Merge branch 'dynamic_reduce' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into dyn_model_test

src/include/migraphx/op/broadcast.hpp

@@ -142,7 +142,6 @@ struct broadcast
     {
         return args[0].reshape(dyn_out.computed_shape);
     }
-
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };
 

src/include/migraphx/op/reduce_op.hpp

@@ -26,6 +26,7 @@
 
 #include <migraphx/op/name.hpp>
 #include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/tensor_view.hpp>
 #include <migraphx/shape_for_each.hpp>
@@ -105,18 +106,41 @@ struct reduce_op : op_name<Derived>
         return tuned_axes;
     }
 
+    /**
+     * @brief returns a shape in which the axis or axes named
+     * for reduction by this op are set, to size 1.
+     *
+     * @param inputs list of input shapes
+     * @return shape
+     */
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto s          = inputs.at(0);
-        auto lens       = s.lens();
-        auto tuned_axes = tune_axes(lens.size());
-        for(auto axis : tuned_axes)
+        check_shapes{inputs, *this, true}.has(1);
+        auto s = inputs.at(0);
+        if(s.dynamic())
         {
-            lens[axis] = 1;
+            auto output_dyn_dims = s.dyn_dims();
+            auto tuned_axes      = tune_axes(output_dyn_dims.size());
+            for(const auto& axis : tuned_axes)
+            {
+                // At the time of writing, there's no functional difference between
+                // optimum of 0 (no opt) or 1.
+                output_dyn_dims[axis] = {1, 1, 0};
+            }
+
+            return shape{s.type(), output_dyn_dims};
+        }
+        else
+        {
+            auto lens       = s.lens();
+            auto tuned_axes = tune_axes(lens.size());
+            for(const auto& axis : tuned_axes)
+            {
+                lens[axis] = 1;
+            }
+
+            return inputs[0].with_lens(lens);
         }
-
-        return inputs[0].with_lens(lens);
     }
 
     template <class T>
@@ -124,7 +148,7 @@ struct reduce_op : op_name<Derived>
                    const std::vector<T>& in_lens,
                    std::vector<T>& out_lens) const
     {
-        for(auto axis : tuned_axes)
+        for(const auto& axis : tuned_axes)
         {
             out_lens[axis] = in_lens[axis];
         }
@@ -151,17 +175,17 @@ struct reduce_op : op_name<Derived>
             static_cast<const Derived&>(*this).output(batch_shape)(val);
     }
 
-    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};
+        argument result{dyn_out.computed_shape};
         auto arg_lens   = args.front().get_shape().lens();
         auto tuned_axes = tune_axes(arg_lens.size());
-        std::vector<std::size_t> batch_lens(output_shape.lens().size(), 1);
+        std::vector<std::size_t> batch_lens(dyn_out.computed_shape.lens().size(), 1);
         tune_dims(tuned_axes, arg_lens, batch_lens);
-        shape batch_shape{output_shape.type(), batch_lens};
+        shape batch_shape{dyn_out.computed_shape.type(), batch_lens};
         visit_all(result, args[0])([&](auto output, auto input) {
-            par_for(output_shape.elements(), [&](auto i) {
-                auto out_idx = output_shape.multi(i);
+            par_for(dyn_out.computed_shape.elements(), [&](auto i) {
+                auto out_idx = dyn_out.computed_shape.multi(i);
                 this->reduce(input, batch_shape, tuned_axes, out_idx, output);
             });
         });

src/onnx/parse_reduce_op.cpp

@@ -68,8 +68,7 @@ instruction_ref parse_reduce_oper(const std::string& op_name,
         }
         else
         {
-            std::size_t n_dim = args.front()->get_shape().lens().size();
-            axes.resize(n_dim);
+            axes.resize(args.front()->get_shape().ndim());
             std::iota(axes.begin(), axes.end(), 0);
         }
     }

test/onnx/gen_onnx.py

@@ -23,7 +23,7 @@
 #####################################################################################
 # This script generates onnx files for MIGraphX onnx operator tests.
 # To generate an individual onnx file, you can use the following
-# command: python -c "import gen_onnx; gen_onnx.{test_name}_test()"
+# command: python3 -c "import gen_onnx; gen_onnx.{test_name}_test()"
 import numpy as np
 import onnx
 from onnx import helper
@@ -4675,6 +4675,34 @@ def reducel1_test():
     return ([node], [x], [y])
 
 
+@onnx_test
+def reducel1_dyn_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+    axes = [-2]
+
+    node = onnx.helper.make_node('ReduceL1',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducel1_dyn_noaxes_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
+
+    node = onnx.helper.make_node('ReduceL1',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def reducel2_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
@@ -4724,6 +4752,22 @@ def reduce_log_sum_exp_test():
 def reducemax_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
     y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
+
+    axes = [2]
+
+    node = onnx.helper.make_node('ReduceMax',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 axes=axes,
+                                 keepdims=0)
+
+    return ([node], [x], [y])
+
+
+@onnx_test
+def reducemax_dyn_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 4, 6])
     axes = [2]
 
     node = onnx.helper.make_node('ReduceMax',

test/onnx/onnx_test.cpp

@@ -4468,6 +4468,50 @@ TEST_CASE(reducel1_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(reducel1_dyn_test)
+{
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        // a shape with 4 dynamic dimensions
+        auto l0      = mm->add_parameter("x",
+                                    migraphx::shape{migraphx::shape::float_type,
+                                                    {{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
+        auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
+        auto sum_ins =
+            mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {-2}}}), abs_ins);
+        auto sq_ins = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {-2}}}), sum_ins);
+        mm->add_return({sq_ins});
+
+        migraphx::onnx_options options;
+        options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
+        auto prog                       = migraphx::parse_onnx("reducel1_dyn_test.onnx", options);
+
+        EXPECT(p == prog);
+    }
+
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        // No axes given in the onnx file.  Parser should default to all axes.
+        auto l0      = mm->add_parameter("x",
+                                    migraphx::shape{migraphx::shape::float_type,
+                                                    {{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
+        auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
+        auto sum_ins =
+            mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {0, 1, 2, 3}}}), abs_ins);
+        auto sq_ins =
+            mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0, 1, 2, 3}}}), sum_ins);
+        mm->add_return({sq_ins});
+
+        migraphx::onnx_options options;
+        options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
+        auto prog = migraphx::parse_onnx("reducel1_dyn_noaxes_test.onnx", options);
+
+        EXPECT(p == prog);
+    }
+}
+
 TEST_CASE(reducel2_test)
 {
     migraphx::program p;
@@ -4518,6 +4562,24 @@ TEST_CASE(reducemax_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(reducemax_dyn_test)
+{
+    // input shape with 4 dynamic dimensions
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    auto l0  = mm->add_parameter(
+        "x", migraphx::shape{migraphx::shape::float_type, {{3, 3}, {4, 4}, {5, 5}, {6, 6}}});
+    auto r0 = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {2}}}), l0);
+    auto r1 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {2}}}), r0);
+    mm->add_return({r1});
+
+    migraphx::onnx_options options;
+    options.map_dyn_input_dims["x"] = {{3, 3}, {4, 4}, {5, 5}, {6, 6}};
+    auto prog                       = migraphx::parse_onnx("reducemax_dyn_test.onnx", options);
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(reducemean_test)
 {
     migraphx::program p;

test/op_shape_test.cpp

@@ -1946,9 +1946,51 @@ void test_reduce_ops()
     }
 }
 
+// dynamic shape
+template <class T>
+void test_dyn_reduce_ops()
+{
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{2, 3, 3}, {1, 1, 0}})},
+                     T{{-1}},
+                     input);
+    }
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{1, 1, 0}, {2, 4, 4}})},
+                     T{{0}},
+                     input);
+    }
+    {
+        // Empty axis argument reduces all axes
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        expect_shape(migraphx::shape{migraphx::shape::float_type,
+                                     std::vector<migraphx::shape::dynamic_dimension>(
+                                         {{1, 1, 0}, {1, 1, 0}})},
+                     T{{}},
+                     input);
+    }
+    {
+        migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
+        throws_shape(T{{4}}, input);
+    }
+}
+
+TEST_CASE(reduce_max) { test_reduce_ops<migraphx::op::reduce_max>(); }
 TEST_CASE(reduce_mean) { test_reduce_ops<migraphx::op::reduce_mean>(); }
+TEST_CASE(reduce_prod) { test_reduce_ops<migraphx::op::reduce_prod>(); }
 TEST_CASE(reduce_sum) { test_reduce_ops<migraphx::op::reduce_sum>(); }
 
+TEST_CASE(reduce_max_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_max>(); }
+TEST_CASE(reduce_mean_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_mean>(); }
+TEST_CASE(reduce_prod_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_prod>(); }
+TEST_CASE(reduce_sum_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_sum>(); }
+
 TEST_CASE(reshape_shape)
 {
     migraphx::shape input{migraphx::shape::float_type, {24, 1, 1, 1}};

test/ref_ops_test.cpp

@@ -5781,6 +5781,27 @@ TEST_CASE(reduce_max_axis0)
     EXPECT(results_vector == gold);
 }
 
+TEST_CASE(reduce_max_dynamic_axis0)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+    migraphx::shape s{migraphx::shape::float_type, {{2, 4, 2}, {3, 5, 3}}};
+    auto input         = mm->add_parameter("X", s);
+    auto reduce_max_op = migraphx::make_op("reduce_max", {{"axes", {0}}});
+    mm->add_instruction(reduce_max_op, input);
+    p.compile(migraphx::ref::target{});
+
+    migraphx::parameter_map params;
+    migraphx::shape input_fixed_shape{migraphx::shape::float_type, {2, 5}};
+    std::vector<float> input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
+    params["X"] = migraphx::argument(input_fixed_shape, input_data.data());
+    auto result = p.eval(params).back();
+    std::vector<float> results_vector;
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    std::vector<float> gold = {6, 7, 8, 9, 10};
+    EXPECT(migraphx::verify_range(results_vector, gold));
+}
+
 TEST_CASE(reduce_max_axis01)
 {
     migraphx::program p;