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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2022/08/25 12:18:10 UTC
[GitHub] [incubator-mxnet] Kacper-Pietkun commented on a diff in pull request #21132: [FEATURE] Dnnl sum primitive path
Kacper-Pietkun commented on code in PR #21132:
URL: https://github.com/apache/incubator-mxnet/pull/21132#discussion_r954894121
##########
src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:
##########
@@ -38,31 +39,39 @@ void DNNLBinaryOpForward(const nnvm::NodeAttrs& attrs,
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_diff = 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_diff) {
- 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[1].shape().Size() == 1) {
- // BinaryBroadcastShapeCompact function doesn't reshape tensors of size (1,1,...,1)
- // 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)};
+ // We can use more efficient sum kernel when there is no broadcast - when shapes are the same
+ const bool same_shape = (inputs[0].shape() == inputs[1].shape());
+
+ if (same_shape && alg == dnnl::algorithm::binary_add) {
+ DNNLSumFwd& fwd = DNNLSumFwd::GetCached(inputs, outputs);
+ fwd.Execute(ctx, inputs, req, outputs);
} else {
- new_inputs = {inputs[0], inputs[1]};
- new_outputs = {outputs[0]};
- }
+ mxnet::TShape new_lshape, new_rshape, new_oshape;
+ int ndim_diff = 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_diff) {
+ 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[1].shape().Size() == 1) {
+ // BinaryBroadcastShapeCompact function doesn't reshape tensors of size (1,1,...,1)
+ // into shape (1). It is mandatory for oneDNN primitive to have this reshape done.
Review Comment:
BinaryBroadcastShapeCompact does not support reshape of tensors of size (1,1,...,1), but we need to reshape such tensors into shape (1) for oneDNN to work properly
##########
src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:
##########
@@ -38,31 +39,39 @@ void DNNLBinaryOpForward(const nnvm::NodeAttrs& attrs,
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_diff = 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_diff) {
- 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[1].shape().Size() == 1) {
- // BinaryBroadcastShapeCompact function doesn't reshape tensors of size (1,1,...,1)
- // 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)};
+ // We can use more efficient sum kernel when there is no broadcast - when shapes are the same
+ const bool same_shape = (inputs[0].shape() == inputs[1].shape());
+
+ if (same_shape && alg == dnnl::algorithm::binary_add) {
+ DNNLSumFwd& fwd = DNNLSumFwd::GetCached(inputs, outputs);
+ fwd.Execute(ctx, inputs, req, outputs);
} else {
- new_inputs = {inputs[0], inputs[1]};
- new_outputs = {outputs[0]};
- }
+ mxnet::TShape new_lshape, new_rshape, new_oshape;
+ int ndim_diff = 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_diff) {
+ 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[1].shape().Size() == 1) {
+ // BinaryBroadcastShapeCompact function doesn't reshape tensors of size (1,1,...,1)
+ // into shape (1). It is mandatory for oneDNN primitive to have this reshape done.
Review Comment:
BinaryBroadcastShapeCompact does not support reshape of tensors of size (1,1,...,1), but we need to reshape such tensors into shape (1) for oneDNN to work properly.
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