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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2022/01/17 15:42:45 UTC
[GitHub] [incubator-mxnet] piotrwolinski-intel opened a new pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
piotrwolinski-intel opened a new pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825
## Description ##
This PR introduces oneDNN backward adaptive pooling implementation.
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1042713798
Jenkins CI successfully triggered : [centos-gpu, windows-gpu, centos-cpu]
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[GitHub] [incubator-mxnet] bartekkuncer commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
bartekkuncer commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r787896195
##########
File path: src/operator/nn/dnnl/dnnl_pooling.cc
##########
@@ -233,17 +234,30 @@ void InitPoolingPrimitiveParams(const PoolingParam& param,
dnnl::pooling_forward::primitive_desc GetPoolingFwdPdesc(const PoolingParam& param,
const bool is_train,
const dnnl::memory::desc& data_md,
- const dnnl::memory::desc& out_md) {
- CHECK(param.kernel.ndim() == 1 || param.kernel.ndim() == 2 || param.kernel.ndim() == 3)
- << "Not Implemented";
+ const dnnl::memory::desc& out_md,
+ const bool use_adaptive_pooling) {
+ CHECK((param.kernel.ndim() >= 1 && param.kernel.ndim() <= 3) || use_adaptive_pooling)
+ << "Not Implemented"; // to be changed
Review comment:
to be changed when?
##########
File path: src/operator/nn/dnnl/dnnl_pooling.cc
##########
@@ -384,22 +400,40 @@ DNNLPoolingBwd& GetPoolingBwd(const PoolingParam& param,
return it->second;
}
-void DNNLPoolingGradCompute(const OpContext& ctx,
- const PoolingParam& param,
- const NDArray& out_grad,
- const NDArray& in_data,
- const NDArray* workspace,
- const OpReqType req,
- const NDArray& in_grad) {
- if (req == kNullOp) {
+void DNNLPoolingGradCompute(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ if (req[0] == kNullOp) {
return;
}
+ const PoolingParam& param = nnvm::get<PoolingParam>(attrs.parsed);
+
+ const NDArray& out_grad = inputs[0];
+ const NDArray* workspace = nullptr;
+ const NDArray* in_data = nullptr;
+ if (DNNLRequireWorkspace(param)) {
+ // The first two elements are the gradient of the outputs in forward.
Review comment:
```suggestion
// The first two elements are the gradients of the outputs in forward.
```
##########
File path: src/operator/nn/dnnl/dnnl_pooling-inl.h
##########
@@ -28,10 +28,13 @@
#include <dnnl.hpp>
#include <utility>
+#include <vector>
#include "../pooling-inl.h"
#include "./dnnl_base-inl.h"
+#define DIV_ROUND_UP(a, b) (((a) + (b - 1)) / b)
Review comment:
Does a have to be in brackets?
##########
File path: src/operator/nn/dnnl/dnnl_pooling.cc
##########
@@ -357,21 +364,30 @@ DNNLPoolingBwd& GetPoolingBwd(const PoolingParam& param,
auto dst_md = dnnl::memory::desc(dst_dims, get_data_type(data_md), any);
// fwd hint
- auto fwd_pd = GetPoolingFwdPdesc(param, true, data_md, dst_md);
+ auto fwd_pd = GetPoolingFwdPdesc(param, true, data_md, dst_md, use_adaptive_pooling);
// creat bwd desc
Review comment:
create?
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -25,11 +25,13 @@
// #include "elemwise_op_common.h"
#include "../elemwise_op_common.h"
#if MXNET_USE_ONEDNN == 1
+#include "../nn/dnnl/dnnl_base-inl.h"
#include "../nn/dnnl/dnnl_pooling-inl.h"
#endif // MXNET_USE_ONEDNN
#define START_IND(a, b, c) static_cast<int>(std::floor(static_cast<float>(a * c) / b))
#define END_IND(a, b, c) static_cast<int>(std::ceil(static_cast<float>((a + 1) * c) / b))
+#define DIV_ROUND_UP(a, b) (((a) + (b - 1)) / b)
Review comment:
Does a have to be in brackets?
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[GitHub] [incubator-mxnet] bgawrych commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
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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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1016176907
Jenkins CI successfully triggered : [centos-gpu]
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1020885872
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1021089563
@mxnet-bot run ci [windows-gpu]
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r806644597
##########
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:
As I checked earlier I thought it was necessary to do it this way, but from what I've checked now it should be okay to make it similar way as in pooling for instance.
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r805828334
##########
File path: src/operator/nn/dnnl/dnnl_pooling.cc
##########
@@ -357,21 +364,30 @@ DNNLPoolingBwd& GetPoolingBwd(const PoolingParam& param,
auto dst_md = dnnl::memory::desc(dst_dims, get_data_type(data_md), any);
// fwd hint
- auto fwd_pd = GetPoolingFwdPdesc(param, true, data_md, dst_md);
+ auto fwd_pd = GetPoolingFwdPdesc(param, true, data_md, dst_md, use_adaptive_pooling);
// creat bwd desc
Review comment:
Yes, it was meant to be create.
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1021089620
Jenkins CI successfully triggered : [windows-gpu]
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
mxnet-bot commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1014674888
Hey @piotrwolinski-intel , Thanks for submitting the PR
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[GitHub] [incubator-mxnet] mxnet-bot commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1016176826
@mxnet-bot run ci [centos-gpu]
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r805824553
##########
File path: src/operator/nn/dnnl/dnnl_pooling-inl.h
##########
@@ -28,10 +28,13 @@
#include <dnnl.hpp>
#include <utility>
+#include <vector>
#include "../pooling-inl.h"
#include "./dnnl_base-inl.h"
+#define DIV_ROUND_UP(a, b) (((a) + (b - 1)) / b)
Review comment:
Not necessarily, changed for better readability.
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -25,11 +25,13 @@
// #include "elemwise_op_common.h"
#include "../elemwise_op_common.h"
#if MXNET_USE_ONEDNN == 1
+#include "../nn/dnnl/dnnl_base-inl.h"
#include "../nn/dnnl/dnnl_pooling-inl.h"
#endif // MXNET_USE_ONEDNN
#define START_IND(a, b, c) static_cast<int>(std::floor(static_cast<float>(a * c) / b))
#define END_IND(a, b, c) static_cast<int>(std::ceil(static_cast<float>((a + 1) * c) / b))
+#define DIV_ROUND_UP(a, b) (((a) + (b - 1)) / b)
Review comment:
Not necessarily, changed for better readability.
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[GitHub] [incubator-mxnet] bgawrych merged pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
bgawrych merged pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1020885811
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1015263228
@mxnet-bot run ci [unix-cpu, windows-gpu]
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1020885811
@mxnet-bot run ci [centos-cpu, unix-cpu, windows-gpu]
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#issuecomment-1042713722
@mxnet-bot run ci [centos-cpu, centos-gpu, windows-gpu]
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[GitHub] [incubator-mxnet] piotrwolinski-intel commented on a change in pull request #20825: [master] Implemented oneDNN Backward Adaptive Pooling kernel
Posted by GitBox <gi...@apache.org>.
piotrwolinski-intel commented on a change in pull request #20825:
URL: https://github.com/apache/incubator-mxnet/pull/20825#discussion_r806644597
##########
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:
As I checked earlier I thought it was necessary to do it this way, but from what I've checked now it should be okay to make it similar way as in pooling for instance.
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