Merge branch 'develop' into dyn_pad

.github/workflows/add-to-project.yaml

+name: Add PRs and issues to GitHub projects
+
+on:
+  pull_request:
+    types:
+      - opened
+  issues:
+    types:
+      - opened
+
+jobs:
+  add-to-project:
+    name: Add PRs and issues to MIGX project
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/add-to-project@v0.4.0
+        with:
+          project-url: https://github.com/orgs/ROCmSoftwarePlatform/projects/20
+          github-token: $${{ secrets.MIGRAPHX_BOT_TOKEN }}

Dockerfile

@@ -87,7 +87,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
 
 ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
 
-RUN cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@0f38fb33f518b53b94b541feb9b079668c5518e8 -DBUILD_MIXR_TARGET=On -DLLVM_ENABLE_ZSTD=Off -DLLVM_ENABLE_THREADS=Off
+RUN cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@78b706fe9879587ab98b6614ae539265374a3fae -DBUILD_MIXR_TARGET=On -DLLVM_ENABLE_ZSTD=Off -DLLVM_ENABLE_THREADS=Off
 
 ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
 ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db

Jenkinsfile

@@ -11,16 +11,22 @@ def rocmtestnode(Map conf) {
     def image = 'migraphxlib'
     env.CCACHE_COMPRESSLEVEL = 7
     env.CCACHE_DIR = ccache
-    def cmake_build = { compiler, flags ->
+    def cmake_build = { bconf ->
+        def compiler = bconf.get("compiler", "/opt/rocm/llvm/bin/clang++")
+        def flags = bconf.get("flags", "")
+        def gpu_debug = bconf.get("gpu_debug", "0")
         def cmd = """
-            env
             ulimit -c unlimited
             echo "leak:dnnl::impl::malloc" > suppressions.txt
             export LSAN_OPTIONS="suppressions=\$(pwd)/suppressions.txt"
+            export MIGRAPHX_GPU_DEBUG=${gpu_debug}
+            export CXX=${compiler}
+            export CXXFLAGS='-Werror'
+            env
             rm -rf build
             mkdir build
             cd build
-            CXX=${compiler} CXXFLAGS='-Werror' cmake -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache ${flags} ..
+            cmake -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache ${flags} ..
             make -j\$(nproc) generate all doc package check VERBOSE=1
         """
         echo cmd
@@ -93,28 +99,32 @@ rocmtest clang_debug: rocmnode('vega') { cmake_build ->
     stage('Hip Clang Debug') {
         def sanitizers = "undefined"
         def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
-        cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
+        cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
+    }
+}, clang_gpu_debug: rocmnode('vega') { cmake_build ->
+    stage('Hip Clang GPU Debug') {
+        cmake_build(flags: "-DCMAKE_BUILD_TYPE=release", gpu_debug: true)
     }
 }, clang_release: rocmnode('vega') { cmake_build ->
     stage('Hip Clang Release') {
-        cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=release")
+        cmake_build(flags: "-DCMAKE_BUILD_TYPE=release")
         stash includes: 'build/*.deb', name: 'migraphx-package'
     }
 }, mlir_debug: rocmnode('vega') { cmake_build ->
     stage('MLIR Debug') {
         def sanitizers = "undefined"
         def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
-        cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
+        cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
     }
 }, clang_asan: rocmnode('nogpu') { cmake_build ->
     stage('Clang ASAN') {
         def sanitizers = "undefined,address"
         def debug_flags = "-g -O2 -fno-omit-frame-pointer -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
-        cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=Off -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
+        cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=Off -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
     }
 }//, clang_release_navi: rocmnode('navi21') { cmake_build ->
 //    stage('HIP Clang Release Navi') {
-//        cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=release")
+//        cmake_build(flags: "-DCMAKE_BUILD_TYPE=release")
 //    }
 //}
 

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>
@@ -105,26 +106,49 @@ 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);
+        check_shapes{inputs, *this, true}.has(1);
         auto s = inputs.at(0);
+        if(s.dynamic())
+        {
+            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(auto axis : tuned_axes)
+            for(const auto& axis : tuned_axes)
             {
                 lens[axis] = 1;
             }
 
             return inputs[0].with_lens(lens);
         }
+    }
 
     template <class T>
     void tune_dims(const std::vector<int64_t>& tuned_axes,
                    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);
         }
     }

src/targets/gpu/jit/gather.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+#include <migraphx/gpu/compiler.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/gpu/context.hpp>
+
+#include <migraphx/gpu/compile_hip_code_object.hpp>
+#include <migraphx/gpu/compile_hip.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+// NOLINTNEXTLINE
+static const char* const gather_kernel = R"__migraphx__(
+#include <migraphx/kernels/gather.hpp>
+#include <migraphx/kernels/ops.hpp>
+#include <migraphx/kernels/integral_constant.hpp>
+#include <migraphx/kernels/generic_constant.hpp>
+#include <args.hpp>
+
+namespace migraphx {
+
+extern "C" {
+
+__global__ void gather_kernel(void* in_data, void* in_indices, void* output) 
+{
+    make_tensors()(in_data, in_indices, output)([](auto&&... xs) { 
+        gather<${axis}>(xs...); 
+    });
+}
+
+}
+
+} // namespace migraphx
+
+)__migraphx__";
+
+struct gather_compiler : compiler<gather_compiler>
+{
+    std::vector<std::string> names() const { return {"gather"}; }
+
+    operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
+    {
+        hip_compile_options options;
+        const auto& out_s = inputs.back();
+        options.set_launch_params(v, compute_global_for(ctx, out_s.elements()));
+        options.inputs         = inputs;
+        options.output         = out_s;
+        options.kernel_name    = "gather_kernel";
+        options.virtual_inputs = inputs;
+
+        auto axis = v.at("axis").to<std::string>();
+
+        auto src = interpolate_string(gather_kernel, {{"axis", axis}});
+
+        return compile_hip_code_object(src, options);
+    }
+
+    compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
+    {
+        return replace(compile_op(ctx, to_shapes(ins->inputs()), op.to_value()));
+    }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/kernels/include/migraphx/kernels/gather.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+#ifndef MIGRAPHX_GUARD_KERNELS_GATHER_HPP
+#define MIGRAPHX_GUARD_KERNELS_GATHER_HPP
+
+#include <migraphx/kernels/index.hpp>
+#include <migraphx/kernels/shape.hpp>
+#include <migraphx/kernels/algorithm.hpp>
+#include <migraphx/kernels/tensor_view.hpp>
+
+namespace migraphx {
+
+template <int Axis, class Input, class Indices>
+constexpr auto gather_shape(Input input, Indices indices)
+{
+    auto lengths = input.lens;
+
+    lengths[Axis] = indices.elements();
+    return make_shape(lengths, input.strides);
+}
+
+template <int Axis, class Input, class Indices, class Output>
+__device__ void gather(Input input, Indices indices, Output output)
+{
+    auto ind           = make_index();
+    auto axis_dim_size = input.get_shape().lens[Axis];
+
+    constexpr auto out_comp = gather_shape<Axis>(get_shape_c<Input>{}, get_shape_c<Indices>{});
+
+    ind.global_stride(output.get_shape().elements(), [&](auto i) {
+        auto idx      = out_comp.multi(i);
+        auto in_index = indices[idx[Axis]];
+
+        auto new_in_index = (in_index < 0) ? in_index + axis_dim_size : in_index;
+
+        idx[Axis] = new_in_index;
+
+        output[i] = input[idx];
+    });
+}
+
+} // namespace migraphx
+#endif

src/targets/gpu/kernels/include/migraphx/kernels/shape.hpp

@@ -128,6 +128,7 @@ struct shape
         result[0] = tidx;
         return result;
     }
+
     /// Convert multi-index into a single index
     constexpr index_int single(index_array idx) const
     {

src/targets/gpu/lowering.cpp

@@ -90,7 +90,6 @@ struct miopen_apply
 
         add_extend_op("argmax");
         add_extend_op("argmin");
-        add_extend_op("gather");
         add_extend_op("logsoftmax");
         add_extend_op("lrn");
         add_extend_op("multinomial");

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
@@ -4722,6 +4722,34 @@ 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])
@@ -4771,6 +4799,22 @@ 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

@@ -4506,6 +4506,50 @@ 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;
@@ -4556,6 +4600,24 @@ 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

@@ -1978,9 +1978,51 @@ 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

@@ -5773,6 +5773,27 @@ 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;

test/verify/test_gather.cpp

@@ -27,7 +27,8 @@
 #include <migraphx/generate.hpp>
 #include <migraphx/make_op.hpp>
 
-struct test_gather : verify_program<test_gather>
+template <int Axis>
+struct test_gather : verify_program<test_gather<Axis>>
 {
     migraphx::program create_program() const
     {
@@ -38,8 +39,13 @@ struct test_gather : verify_program<test_gather>
         std::vector<int> indices{1, 2, 2, 1};
         auto a0  = mm->add_parameter("data", s);
         auto a1  = mm->add_literal(migraphx::literal{s_indices, indices});
-        int axis = 0;
+        int axis = Axis;
         mm->add_instruction(migraphx::make_op("gather", {{"axis", axis}}), a0, a1);
         return p;
     }
 };
+
+// Standard gather test
+template struct test_gather<0>;
+// Test Negative axis
+template struct test_gather<-2>;

test/verify/test_gather_1d_index.cpp

@@ -27,7 +27,8 @@
 #include <migraphx/generate.hpp>
 #include <migraphx/make_op.hpp>
 
-struct test_gather_1d_index : verify_program<test_gather_1d_index>
+template <int Index>
+struct test_gather_1d_index : verify_program<test_gather_1d_index<Index>>
 {
     migraphx::program create_program() const
     {
@@ -35,7 +36,7 @@ struct test_gather_1d_index : verify_program<test_gather_1d_index>
         auto* mm = p.get_main_module();
         migraphx::shape s{migraphx::shape::float_type, {3, 3}};
         migraphx::shape s_indices{migraphx::shape::int32_type, {1}};
-        std::vector<int> indices{1};
+        std::vector<int> indices{Index};
         auto a0  = mm->add_parameter("data", s);
         auto a1  = mm->add_literal(migraphx::literal{s_indices, indices});
         int axis = -1;
@@ -43,3 +44,7 @@ struct test_gather_1d_index : verify_program<test_gather_1d_index>
         return p;
     }
 };
+
+// Test for positive and negative single dim indices
+template struct test_gather_1d_index<1>;
+template struct test_gather_1d_index<-1>;

test/verify/test_gather_asymmetric.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_gather_asymmetric : verify_program<test_gather_asymmetric>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s{migraphx::shape::float_type, {3, 5}};
+        migraphx::shape s_indices{migraphx::shape::int32_type, {2, 1}};
+        std::vector<int> indices{1, 2};
+        auto a0  = mm->add_parameter("data", s);
+        auto a1  = mm->add_literal(migraphx::literal{s_indices, indices});
+        int axis = 0;
+        mm->add_instruction(migraphx::make_op("gather", {{"axis", axis}}), a0, a1);
+        return p;
+    }
+};

test/verify/test_gather_neg_axis_even_dims.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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.
+ */
+
+#include "verify_program.hpp"
+#include <migraphx/program.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/make_op.hpp>
+
+struct test_gather_neg_axis_even_dims : verify_program<test_gather_neg_axis_even_dims>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        auto* mm = p.get_main_module();
+        migraphx::shape s{migraphx::shape::float_type, {4, 4}};
+        migraphx::shape s_indices{migraphx::shape::int32_type, {2, 2}};
+        std::vector<int> indices{1, 2, 2, 1};
+        auto a0  = mm->add_parameter("data", s);
+        auto a1  = mm->add_literal(migraphx::literal{s_indices, indices});
+        int axis = -1;
+        mm->add_instruction(migraphx::make_op("gather", {{"axis", axis}}), a0, a1);
+        return p;
+    }
+};