bgawrych commented on a change in pull request #20713:
URL: https://github.com/apache/incubator-mxnet/pull/20713#discussion_r763899696
##########
File path: tests/python/unittest/test_operator.py
##########
@@ -1915,7 +1915,11 @@ def gen_broadcast_data(idx):
[[1, 1, 65, 2, 22], [1, 1, 65, 1, 1]],
[[1, 24, 103, 17, 18], [1, 24, 1, 1, 1]],
[[1, 1, 1, 1, 2], [1, 24, 194, 50, 1]],
- [[1, 1, 107, 84, 9], [1, 1, 1, 1, 1]]])
+ [[1, 1, 107, 84, 9], [1, 1, 1, 1, 1]],
+ [[8, 1, 6, 1], [7, 1, 5]], [[5, 4], [1]],
+ [[256, 256, 3], [3]], [[5, 4], [4]],
+ [[15, 3, 5], [3, 5]], [[15, 3, 5], [1, 5]],
+ [[15, 3, 5], [3, 1]]])
Review comment:
maybe some shapes with zeros?
##########
File path: src/operator/tensor/elemwise_binary_broadcast_op_basic.cc
##########
@@ -24,9 +24,76 @@
#include "./elemwise_unary_op.h"
#include "./elemwise_binary_op-inl.h"
#include "./elemwise_binary_broadcast_op.h"
+#if MXNET_USE_ONEDNN == 1
+#include "../nn/dnnl/dnnl_binary-inl.h"
+#endif // MXNET_USE_ONEDNN == 1
namespace mxnet {
namespace op {
+
+#if MXNET_USE_ONEDNN == 1
+template <dnnl::algorithm alg>
+void DNNLBinaryOpForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ mxnet::TShape new_lshape, new_rshape, new_oshape;
+ int ndim = BinaryBroadcastShapeCompact(inputs[0].shape(),
+ inputs[1].shape(),
+ outputs[0].shape(),
+ &new_lshape,
+ &new_rshape,
+ &new_oshape);
+ std::vector<NDArray> new_inputs;
+ std::vector<NDArray> new_outputs;
+ if (ndim) {
+ new_inputs = {inputs[0].Reshape(new_lshape),
inputs[1].Reshape(new_rshape)};
+ new_outputs = {outputs[0].Reshape(new_oshape)};
+ } else if (inputs[0].shape().Size() == 1 && inputs[0].shape().Size() == 1) {
Review comment:
check condition
##########
File path: src/operator/nn/dnnl/dnnl_binary.cc
##########
@@ -0,0 +1,77 @@
+/*
+ * 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_binary.cc
+ * \author: Adam Grabowski, [email protected]
+ */
+
+#if MXNET_USE_ONEDNN == 1
+#include "./dnnl_binary-inl.h"
+
+namespace mxnet {
+namespace op {
+
+DNNLBinaryOpFwd::DNNLBinaryOpFwd(const dnnl::algorithm alg,
+ const nnvm::NodeAttrs& attrs,
+ const std::vector<NDArray>& inputs,
+ const std::vector<NDArray>& outputs) {
+ auto src0_desc = inputs[0].GetDNNLData()->get_desc();
+ auto src1_desc = inputs[1].GetDNNLData()->get_desc();
+ auto dst_desc = outputs[0].GetDNNLData()->get_desc();
+
+ dnnl::binary::desc fwd_desc(alg, src0_desc, src1_desc, dst_desc);
+ fwd_pd = std::make_shared<binary_op_fwd_pd_t>(fwd_desc,
mxnet::CpuEngine::Get()->get_engine());
+ fwd = std::make_shared<binary_op_fwd_t>(*fwd_pd);
+}
+
+void DNNLBinaryOpFwd::Execute(const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ auto engine = mxnet::CpuEngine::Get()->get_engine();
+ auto src0 =
+ dnnl::memory(fwd_pd->src0_desc(), engine,
reinterpret_cast<void*>(inputs[0].data().dptr_));
Review comment:
Can't you just use GetDNNLData() here instead of creating memory by hand?
##########
File path: tests/python/unittest/test_operator.py
##########
@@ -2012,10 +2016,16 @@ def reduce_op(shape, x):
if shape == x.shape:
return x
keepdims_shape = list(x.shape)
+ #calculate difference between output and input ndims
Review comment:
add space after #
##########
File path: src/operator/tensor/elemwise_binary_broadcast_op_basic.cc
##########
@@ -24,9 +24,76 @@
#include "./elemwise_unary_op.h"
#include "./elemwise_binary_op-inl.h"
#include "./elemwise_binary_broadcast_op.h"
+#if MXNET_USE_ONEDNN == 1
+#include "../nn/dnnl/dnnl_binary-inl.h"
+#endif // MXNET_USE_ONEDNN == 1
namespace mxnet {
namespace op {
+
+#if MXNET_USE_ONEDNN == 1
+template <dnnl::algorithm alg>
+void DNNLBinaryOpForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ mxnet::TShape new_lshape, new_rshape, new_oshape;
+ int ndim = BinaryBroadcastShapeCompact(inputs[0].shape(),
+ inputs[1].shape(),
+ outputs[0].shape(),
+ &new_lshape,
+ &new_rshape,
+ &new_oshape);
+ std::vector<NDArray> new_inputs;
+ std::vector<NDArray> new_outputs;
+ if (ndim) {
+ new_inputs = {inputs[0].Reshape(new_lshape),
inputs[1].Reshape(new_rshape)};
+ new_outputs = {outputs[0].Reshape(new_oshape)};
+ } else if (inputs[0].shape().Size() == 1 && inputs[0].shape().Size() == 1) {
+ // BinaryBroadcastShapeCompact function doesn't reshape shape().Size() ==
1 tensors
+ // into shape (1). It is mandatory for oneDNN primitive to have this
reshape done.
+ mxnet::TShape one_shape = mxnet::TShape(1, 1);
+ new_inputs = {inputs[0].Reshape(one_shape),
inputs[1].Reshape(one_shape)};
+ new_outputs = {outputs[0].Reshape(one_shape)};
+ } else {
+ new_inputs = {inputs[0], inputs[1]};
+ new_outputs = {outputs[0]};
+ }
+
+ DNNLBinaryOpFwd& fwd = DNNLBinaryOpFwd::GetBinaryOpForward<alg>(attrs,
new_inputs, new_outputs);
+ fwd.Execute(new_inputs, req, new_outputs);
+}
+#endif
+
+template <typename OP>
+static void BinaryOperatorComputeExCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+#if MXNET_USE_ONEDNN == 1
+ if (common::ContainsOnlyStorage(inputs, kDefaultStorage)) {
+ if (SupportDNNLBinary(inputs)) {
+ const dnnl::algorithm alg = GetDNNLAlgorithm<OP>::dnnl_alg;
Review comment:
GetDNNLAlgorithm => DNNLAlgorithm ?
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