remove unnecessary changes

src/include/migraphx/generate.hpp

@@ -81,15 +81,7 @@ template <class T>
 std::vector<T> generate_tensor_data(const migraphx::shape& s, unsigned long seed = 0)
 {
     std::vector<T> result(s.elements());
-    shape::type_t type = s.type();
-    if(type == shape::int64_type or type == shape::int32_type)
-    {
-        std::generate(result.begin(), result.end(), [] { return 1; });
-    }
-    else
-    {
-        std::generate(result.begin(), result.end(), xorshf96_generator<T>{seed});
-    }
+    std::generate(result.begin(), result.end(), xorshf96_generator<T>{seed});
 
     // std::generate(result.begin(), result.end(), [&]{ return seed % 7; });
     // std::generate(result.begin(), result.end(), []{ return 1; });

src/onnx/onnx.cpp

@@ -261,28 +261,15 @@ struct onnx_parser
         return prog.add_instruction(op, std::move(args));
     }
 
-    //    instruction_ref
-    //    parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
-    //    {
-    //        auto dims = args.front()->get_shape().lens();
-    //        auto r =
-    //            prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1]), 1, 1}},
-    //            args.front());
-    //        auto s = prog.add_instruction(op::softmax{}, r);
-    //        return prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1])}}, s);
-    //    }
-
-    instruction_ref parse_softmax(const std::string&,
-                                  const attribute_map& attributes,
-                                  std::vector<instruction_ref> args)
+    instruction_ref
+    parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
     {
-        int axis = 1;
-        if(contains(attributes, "axis"))
-        {
-            axis = parse_value(attributes.at("axis")).at<int>();
-        }
-
-        return prog.add_instruction(op::softmax{axis}, std::move(args));
+        auto dims = args.front()->get_shape().lens();
+        auto r =
+            prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1]), 1, 1}},
+            args.front());
+        auto s = prog.add_instruction(op::softmax{}, r);
+        return prog.add_instruction(op::reshape{{long(dims[0]), long(dims[1])}}, s);
     }
 
     instruction_ref parse_logsoftmax(const std::string&,

test/onnx/onnx_test.cpp

@@ -423,7 +423,9 @@ TEST_CASE(softmax_test)
 {
     migraphx::program p;
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 3}});
-    p.add_instruction(migraphx::op::softmax{1}, l0);
+    auto r  = p.add_instruction(migraphx::op::reshape{{1, 3, 1, 1}}, l0);
+    auto s  = p.add_instruction(migraphx::op::softmax{}, r);
+    p.add_instruction(migraphx::op::reshape{{1, 3}}, s);
     auto prog = migraphx::parse_onnx("softmax_test.onnx");
 
     EXPECT(p == prog);