bgawrych commented on a change in pull request #19896:
URL: https://github.com/apache/incubator-mxnet/pull/19896#discussion_r590361512
##########
File path: src/operator/contrib/adaptive_avg_pooling-inl.h
##########
@@ -156,5 +161,13 @@ MSHADOW_XINLINE int get_stride(Tensor<xpu, Dim, DType>
tensor, int idx) {
} // namespace op
} // namespace mxnet
-
+namespace std {
+template <>
+struct hash<mxnet::op::AdaptiveAvgPoolParam> {
Review comment:
Maybe it would be worth to comment why here we returning just 0? If I
understand correctly only param of this OP determines output size and shape is
already part of NDArray passed to AddSign function
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -197,6 +198,91 @@
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
}
}
+#if MXNET_USE_MKLDNN == 1
+bool CanMKLDNNSupportAveragePooling(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_1 = floor((IH << 1) / OH) - floor(IH / OH);
+ const int strides_2 = floor((IW << 1) / OW) - floor(IW / OW);
+ const int kernel_1 = ceil((IH << 1) / OH) - floor(IH / OH);
+ const int kernel_2 = ceil((IW << 1) / OW) - floor(IW / OW);
+ const int pad_l_top = (strides_1 * (OH - 1) + kernel_1 - IH) / 2;
+ const int pad_l_left = (strides_2 * (OW - 1) + kernel_2 - IW) / 2;
+
+ return pad_l_top == 0 && pad_l_left == 0;
+}
+
+
+void AdaptiveAvgPoolComputeExCPU(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector<NDArray> &inputs,
+ const std::vector<OpReqType> &req,
+ const std::vector<NDArray> &outputs) {
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ /*
+ DNNL doesn't support adaptive pooling.
+ Fallback is needed when padding is not equal 0;
+ */
+ if (SupportMKLDNN(inputs[0]) &&
+ CanMKLDNNSupportAveragePooling(inputs[0], outputs[0])) {
+ const AdaptiveAvgPoolParam ¶m =
+ nnvm::get<AdaptiveAvgPoolParam>(attrs.parsed);
+ const NDArray *workspace = nullptr;
+ MKLDNN_OPCHECK_INIT(false, 1, inputs, outputs);
+ MKLDNNAdaptivePoolingCompute(ctx, param, inputs[0], req[0], outputs[0],
+ workspace);
+ 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_MKLDNN == 1
+ if (!dispatched) {
+ dispatched = MKLDNNStorageType(attrs, dev_mask, true, dispatch_mode,
+ in_attrs, out_attrs);
+ }
+ if (!MKLDNNEnvSet()) {
Review comment:
MKLDNNEnvSet is already called in MKLDNNStorageType, so probably there
is no need to second check
##########
File path: src/operator/nn/mkldnn/mkldnn_adaptive_pooling.cc
##########
@@ -0,0 +1,98 @@
+/*
+ * 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_adaptive_pooling.cc
+ * \brief
+ * \author Mateusz Ozga
+*/
+
+#if MXNET_USE_MKLDNN == 1
+
+#include "./mkldnn_adaptive_pooling-inl.h"
+
+namespace mxnet {
+namespace op {
+void MKLDNNAdaptivePoolingFwd::Init(
+ const mxnet::NDArray &input, const mxnet::NDArray &output,
+ const mkldnn::memory::dims &kernel, const mkldnn::memory::dims &strides,
+ const mkldnn::memory::dims &pad_l, const mkldnn::memory::dims &pad_r,
+ const bool is_train, const mkldnn::algorithm alg_kind) {
+ const auto src_md = input.GetMKLDNNData()->get_desc();
+ const auto dst_md = GetMemDesc(output);
+ const mkldnn::engine engine = CpuEngine::Get()->get_engine();
+
+ if (alg_kind != mkldnn::algorithm::pooling_avg &&
+ alg_kind != mkldnn::algorithm::pooling_avg_include_padding &&
+ alg_kind != mkldnn::algorithm::pooling_avg_exclude_padding) {
+ LOG(FATAL) << "MKLDNN Adaptive Pooling: algorithm is not supported";
+ }
+
+ mkldnn::prop_kind prop = mkldnn::prop_kind::forward_scoring;
+ if (is_train && alg_kind != mkldnn::algorithm::pooling_avg) {
+ prop = mkldnn::prop_kind::forward_training;
+ }
+ if (is_train && prop == mkldnn::prop_kind::forward_scoring) {
+ LOG(INFO) << "MKLDNN Pooling: training with prop_kind is forward_scoring";
+ }
+
+ const auto fwd_desc = mkldnn::pooling_forward::desc(
+ prop, alg_kind, src_md, dst_md, strides, kernel, pad_l, pad_r);
+ this->fwd_pd_.reset(
+ new mkldnn::pooling_forward::primitive_desc(fwd_desc, engine));
+ this->fwd_.reset(new mkldnn::pooling_forward(*(this->fwd_pd_)));
+}
+
+void MKLDNNAdaptivePoolingFwd::Execute(const NDArray &input,
+ const OpReqType req,
+ const NDArray &output,
+ const NDArray *workspace) {
+ NDArray in_buffer = input;
+ if (input.IsView() && input.IsMKLDNNData()) {
+ in_buffer = input.Reorder2Default();
+ }
+
+ auto input_mem = in_buffer.GetMKLDNNData();
+ auto output_mem_t = CreateMKLDNNMem(output, this->fwd_pd_->dst_desc(), req);
+
+ mkldnn_args_map_t args = {{MKLDNN_ARG_SRC, *input_mem},
+ {MKLDNN_ARG_DST, *(output_mem_t.second)}};
+
+ if (this->with_workspace_) {
+ auto engine = CpuEngine::Get()->get_engine();
+ if (workspace == nullptr) {
+ LOG(FATAL) << "MKLDNN Average Pooling: incorrect worskapce input";
+ }
+ auto ws = std::make_shared<mkldnn::memory>(
+ (*(this->fwd_pd_)).workspace_desc(), engine,
Review comment:
Can't it be just this->fwd_pd_->workspace_desc() ?
##########
File path: src/operator/contrib/adaptive_avg_pooling.cc
##########
@@ -197,6 +198,91 @@
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
}
}
+#if MXNET_USE_MKLDNN == 1
+bool CanMKLDNNSupportAveragePooling(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_1 = floor((IH << 1) / OH) - floor(IH / OH);
+ const int strides_2 = floor((IW << 1) / OW) - floor(IW / OW);
+ const int kernel_1 = ceil((IH << 1) / OH) - floor(IH / OH);
+ const int kernel_2 = ceil((IW << 1) / OW) - floor(IW / OW);
+ const int pad_l_top = (strides_1 * (OH - 1) + kernel_1 - IH) / 2;
+ const int pad_l_left = (strides_2 * (OW - 1) + kernel_2 - IW) / 2;
+
+ return pad_l_top == 0 && pad_l_left == 0;
+}
+
+
+void AdaptiveAvgPoolComputeExCPU(const nnvm::NodeAttrs &attrs,
+ const OpContext &ctx,
+ const std::vector<NDArray> &inputs,
+ const std::vector<OpReqType> &req,
+ const std::vector<NDArray> &outputs) {
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ /*
+ DNNL doesn't support adaptive pooling.
Review comment:
oneDNN ?
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