Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into scalar_parsing

src/include/migraphx/op/common.hpp

@@ -31,6 +31,8 @@ enum class rnn_direction
     bidirectional,
 };
 
+std::ostream& operator<<(std::ostream& os, rnn_direction v);
+
 } // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/op/gather.hpp

@@ -19,6 +19,13 @@ namespace op {
 struct gather
 {
     int axis = 0;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+
     std::string name() const { return "gather"; }
 
     shape compute_shape(std::vector<shape> inputs) const

src/include/migraphx/op/gru.hpp

@@ -27,6 +27,16 @@ struct gru
     float clip              = 0.0f;
     int linear_before_reset = 0;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.hidden_size, "hidden_size"),
+                    f(self.actv_funcs, "actv_func"),
+                    f(self.direction, "direction"),
+                    f(self.clip, "clip"),
+                    f(self.linear_before_reset, "linear_before_reset"));
+    }
+
     std::string name() const { return "gru"; }
     shape compute_shape(std::vector<shape> inputs) const
     {

src/include/migraphx/op/logsoftmax.hpp

@@ -19,6 +19,13 @@ namespace op {
 struct logsoftmax
 {
     int axis = 1;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+
     std::string name() const { return "logsoftmax"; }
     shape compute_shape(std::vector<shape> inputs) const
     {

src/include/migraphx/op/lstm.hpp

@@ -25,6 +25,15 @@ struct lstm
     float clip              = 0.0f;
     int input_forget        = 0;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.hidden_size, "hidden_size"),
+                    f(self.actv_funcs, "actv_func"),
+                    f(self.direction, "direction"),
+                    f(self.input_forget, "input_forget"));
+    }
+
     std::string name() const { return "lstm"; }
     shape compute_shape(std::vector<shape> inputs) const
     {

src/include/migraphx/op/rnn.hpp

@@ -25,6 +25,15 @@ struct rnn
     rnn_direction direction = rnn_direction::forward;
     float clip              = 0.0f;
 
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.hidden_size, "hidden_size"),
+                    f(self.actv_funcs, "actv_func"),
+                    f(self.direction, "direction"),
+                    f(self.clip, "clip"));
+    }
+
     std::string name() const { return "rnn"; }
     shape compute_shape(std::vector<shape> inputs) const
     {

src/rewrite_rnn.cpp

@@ -4,6 +4,7 @@
 #include <migraphx/operators.hpp>
 #include <migraphx/iterator_for.hpp>
 #include <migraphx/dfor.hpp>
+#include <migraphx/op/common.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -1166,5 +1167,14 @@ std::vector<operation> rewrite_rnn::lstm_actv_funcs(instruction_ref ins) const
     }
 }
 
+namespace op {
+std::ostream& operator<<(std::ostream& os, rnn_direction v)
+{
+    std::vector<std::string> rnn_direction_str = {"forward", "reverse", "bidirectional"};
+    os << rnn_direction_str[static_cast<std::underlying_type<rnn_direction>::type>(v)];
+    return os;
+}
+} // namespace op
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/tf/tf.cpp

@@ -124,6 +124,7 @@ struct tf_parser
         add_mem_op("Reshape", &tf_parser::parse_reshape);
         add_mem_op("Softmax", &tf_parser::parse_softmax);
         add_mem_op("Squeeze", &tf_parser::parse_squeeze);
+        add_mem_op("StridedSlice", &tf_parser::parse_stridedslice);
     }
 
     template <class F>
@@ -508,6 +509,46 @@ struct tf_parser
         return prog.add_instruction(op, args[0]);
     }
 
+    instruction_ref parse_stridedslice(const std::string&,
+                                       const attribute_map& attributes,
+                                       std::vector<instruction_ref> args)
+    {
+        op::slice op;
+        auto starts     = args[1]->eval().get<int32_t>().to_vector();
+        auto ends       = args[2]->eval().get<int32_t>().to_vector();
+        size_t num_axes = args[0]->get_shape().lens().size();
+        if(num_axes >= 4)
+        {
+            reorder_data(starts);
+            reorder_data(ends);
+        }
+
+        op.starts = std::vector<int64_t>(starts.begin(), starts.end());
+        op.ends   = std::vector<int64_t>(ends.begin(), ends.end());
+        op.axes   = std::vector<int64_t>(num_axes);
+        std::iota(op.axes.begin(), op.axes.end(), 0);
+        uint32_t shrink_axis_mask = 0;
+        uint32_t bitwise_compare  = 1;
+        std::vector<int64_t> squeeze_axes;
+
+        if(contains(attributes, "shrink_axis_mask"))
+            shrink_axis_mask = static_cast<uint32_t>(attributes.at("shrink_axis_mask").i());
+
+        for(size_t i = 0; i < num_axes; i++)
+        {
+            // the LSB corresponds to axis 0 when determining which axes to squeeze
+            if(((shrink_axis_mask >> i) & bitwise_compare) == 1)
+                squeeze_axes.push_back(i);
+        }
+        if(num_axes >= 4)
+        {
+            squeeze_axes = parse_axes(squeeze_axes);
+        }
+
+        auto l0 = prog.add_instruction(op, args[0]);
+        return prog.add_instruction(op::squeeze{squeeze_axes}, l0);
+    }
+
     void parse_graph(const tensorflow::GraphDef& graph)
     {
         nodes = get_nodes(graph, input_nodes);

test/tf/tf_test.cpp

@@ -237,4 +237,28 @@ TEST_CASE(squeeze_test)
 
     EXPECT(p == prog);
 }
+
+TEST_CASE(stridedslice_test)
+{
+    migraphx::program p;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 10, 1, 1}});
+    std::size_t num_axes = 4;
+    migraphx::op::slice op;
+    op.starts = {0, 0, 0, 0};
+    op.ends   = {1, 5, 1, 1};
+    op.axes   = std::vector<int64_t>(num_axes);
+    std::iota(op.axes.begin(), op.axes.end(), 0);
+    // add literals for starts, ends, and strides in tf (NHWC format)
+    p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{0, 0, 0, 0});
+    p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{1, 1, 1, 5});
+    p.add_literal(migraphx::shape{migraphx::shape::int32_type, {4}}, std::vector<int>{1, 1, 1, 1});
+
+    auto l1          = p.add_instruction(op, l0);
+    auto shrink_axis = 2;
+    p.add_instruction(migraphx::op::squeeze{{shrink_axis}}, l1);
+    auto prog = migraphx::parse_tf("stridedslice_test.pb", true);
+
+    EXPECT(p == prog);
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }