bgawrych commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r805844929
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -238,18 +277,45 @@ void AdaptiveAvgPoolComputeExCPU(const nnvm::NodeAttrs&
attrs,
oneDNN doesn't support adaptive pooling.
Fallback is needed when padding is not equal 0;
*/
- const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
if (SupportDNNL(inputs[0]) && SupportDNNLAveragePooling(inputs[0],
outputs[0])) {
- const NDArray* workspace = nullptr;
DNNL_OPCHECK_INIT(false, 1, inputs, outputs);
- DNNLPoolingCompute(ctx, param, inputs[0], req[0], outputs[0], workspace,
true);
+ DNNLRun(DNNLPoolingCompute, attrs, ctx, inputs, req, outputs);
DNNL_OPCHECK_RUN(PoolingCompute<cpu>, attrs, ctx, inputs, req, outputs);
return;
}
FallBackCompute(AdaptiveAvgPoolOpForward<cpu>, attrs, ctx, inputs, req,
outputs);
}
#endif
+inline static bool AdaptivePoolingStorageType(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(), 1);
+ bool dispatched = false;
+#if MXNET_USE_ONEDNN == 1
+ if (!dispatched) {
+ dispatched = DNNLStorageType(attrs, dev_mask, true, dispatch_mode,
in_attrs, out_attrs);
+ }
+ if (!DNNLEnvSet()) {
+ *dispatch_mode = DispatchMode::kFComputeFallback;
+ }
+#else
+ for (int& v : *in_attrs)
+ if (v == -1)
+ v = kDefaultStorage;
+ if (!dispatched && common::ContainsOnlyStorage(*in_attrs, kDefaultStorage)) {
+ dispatched =
+ storage_type_assign(out_attrs, kDefaultStorage, dispatch_mode,
DispatchMode::kFCompute);
+ }
+ if (!dispatched) {
+ dispatched = dispatch_fallback(out_attrs, dispatch_mode);
+ }
+#endif
+ return dispatched;
+}
Review comment:
Why you're using this function? Isn't call to DNNLStorageType enough
like in other ops (e.g. src/operator/nn/pooling.cc:355)?
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -169,6 +171,67 @@ void AdaptiveAvgPoolUpdateOutput(mshadow::Stream<cpu>* s,
}
}
+#if MXNET_USE_ONEDNN == 1
+bool SupportDNNLAveragePooling(const NDArray& in_data, const NDArray&
out_data) {
+ for (int64_t idx = 2; idx < in_data.shape().ndim(); ++idx) {
+ const int s1 = in_data.shape()[idx];
+ const int s2 = out_data.shape()[idx];
+ if (s2 == 0) {
+ return false;
+ }
+ if (s1 % s2 != 0) {
+ return false;
+ }
+ }
+ const int IH = in_data.shape()[2];
+ const int IW = in_data.shape()[3];
+ const int OH = out_data.shape()[2];
+ const int OW = out_data.shape()[3];
+ const int strides_H = ((IH << 1) / OH) - (IH / OH);
+ const int strides_W = ((IW << 1) / OW) - (IW / OW);
+ const int kernel_H = DIV_ROUND_UP((IH << 1) / OH, 1) - (IH / OH);
+ const int kernel_W = DIV_ROUND_UP((IW << 1) / OW, 1) - (IW / OW);
+ const int pad_l_top = (strides_H * (OH - 1) + kernel_H - IH) / 2;
+ const int pad_l_left = (strides_W * (OW - 1) + kernel_W - IW) / 2;
+ return pad_l_top == 0 && pad_l_left == 0;
+}
+
+void AdaptiveAvgPoolOpBackwardExCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ // Pooling does not currently support working with views
+ if (inputs[0].IsView() || outputs[0].IsView()) {
+ FallBackCompute(AdaptiveAvgPoolOpBackward<cpu>, attrs, ctx, inputs, req,
outputs);
+ return;
+ }
Review comment:
This is probably not necessary anymore as you're using DNNLRun function
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -262,13 +328,14 @@ The pooling kernel and stride sizes are automatically
chosen for desired output
(N x C x height x width) for any input (NCHW).
)code" ADD_FILELINE)
- .set_attr_parser(ParamParser<PoolingParam>)
+ .set_attr_parser(PoolingParamParser)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<mxnet::FInferShape>("FInferShape", AdaptiveAvgPoolOpInferShape)
.set_attr<FCompute>("FCompute<cpu>", AdaptiveAvgPoolOpForward<cpu>)
.set_attr<nnvm::FGradient>("FGradient",
ElemwiseGradUseNone{"_backward_contrib_AdaptiveAvgPooling2D"})
+ .set_attr<FInferStorageType>("FInferStorageType",
AdaptivePoolingStorageType)
Review comment:
This can be put in MXNET_USE_ONEDNN compilation scope
##########
File path: src/operator/nn/pooling.cc
##########
@@ -294,7 +295,6 @@ void PoolingComputeExCPU(const nnvm::NodeAttrs& attrs,
const std::vector<OpReqType>& req,
const std::vector<NDArray>& outputs) {
const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
- const NDArray* workspace = nullptr;
// Pooling does not currently support working with views
if (inputs[0].IsView() || outputs[0].IsView()) {
Review comment:
Same as above
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -277,10 +344,35 @@ The pooling kernel and stride sizes are automatically
chosen for desired output
.add_arguments(PoolingParam::__FIELDS__());
NNVM_REGISTER_OP(_backward_contrib_AdaptiveAvgPooling2D)
- .set_attr_parser(ParamParser<PoolingParam>)
+ .set_attr_parser(PoolingParamParser)
.set_num_inputs(1)
.set_num_outputs(1)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
+#if MXNET_USE_ONEDNN == 1
+ .set_attr<FInferStorageType>("FInferStorageType",
BackwardAdaptivePoolingStorageType)
+ // Different backend requires different FInplaceOption
+ .set_attr<nnvm::FInplaceOption>("FInplaceOption",
+ [](const NodeAttrs& attrs) {
+ const PoolingParam& param =
+
nnvm::get<PoolingParam>(attrs.parsed);
+ if (DNNLRequireWorkspace(param) &&
param.IsAdaptivePooling())
+ return std::vector<std::pair<int,
int>>{{1, 0}};
+ return std::vector<std::pair<int,
int>>();
+ })
+ .set_attr<FResourceRequest>("FResourceRequest",
+ [](const NodeAttrs& n) {
+ return
std::vector<ResourceRequest>{ResourceRequest::kTempSpace};
+ })
+#else
+ .set_attr<nnvm::FInplaceOption>("FInplaceOption",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::pair<int,
int>>();
+ })
+#endif
+#if MXNET_USE_ONEDNN == 1
+ .set_attr<bool>("TIsDNNL", true)
+ .set_attr<FComputeEx>("FComputeEx<cpu>", AdaptiveAvgPoolOpBackwardExCPU)
+#endif
Review comment:
Use only one MXNET_USE_ONEDNN condition
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