const SplitParam& param,
+                                     const Tensors& tensors,
+                                     const TShape& split_pts,
+                                     const int split_axis) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<DNNLSplitSignature, DNNLSplitFwd, OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<DNNLSplitSignature, DNNLSplitFwd, OpHash> fwds;
+#endif
+
+  DNNLSplitSignature key(param);
+  key.AddSign(tensors.input);
+  key.AddSign(tensors.outputs);
+  key.AddSign(split_pts);
+  key.AddSign(split_axis);
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    DNNLSplitFwd fwd(tensors, split_pts, split_axis);
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+DNNLSplitFwd::DNNLSplitFwd(const Tensors& tensors, const TShape& split_pts, const int split_axis) {
+  const auto cpu_engine = CpuEngine::Get()->get_engine();
+  const auto input      = tensors.input.Reorder2Default();
+  const auto& ishape    = input.shape();
+  const auto& dtype     = get_dnnl_type(input.dtype());
+  const auto format_tag = static_cast<dnnl::memory::format_tag>(GetDefaultFormat(ishape.ndim()));
+
+  dnnl::memory::dims strides(ishape.ndim(), 1);
+  // last dim stride = 1, start loop from the penultimate
+  for (int i = ishape.ndim() - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * ishape[i + 1];
+  }
+
+  for (int i = 0; i < tensors.outputs.size(); ++i) {
+    const auto& out = tensors.outputs[i];
+    if (out.shape().Size() == 0) {
+      continue;
+    }
+    dnnl::memory::dims dnnl_dims(ishape.begin(), ishape.end());
+    // ending split point is always last dimension
+    int end_split_pt      = (i + 1 >= split_pts.ndim()) ? ishape[split_axis] : split_pts[i + 1];
+    dnnl_dims[split_axis] = end_split_pt - split_pts[i];
+
+    auto in_mem_desc  = dnnl::memory::desc(dnnl_dims, dtype, strides);
+    auto out_mem_desc = dnnl::memory::desc(dnnl_dims, dtype, format_tag);
+
+    const auto split_pd = split_fwd_pd_t(cpu_engine, in_mem_desc, cpu_engine, out_mem_desc);
+    split_pds.emplace_back(split_pd);
+    split_fwds.emplace_back(split_fwd_t(split_pd));
+  }
+}
+
+void DNNLSplitFwd::Execute(const Tensors& tensors,
+                           const TShape& split_pts,
+                           const int split_axis,
+                           const std::vector<OpReqType>& req) const {
+  const auto& cpu_engine = CpuEngine::Get()->get_engine();
+
+  const auto& input_tensor = tensors.input.Reorder2Default();
+  const auto& ishape       = input_tensor.shape();
+
+  std::vector<int> strides(ishape.ndim(), 1);
+  for (int i = ishape.ndim() - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * ishape[i + 1];

Review comment:
       This vector could be an attribute initialized in DNNLSplitFwd() class constructor.




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