[v1.x] Backport of improve log_softmax op performance by using DNNL s…

…upport (apache#18320) (apache#18469)

* Improve log_softmax performance by OneDNN library

* Adapt tests for MKLDNN log_softmax

* Fix lint errors

* Fix indent and comments

src/operator/nn/log_softmax.cc

@@ -26,10 +26,79 @@
 #include "../tensor/elemwise_unary_op.h"
 #include "../tensor/elemwise_binary_op.h"
 #include "../operator_common.h"
+#if MXNET_USE_MKLDNN == 1
+#include "mkldnn/mkldnn_base-inl.h"
+#include "mkldnn/mkldnn_ops-inl.h"
+#endif
 
 namespace mxnet {
 namespace op {
 
+#if MXNET_USE_MKLDNN == 1
+static void LogSoftmaxComputeExCPU(const nnvm::NodeAttrs& attrs,
+                                   const OpContext& ctx,
+                                   const std::vector<NDArray>& inputs,
+                                   const std::vector<OpReqType>& req,
+                                   const std::vector<NDArray>& outputs) {
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  if (SupportMKLDNNLogSoftmax(param, inputs[0], outputs[0])) {
+    MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
+    MKLDNNRun(MKLDNNLogSoftmaxForward, attrs, ctx, inputs[0], req[0], outputs[0]);
+    auto fn = SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>;
+    MKLDNN_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>, attrs, ctx,
+                  inputs, req, outputs);
+}
+
+static void LogSoftmaxGradComputeExCPU(const nnvm::NodeAttrs& attrs,
+                                       const OpContext& ctx,
+                                       const std::vector<NDArray>& inputs,
+                                       const std::vector<OpReqType>& req,
+                                       const std::vector<NDArray>& outputs) {
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  if (SupportMKLDNNLogSoftmax(param, inputs[1], outputs[0])) {
+    MKLDNN_OPCHECK_INIT(false, outputs.size(), inputs, outputs);
+    MKLDNNRun(MKLDNNLogSoftmaxBackward, attrs, ctx, inputs, req, outputs);
+    auto fn = SoftmaxGradCompute<cpu, op::mshadow_op::left, mxnet_op::log_softmax_bwd>;
+    MKLDNN_OPCHECK_RUN(fn, attrs, ctx, inputs, req, outputs);
+    return;
+  }
+  FallBackCompute(SoftmaxGradCompute<cpu, op::mshadow_op::left, mxnet_op::log_softmax_bwd>,
+                  attrs, ctx, inputs, req, outputs);
+}
+
+inline static bool LogSoftmaxStorageType(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);
+}
+
+inline static bool LogSoftmaxGradStorageType(const nnvm::NodeAttrs& attrs,
+                                             const int dev_mask,
+                                             DispatchMode* dispatch_mode,
+                                             std::vector<int> *in_attrs,
+                                             std::vector<int> *out_attrs) {
+  bool support = true;
+  int num_inputs = 2U;
+  if (softmax_has_dtype_override(attrs)) {
+    support = false;
+    num_inputs = 3U;
+  }
+
+  CHECK_EQ(in_attrs->size(), num_inputs);
+  CHECK_EQ(out_attrs->size(), 1U);
+  return MKLDNNStorageType(attrs, dev_mask, support, dispatch_mode, in_attrs, out_attrs);
+}
+#endif
+
 NNVM_REGISTER_OP(log_softmax)
 .add_alias("_npx_log_softmax")
 .describe(R"code(Computes the log softmax of the input.
@@ -49,7 +118,16 @@ Examples::
 
 )code")
 .set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<nnvm::FListInputNames>("FListInputNames",
+    [](const NodeAttrs& attrs){
+      return std::vector<std::string>{"data"};
+})
 .set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu, mxnet_op::log_softmax_fwd>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", LogSoftmaxComputeExCPU)
+.set_attr<FInferStorageType>("FInferStorageType", LogSoftmaxStorageType)
+#endif
 .set_attr<nnvm::FGradient>("FGradient", SoftmaxFGradient{"_backward_log_softmax"})
 .set_attr<nnvm::FInferType>("FInferType", SoftmaxOpType)
 .set_num_inputs(1)
@@ -71,6 +149,11 @@ NNVM_REGISTER_OP(_backward_log_softmax)
 .set_attr<nnvm::FInplaceOption>("FInplaceOption", SoftmaxGradOpInplaceOption)
 .add_argument("args", "NDArray-or-Symbol[]", "Positional input arguments")
 .set_attr_parser(ParamParser<SoftmaxParam>)
+#if MXNET_USE_MKLDNN == 1
+.set_attr<bool>("TIsMKLDNN", true)
+.set_attr<FComputeEx>("FComputeEx<cpu>", LogSoftmaxGradComputeExCPU)
+.set_attr<FInferStorageType>("FInferStorageType", LogSoftmaxGradStorageType)
+#endif
 .set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu, mshadow_op::left,
                                                         mxnet_op::log_softmax_bwd>);
 

src/operator/nn/mkldnn/mkldnn_base-inl.h

@@ -211,6 +211,8 @@ bool SupportQuantizedMKLDNNAct(const ActivationParam &param);
 bool SupportMKLDNNConv(const ConvolutionParam &params, const NDArray &input);
 bool SupportMKLDNNDeconv(const DeconvolutionParam& params, const NDArray &input);
 bool SupportMKLDNNSoftmax(const SoftmaxParam& param, const NDArray &input, const NDArray &output);
+bool SupportMKLDNNLogSoftmax(const SoftmaxParam& param, const NDArray &input,
+                             const NDArray &output);
 bool SupportMKLDNNSoftmaxOutput(const SoftmaxOutputParam &param);
 bool SupportMKLDNNTranspose(const TransposeParam& param, const NDArray &data);
 }  // namespace op

src/operator/nn/mkldnn/mkldnn_log_softmax.cc

@@ -0,0 +1,226 @@
+/*
+ * 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_log_softmax.cc
+ * \brief Implementation of log_softmax function with MKLDNN support
+*/
+
+#include "../softmax-inl.h"
+#include "./mkldnn_ops-inl.h"
+#include "./mkldnn_base-inl.h"
+
+#if MXNET_USE_MKLDNN == 1
+namespace mxnet {
+namespace op {
+
+static mkldnn::logsoftmax_forward::primitive_desc GetLogSoftmaxFwdPd(
+                                  bool is_train,
+                                  const int axis,
+                                  const mkldnn::memory &input_mem) {
+  mkldnn::memory::desc data_md = input_mem.get_desc();
+  auto cpu_engine = CpuEngine::Get()->get_engine();
+  auto prop = is_train ? mkldnn::prop_kind::forward_training
+                       : mkldnn::prop_kind::forward_scoring;
+  auto desc = mkldnn::logsoftmax_forward::desc(prop, data_md, axis);
+  return mkldnn::logsoftmax_forward::primitive_desc(desc, cpu_engine);
+}
+
+static mkldnn::logsoftmax_backward::primitive_desc GetLogSoftmaxBwdPd(
+                                const mkldnn::memory &diff_mem,
+                                const mkldnn::memory &data_mem,
+                                const int axis,
+                                const mkldnn::logsoftmax_forward::primitive_desc &hint_fwd_pd) {
+  mkldnn::memory::desc diff_md = diff_mem.get_desc();
+  mkldnn::memory::desc data_md = data_mem.get_desc();
+  auto cpu_engine = CpuEngine::Get()->get_engine();
+  auto desc = mkldnn::logsoftmax_backward::desc(diff_md, data_md, axis);
+  return mkldnn::logsoftmax_backward::primitive_desc(desc, cpu_engine, hint_fwd_pd);
+}
+
+
+bool SupportMKLDNNLogSoftmax(const SoftmaxParam &param,
+                             const NDArray &data,
+                             const NDArray &output) {
+  const int ndim = data.shape().ndim();
+  const int in_dtype = data.dtype();
+  const int out_dtype = output.dtype();
+  const int axis = CheckAxis(param.axis, ndim);
+  // MKLDNN does not support temperature argument in their log_softmax function
+  // now. Need update this once they start to support it.
+  // Currently, MKLDNN shows bad performance when log_softmax is not performed on the last dimension
+  if (param.temperature.has_value() ||
+      in_dtype != mshadow::kFloat32 ||
+      in_dtype != out_dtype ||
+      axis != (ndim - 1)) {
+    return false;
+  }
+
+  // only supports ndim = 1, 2, 3, 4 for now
+  return (ndim >= 1 && ndim <= 4);
+}
+
+class MKLDNNLogSoftmaxFwd {
+ public:
+  mkldnn::logsoftmax_forward::primitive_desc pd;
+
+  MKLDNNLogSoftmaxFwd(const bool is_train,
+                      const int axis,
+                      const mkldnn::memory &input) : pd(GetLogSoftmaxFwdPd(is_train, axis, input)) {
+    fwd_ = std::make_shared<mkldnn::logsoftmax_forward>(pd);
+  }
+
+  const mkldnn::logsoftmax_forward &GetFwd() const {
+    return *fwd_;
+  }
+
+ private:
+  std::shared_ptr<mkldnn::logsoftmax_forward> fwd_;
+};
+
+typedef ParamOpSign<SoftmaxParam> MKLDNNSoftmaxSignature;
+
+static MKLDNNLogSoftmaxFwd &GetLogSoftmaxFwd(const SoftmaxParam &param,
+                                             const int real_axis,
+                                             const bool is_train,
+                                             const NDArray &data,
+                                             const NDArray &output) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<MKLDNNSoftmaxSignature,
+                                         MKLDNNLogSoftmaxFwd,
+                                         OpHash> fwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<MKLDNNSoftmaxSignature,
+                                            MKLDNNLogSoftmaxFwd,
+                                            OpHash> fwds;
+#endif
+
+  MKLDNNSoftmaxSignature key(param);
+  key.AddSign(real_axis);
+  key.AddSign(is_train);
+  key.AddSign(data);
+  key.AddSign(output);
+
+  auto it = fwds.find(key);
+  if (it == fwds.end()) {
+    MKLDNNLogSoftmaxFwd fwd(is_train, real_axis, *(data.GetMKLDNNData()));
+    it = AddToCache(&fwds, key, fwd);
+  }
+  return it->second;
+}
+
+void MKLDNNLogSoftmaxForward(const nnvm::NodeAttrs& attrs,
+                             const OpContext &ctx,
+                             const NDArray &in_data,
+                             const OpReqType &req,
+                             const NDArray &out_data) {
+  if (req == kNullOp) return;
+  // same as the FCompute path, log_softmax only supports kWriteTo and kWriteInplace for now.
+  CHECK_NE(req, kAddTo);
+
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  int axis = CheckAxis(param.axis, in_data.shape().ndim());
+  auto fwd = GetLogSoftmaxFwd(param, axis, ctx.is_train, in_data, out_data);
+
+  auto in_mem = in_data.GetMKLDNNData();
+  auto out_mem = out_data.GetMKLDNNData(fwd.pd.dst_desc());
+  MKLDNNStream *stream = MKLDNNStream::Get();
+  stream->RegisterPrimArgs(fwd.GetFwd(), {{MKLDNN_ARG_SRC, *in_mem}, {MKLDNN_ARG_DST, *out_mem}});
+  stream->Submit();
+}
+
+class MKLDNNLogSoftmaxBwd {
+ public:
+  mkldnn::logsoftmax_backward::primitive_desc pd;
+
+  MKLDNNLogSoftmaxBwd(const mkldnn::memory &diff_mem,
+                      const mkldnn::memory &data_mem,
+                      const int axis,
+                      const mkldnn::logsoftmax_forward::primitive_desc &hint_fwd_pd) :
+                                    pd(GetLogSoftmaxBwdPd(diff_mem, data_mem, axis, hint_fwd_pd)) {
+    bwd_ = std::make_shared<mkldnn::logsoftmax_backward>(pd);
+  }
+
+  const mkldnn::logsoftmax_backward &GetBwd() const {
+    return *bwd_;
+  }
+
+ private:
+  std::shared_ptr<mkldnn::logsoftmax_backward> bwd_;
+};
+
+static MKLDNNLogSoftmaxBwd &GetLogSoftmaxBwd(const SoftmaxParam &param,
+                                             const int real_axis,
+                                             const std::vector<NDArray> &data,
+                                             const std::vector<NDArray> &output) {
+#if DMLC_CXX11_THREAD_LOCAL
+  static thread_local std::unordered_map<MKLDNNSoftmaxSignature,
+                                         MKLDNNLogSoftmaxBwd,
+                                         OpHash> bwds;
+#else
+  static MX_THREAD_LOCAL std::unordered_map<MKLDNNSoftmaxSignature,
+                                            MKLDNNLogSoftmaxBwd,
+                                            OpHash> bwds;
+#endif
+
+  MKLDNNSoftmaxSignature key(param);
+  key.AddSign(real_axis);
+  key.AddSign(data);
+  key.AddSign(output);
+
+  auto it = bwds.find(key);
+  if (it == bwds.end()) {
+    auto diff_mem = data[0].GetMKLDNNData();
+    auto data_mem = data[1].GetMKLDNNData();
+    auto fwd_pd = GetLogSoftmaxFwdPd(true, real_axis, *data_mem);
+    MKLDNNLogSoftmaxBwd bwd(*diff_mem, *data_mem, real_axis, fwd_pd);
+    it = AddToCache(&bwds, key, bwd);
+  }
+  return it->second;
+}
+
+void MKLDNNLogSoftmaxBackward(const nnvm::NodeAttrs& attrs,
+                              const OpContext &ctx,
+                              const std::vector<NDArray> &in_data,
+                              const std::vector<OpReqType> &req,
+                              const std::vector<NDArray> &out_data) {
+  if (req[0] == kNullOp) return;
+  CHECK_EQ(in_data.size(), 2U);
+  const SoftmaxParam& param = nnvm::get<SoftmaxParam>(attrs.parsed);
+  int axis = CheckAxis(param.axis, in_data[1].shape().ndim());
+  auto diff_mem = in_data[0].GetMKLDNNData();
+  auto data_mem = in_data[1].GetMKLDNNData();
+  auto bwd = GetLogSoftmaxBwd(param, axis, in_data, out_data);
+
+  auto out_mem = CreateMKLDNNMem(out_data[0], bwd.pd.diff_src_desc(), req[0]);
+  MKLDNNStream *stream = MKLDNNStream::Get();
+  mkldnn_args_map_t args = {
+    { MKLDNN_ARG_DST, *data_mem },
+    { MKLDNN_ARG_DIFF_DST, *diff_mem },
+    { MKLDNN_ARG_DIFF_SRC, *out_mem.second }
+  };
+
+  stream->RegisterPrimArgs(bwd.GetBwd(), args);
+  CommitOutput(out_data[0], out_mem);
+  stream->Submit();
+}
+
+}   // namespace op
+}   // namespace mxnet
+#endif

src/operator/nn/mkldnn/mkldnn_ops-inl.h

@@ -98,6 +98,15 @@ void MKLDNNSoftmaxBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                            const std::vector<OpReqType> &req,
                            const std::vector<NDArray> &out_data);
 
+/* For log_softmax */
+void MKLDNNLogSoftmaxForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                             const NDArray &in_data, const OpReqType &req,
+                             const NDArray &out_data);
+void MKLDNNLogSoftmaxBackward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
+                              const std::vector<NDArray> &in_data,
+                              const std::vector<OpReqType> &req,
+                              const std::vector<NDArray> &out_data);
+
 /* For softmax_output */
 void MKLDNNSoftmaxOutputForward(const nnvm::NodeAttrs& attrs, const OpContext &ctx,
                                 const std::vector<NDArray> &in_data,

tests/python/unittest/test_loss.py

@@ -49,10 +49,10 @@ def test_loss_ndarray():
 
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
     L = loss(output, label).asnumpy()
-    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]))
+    assert_almost_equal(L, np.array([ 2.12692809,  0.04858733]), rtol=1e-3, atol=1e-4)
 
     L = loss(output, label, weighting).asnumpy()
-    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]))
+    assert_almost_equal(L, np.array([ 1.06346405,  0.04858733]), rtol=1e-3, atol=1e-4)
 
 
 def get_net(num_hidden, flatten=True):

tests/python/unittest/test_numpy_gluon.py

@@ -153,10 +153,10 @@ def test_np_loss_ndarray():
 
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
     L = loss(output, label).asnumpy()
-    assert_almost_equal(L, _np.array([2.12692809,  0.04858733]), use_broadcast=False)
+    assert_almost_equal(L, _np.array([2.12692809,  0.04858733]), use_broadcast=False, rtol=1e-3)
 
     L = loss(output, label, weighting).asnumpy()
-    assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False)
+    assert_almost_equal(L, _np.array([1.06346405,  0.04858733]), use_broadcast=False, rtol=1e-3)
 
 
 @with_seed()

tests/python/unittest/test_operator.py

@@ -5338,8 +5338,8 @@ def test_log_softmax():
             axis = np.random.randint(0, ndim)
             data = np.random.uniform(-2, 2, size=shape)
             sym = mx.sym.log_softmax(axis=axis-ndim)
-            check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)])
-            check_numeric_gradient(sym, [data], rtol=0.05, atol=1e-3)
+            check_symbolic_forward(sym, [data], [np.log(np_softmax(data, axis=axis)+1e-20)], rtol=1e-3, atol=1e-4)
+            check_numeric_gradient(sym, [data], rtol=1e-1, atol=1e-2)
 
 def test_softmax_with_large_inputs():
     def softmax_forward(input_data, true_output):