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bgawrych pushed a commit to branch v1.x
in repository https://gitbox.apache.org/repos/asf/incubator-mxnet.git
The following commit(s) were added to refs/heads/v1.x by this push:
new ce1b597 [v1.x] Remove identity operators from oneDNN optimized graph
(#20868)
ce1b597 is described below
commit ce1b59790a0bc297349e757d6407e8250d82e78d
Author: bgawrych <[email protected]>
AuthorDate: Tue Feb 8 17:37:20 2022 +0100
[v1.x] Remove identity operators from oneDNN optimized graph (#20868)
* identity fuse
* rewrite test
* fix sanity
* remove clang warning
Co-authored-by: Bartlomiej Gawrych <[email protected]>
---
.../subgraph/mkldnn/mkldnn_identity_property.h | 173 +++++++++++++++++++++
.../subgraph/mkldnn/mkldnn_subgraph_base-inl.h | 2 +-
.../subgraph/mkldnn/mkldnn_subgraph_property.cc | 3 +
tests/python/mkl/test_subgraph.py | 28 ++++
4 files changed, 205 insertions(+), 1 deletion(-)
diff --git a/src/operator/subgraph/mkldnn/mkldnn_identity_property.h
b/src/operator/subgraph/mkldnn/mkldnn_identity_property.h
new file mode 100644
index 0000000..00f9499
--- /dev/null
+++ b/src/operator/subgraph/mkldnn/mkldnn_identity_property.h
@@ -0,0 +1,173 @@
+/*
+ * 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_identity_property.cc
+ * \brief Graph property for removing identity operators
+ */
+
+#ifndef MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_IDENTITY_PROPERTY_H_
+#define MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_IDENTITY_PROPERTY_H_
+#if MXNET_USE_MKLDNN == 1
+
+#include <map>
+#include <string>
+#include <vector>
+
+#include "../common.h"
+#include "../../nn/dropout-inl.h"
+#include "mkldnn_subgraph_base-inl.h"
+
+namespace mxnet {
+namespace op {
+
+class SgMKLDNNIdentitySelector : public SubgraphSelectorV2 {
+ private:
+ std::vector<const BiDirectedNode*> matched_list_;
+ bool pattern_found = false;
+
+ public:
+ bool Select(const BiDirectedNode& seed_node,
+ const std::shared_ptr<NodeAttr>& node_attr) override {
+ bool status = false;
+ if (seed_node.node->op() == Op::Get("_copy")) {
+ status = true;
+ }
+
+ if (seed_node.node->op() == Op::Get("Dropout")) {
+ auto const& dropout_param =
nnvm::get<DropoutParam>(seed_node.node->attrs.parsed);
+ if (dropout_param.mode == dropout::kTraining) {
+ status = true;
+ }
+ }
+
+ if (status) {
+ matched_list_.clear();
+ matched_list_.emplace_back(&seed_node);
+ return true;
+ }
+ return false;
+ }
+
+ bool SelectInput(const BiDirectedNode& n, const BiDirectedNode& input_node)
override {
+ if (pattern_found || input_node.node->is_variable()) {
+ return false;
+ } else if (input_node.node->op()) {
+ matched_list_.emplace_back(&input_node);
+ pattern_found = true;
+ return true;
+ }
+ return false;
+ }
+
+ bool SelectOutput(const BiDirectedNode& n, const BiDirectedNode&
output_node) override {
+ return false;
+ }
+
+ std::vector<BiDirectedNode*> Filter(const std::vector<BiDirectedNode*>&
candidates) override {
+ // candidates should contain only two nodes - custom node and identity node
+ if (pattern_found && candidates.size() == matched_list_.size()) {
+ CHECK_EQ(candidates.size(), 2);
+ return candidates;
+ } else {
+ return std::vector<BiDirectedNode*>(0);
+ }
+ }
+
+ void Reset() override {
+ CHECK_GE(matched_list_.size(), 1);
+ auto new_selector = SgMKLDNNIdentitySelector();
+ new_selector.Select(*matched_list_[0], nullptr);
+ *this = new_selector;
+ }
+};
+
+inline bool IsIdentityNode(const nnvm::ObjectPtr node) {
+ return node->op() && (node->op() == Op::Get("_copy") || node->op() ==
Op::Get("Dropout"));
+}
+
+class SgMKLDNNIdentityProperty : public SubgraphProperty {
+ public:
+ SgMKLDNNIdentityProperty() {}
+
+ static SubgraphPropertyPtr Create() {
+ static const std::string& name = "MKLDNN Identity optimization passs";
+ auto property =
std::make_shared<SgMKLDNNIdentityProperty>();
+ property->SetAttr<std::string>("property_name", name);
+ property->SetAttr<bool>("inference_only", true);
+ return property;
+ }
+
+ nnvm::ObjectPtr CreateSubgraphNode(const nnvm::Symbol& sym,
+ const int subgraph_id = 0) const override
{
+ nnvm::NodeEntry identity_node_entry;
+ for (auto entry : sym.outputs) {
+ if (IsIdentityNode(entry.node)) {
+ identity_node_entry = entry;
+ }
+ }
+
+ auto last_node = identity_node_entry.node;
+ nnvm::Symbol new_sym;
+ new_sym.outputs.emplace_back(last_node);
+
+ nnvm::ObjectPtr org_node;
+ DFSVisit(new_sym.outputs, [&](const nnvm::ObjectPtr& node) {
+ if (!IsIdentityNode(node)) {
+ org_node = node;
+ }
+ });
+
+ // Create copy of original node
+ nnvm::ObjectPtr n = nnvm::Node::Create();
+ n->attrs = org_node->attrs;
+ if (n->op() && n->op()->attr_parser) {
+ n->op()->attr_parser(&(n->attrs));
+ }
+
+ return n;
+ }
+
+ void ConnectSubgraphOutputs(const nnvm::ObjectPtr n,
+ std::vector<nnvm::NodeEntry*>* output_entries)
const override {
+ // output of identity must be connected as output of operator before
identity
+ // e.g. for: /--index 0--> custom_op
+ // (n) slice
+ // \--index 1--> Dropout --index 0--> OUT_NODE
+ // for OUT_NODE index 0 must be changed to index 1
+ for (size_t i = 0; i < output_entries->size(); ++i) {
+ auto out_node = output_entries->at(i)->node;
+ if (IsIdentityNode(out_node)) {
+ output_entries->at(i)->index = out_node->inputs[0].index;
+ }
+ output_entries->at(i)->node = n;
+ }
+ }
+
+ SubgraphSelectorV2Ptr CreateSubgraphSelectorV2() const override {
+ auto selector = std::make_shared<SgMKLDNNIdentitySelector>();
+ return selector;
+ }
+};
+
+} // namespace op
+} // namespace mxnet
+
+#endif // if MXNET_USE_MKLDNN == 1
+#endif // MXNET_OPERATOR_SUBGRAPH_MKLDNN_MKLDNN_IDENTITY_PROPERTY_H_
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
b/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
index 6436852..9e0f9bc 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_base-inl.h
@@ -31,7 +31,7 @@ static inline bool SupportMKLDNNAttr(const
std::shared_ptr<NodeAttr>& node_attr)
return (node_attr->dispatch_mode == DispatchMode::kFComputeEx) &&
(node_attr->itype[0] == mshadow::kFloat32 ||
node_attr->itype[0] == mshadow::kBfloat16) &&
- (ndim == 1 || ndim == 2 || ndim == 4 || ndim == 5);
+ (ndim >= 1 && ndim <= 5);
} else {
return true;
}
diff --git a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
index 2701e0d..bc90407 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_subgraph_property.cc
@@ -24,6 +24,7 @@
#include "mkldnn_fc_post_quantize_property.h"
#include "mkldnn_fc_property.h"
#include "mkldnn_fc_sum_fuse.h"
+#include "mkldnn_identity_property.h"
#include "mkldnn_post_quantize_align_scale_property.h"
#include "mkldnn_post_quantize_property.h"
#include "mkldnn_transformer_post_quantize_property.h"
@@ -36,6 +37,7 @@ MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN)
.set_attr("enable", MKLDNNEnvSet())
.set_attr("context", Context::CPU());
+MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNIdentityProperty);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNConvProperty);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNFCProperty);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN, SgMKLDNNTransformerProperty);
@@ -43,6 +45,7 @@ MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN,
SgMKLDNNFCSumFuseProperty);
MXNET_REGISTER_SUBGRAPH_BACKEND(MKLDNN_QUANTIZE).set_attr("context",
Context::CPU());
+MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNIdentityProperty);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE,
SgMKLDNNConvProperty).set_attr("quantize", true);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE,
SgMKLDNNFCProperty).set_attr("quantize", true);
MXNET_REGISTER_SUBGRAPH_PROPERTY(MKLDNN_QUANTIZE, SgMKLDNNTransformerProperty);
diff --git a/tests/python/mkl/test_subgraph.py
b/tests/python/mkl/test_subgraph.py
index a6af10f..a948e8c 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -710,6 +710,28 @@ def fc_eltwise(no_bias, data_shape, flatten=True,
alg='relu'):
return sym, attr
+def fc_identity_eltwise(data_shape, identity_node):
+ attrs = {'sg_mkldnn_fully_connected_eltwise_0' : {'with_eltwise': 'true'},
+ 'sg_mkldnn_fully_connected_eltwise_1' : {'with_eltwise': 'true'}}
+ data, fc1_weight = head_symbol(data_shape)
+ fc2_weight = mx.symbol.Variable('fc2_weight', dtype='float32')
+
+ sym = mx.symbol.FullyConnected(name='fc1', data=data, weight=fc1_weight,
num_hidden=64,
+ no_bias=True, flatten=True)
+ if identity_node == 'copy':
+ sym = mx.symbol.identity(sym)
+ else:
+ sym = mx.symbol.Dropout(sym)
+ sym = mx.symbol.Activation(sym, act_type='relu')
+ sym = mx.symbol.FullyConnected(name='fc2', data=sym, weight=fc2_weight,
num_hidden=64,
+ no_bias=True, flatten=True)
+ if identity_node == 'copy':
+ sym = mx.symbol.identity(sym)
+ else:
+ sym = mx.symbol.Dropout(sym)
+ sym = mx.symbol.Activation(sym, act_type='relu')
+ return sym, attrs
+
def single_selfatt_qk(data_shape, nheads=16):
attr = {'selfatt_qk': {}}
data = mx.symbol.Variable('data', shape=data_shape, dtype='float32')
@@ -1001,6 +1023,12 @@ def test_single_fc():
check_fusion(syms, dshape, attrs, check_quantization=False)
@with_seed()
+def test_fc_eltwise_identity():
+ for dshape, identity_node in itertools.product(DATA_SHAPE, ['copy',
'dropout']):
+ syms, attrs = fc_identity_eltwise(dshape, identity_node)
+ check_fusion(syms, dshape, attrs, check_quantization=False)
+
+@with_seed()
def test_fc_eltwise():
for dshape, no_bias, flatten, alg in itertools.product(DATA_SHAPE,
[True, False],
