added rand_uniform operation

src/CMakeLists.txt

 #####################################################################################
 # The MIT License (MIT)
 #
-# Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+# Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -183,6 +183,7 @@ register_migraphx_ops(
     quant_convolution
     quant_dot
     quantizelinear
+    rand_uniform
     recip
     reduce_max
     reduce_mean

src/include/migraphx/op/rand_uniform.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+/**
+ * Random Uniform distribution operator.  Given a shape, populate it with random 
+ * values.  Calls to rand_uniform using the same randomization seed will 
+ * always generate the same pseudo-random sequence.  Seed can 
+ * be given as a runtime argument containing a single value, or a compile-time
+ * attribute.
+ *
+ *      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
+ *
+ *      Output:   Same shape.
+ *
+*/
+#ifndef MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
+#define MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/par_for.hpp>
+#include <migraphx/reflect.hpp>
+#include <random>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+struct rand_uniform
+{
+    uint32_t seed      = {0};
+    bool use_auto_seed = false;
+
+    // todo:  not currently settable
+    float range_min    = 0.0f;
+    float range_max    = 1.0f;
+
+    // todo:  integer data type(s) not yet supported
+    shape::type_t dtype             = shape::type_t::float_type;
+
+    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"));
+    }
+
+    std::string name() const { return "rand_uniform"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this, true}.has(1, 2);
+
+        if(inputs.size() > 1 and inputs.at(1).type() != shape::type_t::uint32_type)
+            MIGRAPHX_THROW("RAND_UNIFORM:  Input 2 (seed) must have type unsigned int");
+        auto s = inputs.front();
+        if(s.dynamic())
+        {
+            return s.with_type(dtype);
+        }
+        else
+        {
+            return s.with_lens(s.lens()).with_type(dtype);
+        }
+    }
+
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
+    {
+        argument result{dyn_out.computed_shape};
+
+        auto local_seed(seed);
+        if(use_auto_seed)
+            local_seed = std::chrono::system_clock::now().time_since_epoch().count();
+        else
+        {
+            if(args.size() > 1)
+            {
+                if(args.at(1).get_shape().element_space() > 0)
+                {
+                    visit_all(args[1])([&](auto data) { local_seed = data[0]; });
+                }
+            }
+        }
+        // If a seed argument was not defined, use the value from the seed attribute,
+        // or the default.
+
+        std::mt19937 gen(local_seed);
+        std::uniform_real_distribution<> dis(range_min, range_max);
+        result.visit([&](auto output) {
+            std::generate(output.begin(), output.end(), [&]() { return dis(gen); });
+        });
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

test/op_shape_test.cpp

@@ -2216,6 +2216,28 @@ TEST_CASE(prefix_scan_sum_dyn_2d)
     }
 }
 
+TEST_CASE(rand_uniform)
+{
+    std::vector<migraphx::shape::dynamic_dimension> dd{{5, 8}, {3, 7}};
+    migraphx::shape s1{migraphx::shape::float_type, dd};
+    expect_shape(
+        s1,
+        migraphx::make_op("rand_uniform", {{"seed", 1}}),
+        s1);
+}
+
+TEST_CASE(rand_uniform_2args)
+{
+    std::vector<migraphx::shape::dynamic_dimension> dd{{5, 8}, {3, 7}};
+    migraphx::shape s1{migraphx::shape::float_type, dd};
+    migraphx::shape s2{migraphx::shape::uint32_type, dd};
+
+    expect_shape(
+        s1,
+        migraphx::make_op("rand_uniform", {{"seed", 1}}),
+        s1, s2);
+}
+
 TEST_CASE(quant_convolution_shape)
 {
     migraphx::shape output{migraphx::shape::int32_type, {4, 4, 1, 1}};

test/ref_ops_test.cpp

@@ -6458,6 +6458,90 @@ TEST_CASE(quantizelinear)
     }
 }
 
+TEST_CASE(rand_uniform_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    uint32_t seed(0);
+    size_t sample_size(200);
+
+    //      Shape of the random data
+    migraphx::shape rs{migraphx::shape::float_type, {1, sample_size}};
+
+    // data tensor must be allocated at this point but does not need to be initialized.
+    std::vector<float> data(sample_size);
+    auto input = mm->add_literal(migraphx::literal(rs, data));
+
+    // Runtime randomization seed
+    migraphx::shape seed_shape{migraphx::shape::uint32_type, {1}};
+    std::vector<uint32_t> seed_data{seed};
+    auto seed_input = mm->add_literal(migraphx::literal(seed_shape, seed_data));
+
+    mm->add_instruction(migraphx::make_op("rand_uniform",
+                                          {
+                                              {"seed", seed},
+                                          }),
+                        input,
+                        seed_input);
+    p.compile(migraphx::make_target("ref"));
+
+    migraphx::parameter_map params0;
+    auto result = p.eval(params0).back();
+    std::vector<float> result_vec(sample_size);
+    result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
+
+    // Compare result with the STL's mt19937 generator
+    std::mt19937 gen(seed);
+    std::uniform_real_distribution<> dis(0.0, 1.0);
+    std::vector<float> rand_samples(sample_size);
+    std::generate(rand_samples.begin(), rand_samples.end(), [&]() { return dis(gen); });
+    EXPECT(migraphx::verify::verify_range(result_vec, rand_samples, 100000));
+}
+
+TEST_CASE(rand_uniform_dyn_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    uint32_t seed(17);
+    size_t sample_size(200);
+
+    //      Shape of the random data
+    migraphx::shape rs{migraphx::shape::float_type, {{1, 2}, {2, sample_size + 1}}};
+    auto input = mm->add_parameter("Input_1", rs);
+
+    // Runtime randomization seed
+    migraphx::shape seed_shape{migraphx::shape::uint32_type, {1}};
+    auto seed_input = mm->add_parameter("Seed", seed_shape);
+
+    mm->add_instruction(migraphx::make_op("rand_uniform",
+                                          {
+                                              {"seed", seed + 1},
+                                          }),
+                        input,
+                        seed_input);
+    p.compile(migraphx::make_target("ref"));
+
+    // Create a dummy input to hold the random data
+    migraphx::shape input_fixed_shape1{migraphx::shape::float_type, {sample_size}};
+
+    migraphx::parameter_map params0;
+    params0["Input_1"] = migraphx::argument(input_fixed_shape1);
+    migraphx::shape seed_fixed_shape{migraphx::shape::uint32_type, {1}};
+    std::vector<uint32_t> seed_data = {seed};
+    params0["Seed"]                 = migraphx::argument(seed_fixed_shape, seed_data.data());
+    auto result                     = p.eval(params0).back();
+
+    std::vector<float> result_vec(sample_size);
+    result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
+
+    // Compare result with the STL's mt19937 generator
+    std::mt19937 gen(seed);
+    std::uniform_real_distribution<> dis(0.0, 1.0);
+    std::vector<float> rand_samples(sample_size);
+    std::generate(rand_samples.begin(), rand_samples.end(), [&]() { return dis(gen); });
+    EXPECT(migraphx::verify::verify_range(result_vec, rand_samples, 100000));
+}
+
 TEST_CASE(recip_test)
 {
     migraphx::program p;