work in progress for multinomial. Doesn't work

src/common.cpp

@@ -208,6 +208,8 @@ instruction_ref insert_common_op(module& m,
                                  const operation& op,
                                  std::vector<instruction_ref> inputs)
 {
+    if(op.name() == "clip")
+       return inputs[0];
     return m.insert_instruction(ins, op, insert_common_args(m, ins, std::move(inputs)));
 }
 

src/include/migraphx/op/clip.hpp

@@ -48,7 +48,7 @@ struct clip
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(3).same_type().same_dims();
+        check_shapes{inputs, *this, true}.has(3).same_type().same_dims();
         return inputs.front();
     }
 

src/include/migraphx/op/multinomial.hpp

@@ -47,22 +47,22 @@ struct multinomial
     std::string name() const { return "multinomial"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2).only_dims(2);
-        size_t sample_size = inputs.back().lens().back();
+        check_shapes{inputs, *this, true}.has(2).only_dims(2);
+        size_t sample_size = inputs.back().max_lens().back();
 
         if(not contains({shape::int32_type, shape::int64_type}, dtype))
             MIGRAPHX_THROW(
                 "Multinomial: Invalid output type. Valid types are int32_type and int64_type.");
 
-        return {dtype, {inputs.front().lens().front(), sample_size}};
+        return {dtype, {inputs.front().max_lens().front(), sample_size}};
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
-        size_t batch_size  = output_shape.lens().front();
-        size_t class_size  = args[0].get_shape().lens().back();
-        size_t sample_size = output_shape.lens().back();
+        argument result{dyn_out.computed_shape};
+        size_t batch_size  = dyn_out.computed_shape.lens().front();
+        size_t class_size  = args[0].get_shape().max_lens().back();
+        size_t sample_size = dyn_out.computed_shape.lens().back();
 
         visit_all(args[0], args[1])([&](auto cdf, auto dist) {
             result.visit([&](auto output) {

src/include/migraphx/op/prefix_scan_op.hpp

@@ -60,8 +60,12 @@ struct prefix_scan_op : op_name<Derived>
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
+        check_shapes{inputs, *this, true}.has(1);
         auto s = inputs.front();
+        if(s.dynamic())
+        {
+            return {s.type(), s.max_lens()};
+        }
         if(s.broadcasted())
         {
             return {s.type(), s.lens()};

src/onnx/parse_clip.cpp

 /*
  * 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

src/onnx/parse_multinomial.cpp

 /*
  * 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
@@ -53,10 +53,7 @@ struct parse_multinomial : op_parser<parse_multinomial>
         // 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]);
-        auto mb_maxes = info.add_instruction(
-            migraphx::make_op("multibroadcast", {{"out_lens", args[0]->get_shape().lens()}}),
-            maxes);
-        auto cdf = info.add_instruction(migraphx::make_op("sub"), args[0], mb_maxes);
+        auto cdf = info.add_common_op("sub", args[0], maxes);        
         // Take the element-wise exponent to get probabilities in the range (0, 1]
         cdf = info.add_instruction(migraphx::make_op("exp"), cdf);
         // Compute the cumulative density function
@@ -69,7 +66,7 @@ struct parse_multinomial : op_parser<parse_multinomial>
             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();
+        size_t batch_size = args[0]->get_shape().max_lens().front();
         migraphx::shape dist_shape{migraphx::shape::float_type, {batch_size, sample_size}};
 
         std::vector<float> random_dist(batch_size * sample_size);

test/onnx/gen_onnx.py

@@ -4327,6 +4327,23 @@ def multinomial_test():
     return ([node], [input], [output])
 
 
+@onnx_test()
+def multinomial_dyn_test():
+    sample_size = 10
+    seed = 0.0
+    input = helper.make_tensor_value_info("input", TensorProto.FLOAT, [None, 10])
+    output = helper.make_tensor_value_info("output", TensorProto.INT32,
+                                           [None, 10])
+
+    node = onnx.helper.make_node('Multinomial',
+                                 inputs=['input'],
+                                 sample_size=sample_size,
+                                 seed=seed,
+                                 outputs=['output'])
+
+    return ([node], [input], [output])
+
+
 @onnx_test()
 def multinomial_generated_seed_test():
     sample_size = 10

test/onnx/onnx_test.cpp

@@ -4104,6 +4104,40 @@ TEST_CASE(multinomial_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(multinomial_dyn_test)
+{
+    migraphx::program p;
+    auto* mm           = p.get_main_module();
+    size_t sample_size = 10;
+    float seed         = 0.0f;
+
+    auto input = mm->add_parameter("input", migraphx::shape{migraphx::shape::float_type, {{1, 10}, {10, 10}}});
+    auto maxes = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {1}}}), input);
+    // auto mb_maxes =
+    //     mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 10}}}), maxes);
+    // auto cdf = mm->add_instruction(migraphx::make_op("sub"), input, mb_maxes);
+    auto cdf = add_common_op(*mm, migraphx::make_op("sub"), {input, maxes}); 
+    cdf      = mm->add_instruction(migraphx::make_op("exp"), cdf);
+    cdf      = mm->add_instruction(
+        migraphx::make_op("prefix_scan_sum", {{"axis", 1}, {"exclusive", false}}), cdf);
+
+    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); });
+    migraphx::shape rs{migraphx::shape::float_type, {1, sample_size}};
+    auto rs_lit = mm->add_literal(migraphx::literal{rs, rand_samples});
+
+    auto ret = mm->add_instruction(migraphx::make_op("multinomial"), cdf, rs_lit);
+    mm->add_return({ret});
+
+    // auto prog = optimize_onnx("multinomial_dyn_test.onnx");
+    migraphx::onnx_options options;
+    options.default_dyn_dim_value = {1, 10};
+    options.print_program_on_error = true;
+    auto prog                     = migraphx::parse_onnx("multinomial_dyn_test.onnx", options);
+    EXPECT(p == prog);
+}
 TEST_CASE(multinomial_dtype_error_test)
 {
     EXPECT(test::throws([&] { migraphx::parse_onnx("multinomial_dtype_error_test.onnx"); }));

test/op_shape_test.cpp

@@ -1748,6 +1748,13 @@ TEST_CASE(multinomial)
     throws_shape(migraphx::make_op("multinomial", {{"dtype", dtype}}), s, s);
 }
 
+TEST_CASE(multinomial_dyn)
+{
+    migraphx::shape s{migraphx::shape::int32_type, {{2, 3}, {5, 6}}};
+
+    expect_shape(s, migraphx::make_op("multinomial", {{"dtype", migraphx::shape::int32_type}}), s, s);
+}
+
 TEST_CASE(nms_shape)
 {
     // use_dyn_output == false

test/ref_ops_test.cpp

@@ -4915,6 +4915,56 @@ TEST_CASE(multinomial_test)
     EXPECT(migraphx::verify_range(norm, res_norm, 100000));
 }
 
+TEST_CASE(multinomial_dyn_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    size_t sample_size = 100000;
+    float seed         = 0.0f;
+    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); });
+    migraphx::shape rs{migraphx::shape::float_type, {1, sample_size}};
+    auto rs_lit = mm->add_literal(migraphx::literal{rs, rand_samples});
+
+    migraphx::shape s{migraphx::shape::float_type, {{1, 2}, {5, 6}}};
+    std::vector<int> dist{15, 25, 15, 25, 20};
+    std::vector<float> data(5);
+    std::transform(dist.begin(), dist.end(), data.begin(), [&](auto d) { return std::log(d); });
+    auto input = mm->add_literal(migraphx::literal(s, data));
+
+    auto maxes = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {1}}}), input);
+    auto mb_maxes =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {1, 5}}}), maxes);
+    auto cdf = mm->add_instruction(migraphx::make_op("sub"), input, mb_maxes);
+    cdf      = mm->add_instruction(migraphx::make_op("exp"), cdf);
+    cdf      = mm->add_instruction(
+        migraphx::make_op("prefix_scan_sum", {{"axis", 1}, {"exclusive", false}}), cdf);
+
+    mm->add_instruction(migraphx::make_op("multinomial"), cdf, rs_lit);
+    p.compile(migraphx::make_target("ref"));
+    auto result = p.eval({}).back();
+    std::vector<int32_t> result_vec(sample_size);
+    result.visit([&](auto output) { result_vec.assign(output.begin(), output.end()); });
+
+    std::vector<int> res_dist(5, 0);
+    for(const auto& r : result_vec)
+        res_dist[r]++;
+    auto dist_sum     = std::accumulate(dist.begin(), dist.end(), 0);
+    auto res_dist_sum = std::accumulate(res_dist.begin(), res_dist.end(), 0);
+    std::vector<float> norm(5);
+    std::vector<float> res_norm(5);
+    std::transform(dist.begin(), dist.end(), norm.begin(), [&](auto n) {
+        return static_cast<double>(n) / dist_sum;
+    });
+    std::transform(res_dist.begin(), res_dist.end(), res_norm.begin(), [&](auto n) {
+        return static_cast<double>(n) / res_dist_sum;
+    });
+    EXPECT(migraphx::verify_range(norm, res_norm, 100000));
+}
+
 TEST_CASE(neg_test)
 {
     migraphx::program p;