Merge pull request #3174 from NervanaSystems/rearhart/plaidml

Minor PlaidML fixes

src/ngraph/CMakeLists.txt

@@ -419,8 +419,6 @@ set (SRC
     pass/pass.hpp
     pass/pass_config.cpp
     pass/pass_config.hpp
-    pass/prefix_reshape_elimination.cpp
-    pass/prefix_reshape_elimination.hpp
     pass/propagate_cacheability.cpp
     pass/propagate_cacheability.hpp
     pass/reshape_elimination.cpp

src/ngraph/runtime/plaidml/CMakeLists.txt

@@ -55,6 +55,7 @@ set(SRC
     plaidml_pass_explicit_logicals.cpp
     plaidml_pass_implicit_broadcast.cpp
     plaidml_pass_lower_convolutions.cpp
+    plaidml_pass_prefix_reshape_elimination.cpp
     plaidml_pass_replicate_combination.cpp
     plaidml_pass_replicate_elision.cpp
     plaidml_pass_winograd.cpp

src/ngraph/runtime/plaidml/plaidml_compiler.cpp

@@ -26,7 +26,6 @@
 #include "ngraph/pass/liveness.hpp"
 #include "ngraph/pass/manager.hpp"
 #include "ngraph/pass/nop_elimination.hpp"
-#include "ngraph/pass/prefix_reshape_elimination.hpp"
 #include "ngraph/pass/visualize_tree.hpp"
 #include "ngraph/pass/zero_dim_tensor_elimination.hpp"
 #include "ngraph/runtime/plaidml/plaidml_impl.hpp"
@@ -36,6 +35,7 @@
 #include "ngraph/runtime/plaidml/plaidml_pass_explicit_logicals.hpp"
 #include "ngraph/runtime/plaidml/plaidml_pass_implicit_broadcast.hpp"
 #include "ngraph/runtime/plaidml/plaidml_pass_lower_convolutions.hpp"
+#include "ngraph/runtime/plaidml/plaidml_pass_prefix_reshape_elimination.hpp"
 #include "ngraph/runtime/plaidml/plaidml_pass_replicate_combination.hpp"
 #include "ngraph/runtime/plaidml/plaidml_pass_replicate_elision.hpp"
 #include "ngraph/runtime/plaidml/plaidml_pass_winograd.hpp"
@@ -44,8 +44,7 @@ namespace
 {
     void write_debug(const ngraph::Node& op)
     {
-        PLAIDML_DEBUG << "Node: name=\"" << op.get_name() << "\" desc=\"" << op.description()
-                      << "\"";
+        PLAIDML_DEBUG << "Compiling: " << op;
         for (const auto& op_input : op.get_inputs())
         {
             ngraph::descriptor::Tensor* tensor = op_input.get_output().get_tensor_ptr().get();
@@ -104,7 +103,7 @@ std::shared_ptr<ngraph::runtime::plaidml::PlaidML_Executable>
     pass_manager.register_pass<ngraph::runtime::plaidml::pass::ReplicateElision>();
     pass_manager.register_pass<ngraph::runtime::plaidml::pass::ReplicateCombination>();
     pass_manager.register_pass<ngraph::runtime::plaidml::pass::ImplicitBroadcast>();
-    pass_manager.register_pass<ngraph::pass::PrefixReshapeElimination>();
+    pass_manager.register_pass<ngraph::runtime::plaidml::pass::PrefixReshapeElimination>();
     pass_manager.register_pass<ngraph::runtime::plaidml::pass::LowerConvolutions>();
     if (pass_manager.get_pass_config().get_pass_enable("Winograd"))
     {

src/ngraph/runtime/plaidml/plaidml_config.cpp

@@ -163,6 +163,12 @@ ngraph::runtime::plaidml::Config
         // So to verify that there is a non-zero-length option value, test oval_len
         // To verify that there is no option value, test has_oval
 
+        if (oname_begin == oname_end && !has_oval)
+        {
+            // An empty option; poor style, but advance to the next.
+            continue;
+        }
+
         // Check for verbosity
         if (is_opt("v"))
         {

src/ngraph/runtime/plaidml/plaidml_ops_replicate.cpp

@@ -53,7 +53,7 @@ ngraph::runtime::plaidml::op::Replicate::Replicate(std::shared_ptr<Node> arg,
 
 void ngraph::runtime::plaidml::op::Replicate::validate_and_infer_types()
 {
-    const auto& arg = get_arguments().at(0);
+    std::shared_ptr<Node> arg = get_argument(0);
     Shape shape = arg->get_shape();
     for (auto rit = m_replication_axes.begin(), sit = shape.begin();
          rit != m_replication_axes.end();

src/ngraph/runtime/plaidml/plaidml_pass_implicit_broadcast.cpp

@@ -15,7 +15,7 @@
 //*****************************************************************************
 
 #include "ngraph/runtime/plaidml/plaidml_pass_implicit_broadcast.hpp"
-#include "ngraph/graph_util.hpp"
+#include "ngraph/check.hpp"
 #include "ngraph/op/broadcast.hpp"
 #include "ngraph/op/reshape.hpp"
 #include "ngraph/op/util/binary_elementwise_arithmetic.hpp"
@@ -76,9 +76,28 @@ ngraph::runtime::plaidml::pass::ImplicitBroadcast::ImplicitBroadcast()
         auto implicit_broadcast =
             std::make_shared<plaidml::op::ImplicitBroadcast>(src, broadcast->get_shape());
 
-        replace_node(broadcast, implicit_broadcast);
+        // N.B. We don't use replace_node() here, since it's important to only replace the broadcast with an
+        // implicit broadcast when the consuming operation is an elementwise operation, since PlaidML
+        // contractions don't provide implicit broadcast semantics.
+        bool result = false;
+        for (size_t i = 0; i < broadcast->get_output_size(); ++i)
+        {
+            for (auto& input : broadcast->output(i).get_target_inputs())
+            {
+                Node* node = input.get_node();
+                if (dynamic_cast<ngraph::op::util::UnaryElementwiseArithmetic*>(node) ||
+                    dynamic_cast<ngraph::op::util::BinaryElementwiseArithmetic*>(node))
+                {
+                    input.replace_source_output(implicit_broadcast->output(i));
+                    result = true;
+                }
+            }
+        }
 
-        return true;
+        NGRAPH_CHECK(result,
+                     "Expected at least one elementwise consumer in the PlaidML implicit broadcast "
+                     "rewrite graph pass");
+        return result;
     };
     add_matcher(std::make_shared<pattern::Matcher>(target_op), callback);
 }

src/ngraph/runtime/plaidml/plaidml_pass_lower_convolutions.cpp

@@ -75,19 +75,19 @@ ngraph::runtime::plaidml::pass::LowerConvolutions::LowerConvolutions()
         // op.  Using target always works.
         AxisVector out_axes = to_axes(target, output_transpose);
 
-        auto lhs = node->get_arguments().at(0);
+        auto lhs = node->get_argument(0);
         auto* lhs_transpose = to_transpose(lhs);
         if (lhs_transpose)
         {
-            lhs = lhs_transpose->get_arguments().at(0);
+            lhs = lhs_transpose->get_argument(0);
         }
         AxisVector lhs_axes = to_axes(lhs, lhs_transpose);
 
-        auto rhs = node->get_arguments().at(1);
+        auto rhs = node->get_argument(1);
         auto* rhs_transpose = to_transpose(rhs);
         if (rhs_transpose)
         {
-            rhs = rhs_transpose->get_arguments().at(0);
+            rhs = rhs_transpose->get_argument(0);
         }
         AxisVector rhs_axes = to_axes(rhs, rhs_transpose);
 

src/ngraph/pass/prefix_reshape_elimination.cpp → src/ngraph/runtime/plaidml/plaidml_pass_prefix_reshape_elimination.cpp

@@ -14,7 +14,7 @@
 // limitations under the License.
 //*****************************************************************************
 
-#include "ngraph/pass/prefix_reshape_elimination.hpp"
+#include "ngraph/runtime/plaidml/plaidml_pass_prefix_reshape_elimination.hpp"
 #include "ngraph/graph_util.hpp"
 #include "ngraph/op/reshape.hpp"
 #include "ngraph/op/util/binary_elementwise_arithmetic.hpp"
@@ -23,11 +23,12 @@
 #include "ngraph/pattern/op/any.hpp"
 #include "ngraph/pattern/op/any_of.hpp"
 #include "ngraph/pattern/op/label.hpp"
+#include "ngraph/runtime/plaidml/plaidml_ops_implicit_broadcast.hpp"
 
 using namespace std;
 using namespace ngraph;
 
-pass::PrefixReshapeElimination::PrefixReshapeElimination()
+runtime::plaidml::pass::PrefixReshapeElimination::PrefixReshapeElimination()
 {
     auto src_op = make_shared<pattern::op::Label>(
         element::i8, Shape{}, [](shared_ptr<Node>) { return true; });
@@ -35,7 +36,7 @@ pass::PrefixReshapeElimination::PrefixReshapeElimination()
         element::i8,
         Shape{},
         [](shared_ptr<Node> node) {
-            op::Reshape* reshape = dynamic_cast<op::Reshape*>(node.get());
+            ngraph::op::Reshape* reshape = dynamic_cast<ngraph::op::Reshape*>(node.get());
             if (!reshape)
             {
                 return false;
@@ -71,16 +72,42 @@ pass::PrefixReshapeElimination::PrefixReshapeElimination()
         element::i8,
         Shape{},
         [](shared_ptr<Node> node) {
-            return pattern::has_class<op::util::UnaryElementwiseArithmetic>()(node) ||
-                   pattern::has_class<op::util::BinaryElementwiseArithmetic>()(node);
+            return pattern::has_class<ngraph::op::util::UnaryElementwiseArithmetic>()(node) ||
+                   pattern::has_class<ngraph::op::util::BinaryElementwiseArithmetic>()(node);
         },
         NodeVector{reshape_op});
 
     auto callback = [](pattern::Matcher& m) {
-        replace_node(m.get_matched_nodes().at(1), m.get_matched_nodes().at(2));
-        return true;
+        auto src = m.get_matched_nodes().at(2);
+        auto prefix_reshape =
+            std::static_pointer_cast<ngraph::op::Reshape>(m.get_matched_nodes().at(1));
+        auto implicit_broadcast =
+            std::make_shared<op::ImplicitBroadcast>(src, prefix_reshape->get_shape());
+
+        // N.B. We don't use replace_node() here, since it's important to only replace the prefix reshape with
+        // an implicit broadcast when the consuming operation is an elementwise operation, since PlaidML
+        // contractions don't provide implicit broadcast semantics.
+        bool result = false;
+        for (size_t i = 0; i < prefix_reshape->get_output_size(); ++i)
+        {
+            for (auto& input : prefix_reshape->output(i).get_target_inputs())
+            {
+                Node* node = input.get_node();
+                if (dynamic_cast<ngraph::op::util::UnaryElementwiseArithmetic*>(node) ||
+                    dynamic_cast<ngraph::op::util::BinaryElementwiseArithmetic*>(node))
+                {
+                    input.replace_source_output(implicit_broadcast->output(i));
+                    result = true;
+                }
+            }
+        }
+
+        NGRAPH_CHECK(result,
+                     "Expected at least one elementwise consumer in the PlaidML implicit broadcast "
+                     "rewrite graph pass");
+        return result;
     };
     add_matcher(make_shared<pattern::Matcher>(target_op, "PrefixReshapeElimination"),
                 callback,
-                PassProperty::REQUIRE_STATIC_SHAPE);
+                ngraph::pass::PassProperty::REQUIRE_STATIC_SHAPE);
 }

src/ngraph/pass/prefix_reshape_elimination.hpp → src/ngraph/runtime/plaidml/plaidml_pass_prefix_reshape_elimination.hpp

@@ -20,19 +20,23 @@
 
 namespace ngraph
 {
-    namespace pass
+    namespace runtime
     {
-        class PrefixReshapeElimination;
+        namespace plaidml
+        {
+            namespace pass
+            {
+                class PrefixReshapeElimination;
+            }
+        }
     }
 }
 
-// A pass to eliminate reshapes whose output shapes are the same as
-// their input shape modulo leading size-1 axes.
-//
-// N.B. This pass MUST only be used by backends that can handle the
-//      omission of leading size-1 axes, e.g. backends that implement
-//      NumPy-style broadcast semantics.
-class ngraph::pass::PrefixReshapeElimination final : public ngraph::pass::GraphRewrite
+// A pass that matches reshapes whose output shapes are the same as
+// their input shape modulo leading size-1 axes, and replaces them with
+// ImplicitBroadcast operations (which do the same thing as a passthrough).
+class ngraph::runtime::plaidml::pass::PrefixReshapeElimination final
+    : public ngraph::pass::GraphRewrite
 {
 public:
     PrefixReshapeElimination();

src/ngraph/runtime/plaidml/plaidml_pass_replicate_combination.cpp

@@ -47,9 +47,9 @@ ngraph::runtime::plaidml::pass::ReplicateCombination::ReplicateCombination()
             *ait *= *uit;
         }
 
-        replace_node(lower,
-                     std::make_shared<plaidml::op::Replicate>(upper->get_arguments().at(0),
-                                                              std::move(axes)));
+        replace_node(
+            lower,
+            std::make_shared<plaidml::op::Replicate>(upper->get_argument(0), std::move(axes)));
 
         return true;
     };

src/ngraph/runtime/plaidml/plaidml_pass_replicate_elision.cpp

@@ -74,7 +74,7 @@ ngraph::runtime::plaidml::pass::ReplicateElision::ReplicateElision()
             if (elidable)
             {
                 replaced_any = true;
-                replace_node(replicate, replicate->get_arguments().at(0));
+                replace_node(replicate, replicate->get_argument(0));
             }
         }