Fix time seed bug in random sequence ops (#1027)

Fix bug caused by casting time seed to float

src/onnx/parse_multinomial.cpp

@@ -27,11 +27,6 @@ struct parse_multinomial : op_parser<parse_multinomial>
         if(contains(info.attributes, "sample_size"))
             sample_size = info.attributes.at("sample_size").i();
 
-        float seed = static_cast<float>(
-            std::chrono::high_resolution_clock::now().time_since_epoch().count());
-        if(contains(info.attributes, "seed"))
-            seed = info.attributes.at("seed").f();
-
         // Subtract the per-batch maximum log-probability, making the per-batch max 0
         auto maxes =
             info.add_instruction(migraphx::make_op("reduce_max", {{"axes", {1}}}), args[0]);
@@ -46,7 +41,10 @@ struct parse_multinomial : op_parser<parse_multinomial>
             migraphx::make_op("prefix_scan_sum", {{"axis", 1}, {"exclusive", false}}), cdf);
 
         // Pre-compute random distribution
-        std::mt19937 gen(seed);
+        std::mt19937 gen(std::chrono::high_resolution_clock::now().time_since_epoch().count());
+        if(contains(info.attributes, "seed"))
+            gen.seed(info.attributes.at("seed").f());
+
         std::uniform_real_distribution<> dis(0.0, 1.0);
         size_t batch_size = args[0]->get_shape().lens().front();
         migraphx::shape dist_shape{migraphx::shape::float_type, {batch_size, sample_size}};

src/onnx/parse_randomnormal_ops.cpp

@@ -42,11 +42,6 @@ struct parse_randomnormal_ops : op_parser<parse_randomnormal_ops>
         if(contains(info.attributes, "scale"))
             scale = info.attributes.at("scale").f();
 
-        float seed = static_cast<float>(
-            std::chrono::high_resolution_clock::now().time_since_epoch().count());
-        if(contains(info.attributes, "seed"))
-            seed = info.attributes.at("seed").f();
-
         shape out_shape;
         if(contains(info.attributes, "shape"))
         {
@@ -75,7 +70,10 @@ struct parse_randomnormal_ops : op_parser<parse_randomnormal_ops>
                            ": cannot deduce shape without shape attribute or argument.");
         }
 
-        std::mt19937 gen(seed);
+        std::mt19937 gen(std::chrono::high_resolution_clock::now().time_since_epoch().count());
+        if(contains(info.attributes, "seed"))
+            gen.seed(info.attributes.at("seed").f());
+
         std::normal_distribution<> d(mean, scale);
         std::vector<double> rand_vals(out_shape.elements());
         std::generate(rand_vals.begin(), rand_vals.end(), [&]() { return d(gen); });

src/onnx/parse_randomuniform_ops.cpp

@@ -42,11 +42,6 @@ struct parse_randomuniform_ops : op_parser<parse_randomuniform_ops>
         if(contains(info.attributes, "low"))
             low = info.attributes.at("low").f();
 
-        float seed = static_cast<float>(
-            std::chrono::high_resolution_clock::now().time_since_epoch().count());
-        if(contains(info.attributes, "seed"))
-            seed = info.attributes.at("seed").f();
-
         shape out_shape;
         if(contains(info.attributes, "shape"))
         {
@@ -75,7 +70,10 @@ struct parse_randomuniform_ops : op_parser<parse_randomuniform_ops>
                            ": cannot deduce shape without shape attribute or argument.");
         }
 
-        std::mt19937 gen(seed);
+        std::mt19937 gen(std::chrono::high_resolution_clock::now().time_since_epoch().count());
+        if(contains(info.attributes, "seed"))
+            gen.seed(info.attributes.at("seed").f());
+
         std::uniform_real_distribution<> d(high, low);
         std::vector<double> rand_vals(out_shape.elements());
         std::generate(rand_vals.begin(), rand_vals.end(), [&]() { return d(gen); });

test/onnx/gen_onnx.py

@@ -2725,6 +2725,21 @@ def multinomial_test():
     return ([node], [input], [output])
 
 
+@onnx_test
+def multinomial_generated_seed_test():
+    sample_size = 10
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
+    output = helper.make_tensor_value_info("output", TensorProto.INT32,
+                                           [1, 10])
+
+    node = onnx.helper.make_node('Multinomial',
+                                 inputs=['input'],
+                                 sample_size=sample_size,
+                                 outputs=['output'])
+
+    return ([node], [input], [output])
+
+
 @onnx_test
 def multinomial_dtype_error_test():
     sample_size = 10
@@ -3176,6 +3191,21 @@ def randomnormal_dtype_error_test():
     return ([node], [], [output])
 
 
+@onnx_test
+def randomnormal_generated_seed_test():
+    sample_size = 10
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
+    output = helper.make_tensor_value_info("output", TensorProto.INT32,
+                                           [1, 10])
+
+    node = onnx.helper.make_node('RandomNormal',
+                                 inputs=['input'],
+                                 sample_size=sample_size,
+                                 outputs=['output'])
+
+    return ([node], [input], [output])
+
+
 @onnx_test
 def randomnormal_shape_error_test():
     dtype = 1
@@ -3266,6 +3296,21 @@ def randomuniform_dtype_error_test():
     return ([node], [], [output])
 
 
+@onnx_test
+def randomuniform_generated_seed_test():
+    sample_size = 10
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [1, 10])
+    output = helper.make_tensor_value_info("output", TensorProto.INT32,
+                                           [1, 10])
+
+    node = onnx.helper.make_node('RandomUniform',
+                                 inputs=['input'],
+                                 sample_size=sample_size,
+                                 outputs=['output'])
+
+    return ([node], [input], [output])
+
+
 @onnx_test
 def randomuniform_shape_error_test():
     dtype = 1

test/onnx/multinomial_generated_seed_test.onnx

+multinomial_generated_seed_test:

+0
+inputoutput"Multinomial*
+sample_size
+
multinomial_generated_seed_testZ
+input
+

+

+
+b
+output
+
+

+
+B
\ No newline at end of file

test/onnx/onnx_test.cpp

@@ -2388,6 +2388,14 @@ TEST_CASE(multinomial_dtype_error_test)
     EXPECT(test::throws([&] { migraphx::parse_onnx("multinomial_dtype_error_test.onnx"); }));
 }
 
+TEST_CASE(multinomial_generated_seed_test)
+{
+    auto p1 = optimize_onnx("multinomial_generated_seed_test.onnx");
+    auto p2 = optimize_onnx("multinomial_generated_seed_test.onnx");
+
+    EXPECT(p1 != p2);
+}
+
 TEST_CASE(multinomial_int64_test)
 {
     migraphx::program p;
@@ -2891,6 +2899,14 @@ TEST_CASE(randomnormal_dtype_error_test)
     EXPECT(test::throws([&] { migraphx::parse_onnx("randomnormal_dtype_error_test.onnx"); }));
 }
 
+TEST_CASE(randomnormal_generated_seed_test)
+{
+    auto p1 = optimize_onnx("randomnormal_generated_seed_test.onnx");
+    auto p2 = optimize_onnx("randomnormal_generated_seed_test.onnx");
+
+    EXPECT(p1 != p2);
+}
+
 TEST_CASE(randomnormal_shape_error_test)
 {
     EXPECT(test::throws([&] { migraphx::parse_onnx("randomnormal_shape_error_test.onnx"); }));
@@ -2953,6 +2969,14 @@ TEST_CASE(randomuniform_dtype_error_test)
     EXPECT(test::throws([&] { migraphx::parse_onnx("randomuniform_dtype_error_test.onnx"); }));
 }
 
+TEST_CASE(randomuniform_generated_seed_test)
+{
+    auto p1 = optimize_onnx("randomuniform_generated_seed_test.onnx");
+    auto p2 = optimize_onnx("randomuniform_generated_seed_test.onnx");
+
+    EXPECT(p1 != p2);
+}
+
 TEST_CASE(randomuniform_shape_error_test)
 {
     EXPECT(test::throws([&] { migraphx::parse_onnx("randomuniform_shape_error_test.onnx"); }));

test/onnx/randomnormal_generated_seed_test.onnx

+ randomnormal_generated_seed_test:

+1
+inputoutput"RandomNormal*
+sample_size
+
 randomnormal_generated_seed_testZ
+input
+

+

+
+b
+output
+
+

+
+B
\ No newline at end of file

test/onnx/randomuniform_generated_seed_test.onnx

+!randomuniform_generated_seed_test:

+2
+inputoutput"
RandomUniform*
+sample_size
+
!randomuniform_generated_seed_testZ
+input
+

+

+
+b
+output
+
+

+
+B
\ No newline at end of file