Re-try add of op. random_seed and remove use_auto_seed.

src/include/migraphx/op/rand_uniform.hpp

@@ -31,8 +31,9 @@
  *
  *      Inputs:   (1) the shape of the set to be populated.
  *                (2) randomization seed (uint32).  If not given at inference time, the attribute
- * value, or auto seeding, will be used. Attributes: use_auto_seed bool       Have hardware generate
- * random seed at runtime, overriding the attribute seed seed          uint32     Randomization seed
+ * value, or auto seeding, will be used.
+ *
+ *       Attributes:  seed          uint32     Randomization seed
  *
  *      Output:   Same shape.
  *
@@ -52,8 +53,7 @@ namespace op {
 
 struct rand_uniform
 {
-    uint32_t seed      = {0};
-    bool use_auto_seed = false;
+    uint32_t seed = {0};
 
     // todo:  not currently settable
     float range_min = 0.0f;
@@ -65,8 +65,7 @@ struct rand_uniform
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(
-            f(self.dtype, "dtype"), f(self.seed, "seed"), f(self.use_auto_seed, "use_auto_seed"));
+        return pack(f(self.dtype, "dtype"), f(self.seed, "seed"));
     }
 
     std::string name() const { return "rand_uniform"; }

src/include/migraphx/op/random_seed.hpp

@@ -36,11 +36,11 @@ inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
 /**
- *    Generates a random seed for the use of random number generators.  Generating the seed 
+ *    Generates a random seed for the use of random number generators.  Generating the seed
  * at runtime guarantees there will be a different random sequence on every execution.
- * This operation has no inputs or attributes, and outputs an unsigned integer tensor with 
+ * This operation has no inputs or attributes, and outputs an unsigned integer tensor with
  * a single value.
-*/
+ */
 struct random_seed
 {
     shape::type_t dtype = shape::type_t::uint32_type;
@@ -48,27 +48,27 @@ struct random_seed
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(
-            f(self.dtype, "dtype"));
+        return pack(f(self.dtype, "dtype"));
     }
 
     std::string name() const { return "random_seed"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-            (void) inputs;
-            return migraphx::shape(dtype, {1});
+        (void)inputs;
+        return migraphx::shape(dtype, {1});
     }
 
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
-        (void) args;
+        (void)args;
         argument result(output_shape);
 
         result.visit([&](auto output) {
-            std::generate(output.begin(), output.end(), [&]() { return uint32_t(std::chrono::system_clock::now().time_since_epoch().count()); });
+            std::generate(output.begin(), output.end(), [&]() {
+                return uint32_t(std::chrono::system_clock::now().time_since_epoch().count());
+            });
         });
         return result;
-
     }
 };
 

test/ref_ops_test.cpp

@@ -6542,7 +6542,6 @@ TEST_CASE(rand_uniform_dyn_test)
     EXPECT(migraphx::verify::verify_range(result_vec, rand_samples, 100000));
 }
 
-
 TEST_CASE(rand_uniform_and_seed_test)
 {
     migraphx::program p;
@@ -6556,9 +6555,7 @@ TEST_CASE(rand_uniform_and_seed_test)
 
     // Runtime randomization seed
     auto seed_input = mm->add_instruction(migraphx::make_op("random_seed"));
-    mm->add_instruction(migraphx::make_op("rand_uniform"),
-                        input,
-                        seed_input);
+    mm->add_instruction(migraphx::make_op("rand_uniform"), input, seed_input);
     p.compile(migraphx::make_target("ref"));
 
     // Create a dummy input to hold the random data
@@ -6566,7 +6563,7 @@ TEST_CASE(rand_uniform_and_seed_test)
 
     migraphx::parameter_map params0;
     params0["Input_1"] = migraphx::argument(input_fixed_shape1);
-    auto result                     = p.eval(params0).back();
+    auto result        = p.eval(params0).back();
 
     std::vector<float> result_vec(sample_size);
     result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });