Merge branch 'develop' into test_onnx_zoo

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

+name: Add items to GH project
+
+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.TEST_PR_WORKFLOW }}

.github/workflows/sync-onnxrt-main.yaml

+name: Onnxruntime main weekly sync
+on:
+  schedule:
+    - cron: "05 09 * * 5"
+
+jobs:
+  sync:
+    steps:
+      - uses: actions/checkout@v3
+        with:
+        ref: develop
+        path: ../
+
+  get_date:
+    steps:
+      - run: echo "::set-output name=date::$(date +'%Y-%m-%d')"
+
+  update_file:
+    needs: [sync get_date]
+    steps:
+      - run: git clone https://github.com/microsoft/onnxruntime.git && cd onnxruntime && git rev-parse HEAD >> ../test/onnx/.onnxrt-commit
+
+  Add_commit:
+    needs: update_file
+    steps:
+      - name: Add & Commit
+        uses: EndBug/add-and-commit@v9.1.1
+        with:
+          new_branch: onnxruntime-sync-${{ steps.date.outputs.date }}
+          add: ../test/onnx/.onnxrt-commit
+          message: Update Onnxruntime commit to latest release
+          default_author: github_actions
+          push: true
+  PR:
+    needs: Add_commit
+    steps:
+      - name: GitHub Action for creating Pull Requests
+        uses: devops-infra/action-pull-request@v0.5.3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          title: Sync Onnxruntime main
+          reviewer: pfultz2, causten
+          assignee: TedThemistokleous
+          label: automatic, onnxruntime
+          target_branch: develop

Dockerfile

@@ -5,6 +5,10 @@ ARG PREFIX=/usr/local
 # Support multiarch
 RUN dpkg --add-architecture i386
 
+# Install rocm key
+RUN apt-get update && apt-get install -y gnupg2 --no-install-recommends curl && \
+    curl -sL http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add - 
+
 # Add rocm repository
 RUN sh -c 'echo deb [arch=amd64 trusted=yes] http://repo.radeon.com/rocm/apt/5.3/ ubuntu main > /etc/apt/sources.list.d/rocm.list'
 
@@ -32,10 +36,27 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
     libnuma-dev \
     miopen-hip \
     rocblas \
+    hipfft \
+    rocthrust \
+    rocrand \
+    hipsparse \
+    rccl \
+    rccl-dev \
+    rocm-smi-lib \
+    rocm-dev \
+    roctracer-dev \
+    hipcub  \
+    hipblas  \
+    hipify-clang \
+    half \
+    libssl-dev \
     zlib1g-dev && \
     apt-get clean && \
     rm -rf /var/lib/apt/lists/*
 
+# add this for roctracer dependancies
+RUN pip3 install CppHeaderParser packaging==22.0
+
 # Workaround broken rocm packages
 RUN ln -s /opt/rocm-* /opt/rocm
 RUN echo "/opt/rocm/lib" > /etc/ld.so.conf.d/rocm.conf
@@ -72,22 +93,27 @@ RUN /download_models.sh && rm /download_models.sh
 # Install latest ccache version
 RUN cget -p $PREFIX install facebook/zstd@v1.4.5 -X subdir -DCMAKE_DIR=build/cmake
 RUN cget -p $PREFIX install ccache@v4.1 -DENABLE_TESTING=OFF
+RUN cget -p /opt/cmake install kitware/cmake@v3.24.3
 
-# Install newer cmake for onnx runtime
-ARG CMAKE_VERSION=3.24.2
-RUN cget -p /opt/cmake install -X binary https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION}/cmake-${CMAKE_VERSION}-Linux-x86_64.tar.gz
+RUN export ONNXRT_COMMIT=$(cat test/onnx/.onnxrt-commit)
 
 ARG ONNXRUNTIME_REPO=https://github.com/Microsoft/onnxruntime
 ARG ONNXRUNTIME_BRANCH=main
-ARG ONNXRUNTIME_COMMIT=24f1bd6156cf5968bbc76dfb0e801a9b9c56b9fc
+ARG ONNXRUNTIME_COMMIT=$ONNXRT_COMMIT
+
+# Let us know which commit where're using for CI
+RUN echo "Onnxruntime Commit:" && echo $ONNXRUNTIME_COMMIT
+
 RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXRUNTIME_REPO} onnxruntime && \
     cd onnxruntime && \
     git checkout ${ONNXRUNTIME_COMMIT} && \
     /bin/sh dockerfiles/scripts/install_common_deps.sh
 
+
+
 ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
 
-RUN cget -p /usr/local install ROCmSoftwarePlatform/llvm-project-mlir@c0723a7e50043d973cb73ae51dc30d36679ee7e5 -DBUILD_MIXR_TARGET=On
+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")
 //    }
 //}
 

examples/nlp/python_bert_squad/requirements_bertsquad.txt

@@ -21,6 +21,6 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 # THE SOFTWARE.
 #####################################################################################
-tensorflow==2.7.2
+tensorflow==2.9.3
 onnxruntime
 tokenizers
\ No newline at end of file

src/common.cpp

@@ -77,7 +77,6 @@ std::vector<shape::dynamic_dimension> compute_broadcasted_dyn_dims(shape s0, sha
     }
     auto offset = s1.ndim() - s0.ndim();
     std::vector<shape::dynamic_dimension> out_dims(s1.dyn_dims());
-    shape::dynamic_dimension one_dyn_dim{1, 1, 0};
     std::transform(
         s0.dyn_dims().cbegin(),
         s0.dyn_dims().cend(),
@@ -88,7 +87,7 @@ std::vector<shape::dynamic_dimension> compute_broadcasted_dyn_dims(shape s0, sha
             {
                 return a;
             }
-            else if(a == one_dyn_dim or b == one_dyn_dim)
+            else if(a == 1 or b == 1)
             {
                 // setting opt to 0, may need to be changed
                 return shape::dynamic_dimension{std::max(a.min, b.min), std::max(a.max, b.max), 0};

src/dead_code_elimination.cpp

@@ -51,8 +51,8 @@ void dead_code_elimination::apply(module& m) const
         // Skip instruction with empty shape as output unless its [dynamic, builtin, undefined,
         // identity, allocate]
         if((not i->get_shape().dynamic() and i->get_shape().elements() == 0) and
-           i->name().front() != '@' and
-           not contains({"undefined", "identity", "allocate"}, i->name()))
+           not(i->name().front() == '@') and not contains({"identity", "allocate"}, i->name()) and
+           not i->is_undefined())
             continue;
         assert(std::distance(m.begin(), i) <= std::distance(m.begin(), last));
         std::unordered_set<instruction_ref> visited;

src/driver/main.cpp

@@ -109,8 +109,12 @@ struct loader
         ap(brief, {"--brief"}, ap.help("Make the output brief."), ap.set_value(true));
         ap(output_type,
            {"--cpp"},
-           ap.help("Print out the program as cpp program."),
+           ap.help("Print out the program as C++ program."),
            ap.set_value("cpp"));
+        ap(output_type,
+           {"--python", "--py"},
+           ap.help("Print out the program as python program."),
+           ap.set_value("py"));
         ap(output_type, {"--json"}, ap.help("Print out program as json."), ap.set_value("json"));
         ap(output_type,
            {"--text"},
@@ -259,7 +263,9 @@ struct loader
                 type = "binary";
         }
 
-        if(type == "cpp")
+        if(type == "py")
+            p.print_py(*os);
+        else if(type == "cpp")
             p.print_cpp(*os);
         else if(type == "graphviz")
             p.print_graph(*os, brief);

src/file_buffer.cpp

@@ -30,23 +30,31 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
 template <class T>
-T generic_read_file(const std::string& filename)
+T generic_read_file(const std::string& filename, size_t offset = 0, size_t nbytes = 0)
 {
     std::ifstream is(filename, std::ios::binary | std::ios::ate);
-    std::streamsize size = is.tellg();
-    if(size < 1)
+    if(nbytes == 0)
+    {
+        // if there is a non-zero offset and nbytes is not set,
+        // calculate size of remaining bytes to read
+        nbytes = is.tellg();
+        if(offset > nbytes)
+            MIGRAPHX_THROW("offset is larger than file size");
+        nbytes -= offset;
+    }
+    if(nbytes < 1)
         MIGRAPHX_THROW("Invalid size for: " + filename);
-    is.seekg(0, std::ios::beg);
+    is.seekg(offset, std::ios::beg);
 
-    T buffer(size, 0);
-    if(not is.read(&buffer[0], size))
+    T buffer(nbytes, 0);
+    if(not is.read(&buffer[0], nbytes))
         MIGRAPHX_THROW("Error reading file: " + filename);
     return buffer;
 }
 
-std::vector<char> read_buffer(const std::string& filename)
+std::vector<char> read_buffer(const std::string& filename, size_t offset, size_t nbytes)
 {
-    return generic_read_file<std::vector<char>>(filename);
+    return generic_read_file<std::vector<char>>(filename, offset, nbytes);
 }
 
 std::string read_string(const std::string& filename)

src/include/migraphx/check_shapes.hpp

@@ -198,7 +198,7 @@ struct check_shapes
      */
     const check_shapes& same_ndims() const
     {
-        if(not this->same([](const shape& s) { return s.max_lens().size(); }))
+        if(not this->same([](const shape& s) { return s.ndim(); }))
             MIGRAPHX_THROW(prefix() + "Number of dimensions do not match");
         return *this;
     }

src/include/migraphx/file_buffer.hpp

@@ -31,7 +31,7 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-std::vector<char> read_buffer(const std::string& filename);
+std::vector<char> read_buffer(const std::string& filename, size_t offset = 0, size_t nbytes = 0);
 std::string read_string(const std::string& filename);
 
 void write_buffer(const std::string& filename, const char* buffer, std::size_t size);

src/include/migraphx/instruction.hpp

@@ -121,6 +121,8 @@ struct instruction
 
     bool can_eval() const;
 
+    bool is_undefined() const;
+
     argument eval(bool check_eval = true) const;
 
     void finalize(context& ctx);

src/include/migraphx/literal.hpp

@@ -80,6 +80,7 @@ struct literal : raw_data<literal>
         fill(start, end);
     }
 
+    // Directly copies buffer of x
     template <class T, MIGRAPHX_REQUIRES(sizeof(T) == 1)>
     literal(const shape& s, T* x) : buffer(make_shared_array<char>(s.bytes())), m_shape(s)
     {
@@ -107,25 +108,15 @@ struct literal : raw_data<literal>
     std::shared_ptr<char> buffer;
     shape m_shape;
 
+    // Keeps the same data ordering as the given container
     template <class Iterator>
     void fill(Iterator start, Iterator end)
     {
         assert(std::distance(start, end) == m_shape.elements());
-        if(m_shape.standard())
-        {
-            m_shape.visit_type([&](auto as) { std::copy(start, end, as.from(buffer.get())); });
-        }
-        else
-        {
-            auto it = start;
-            m_shape.visit_type([&](auto as) {
-                auto output = make_view(m_shape, as.from(buffer.get()));
-                shape_for_each(output.get_shape(), [&](const auto& idx) {
-                    output(idx.begin(), idx.end()) = *it; // NOLINT(bugprone-signed-char-misuse)
-                    it++;
-                });
-            });
-        }
+        m_shape.visit_type([&](auto as) {
+            auto output = make_view(m_shape, as.from(buffer.get()));
+            std::copy(start, end, output.begin());
+        });
     }
 };
 

src/include/migraphx/module.hpp

@@ -205,6 +205,12 @@ struct module
 
     void print_graph(std::ostream& os, bool brief = false) const;
 
+    void print_py(std::ostream& os) const;
+    std::unordered_map<instruction_ref, std::string>
+    print_py(std::ostream& os,
+             const std::string& mname,
+             std::unordered_map<instruction_ref, std::string> names) const;
+
     void print_cpp(std::ostream& os) const;
     std::unordered_map<instruction_ref, std::string>
     print_cpp(std::ostream& os,

src/include/migraphx/op/argmax.hpp

@@ -30,6 +30,7 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -56,12 +57,20 @@ struct argmax
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto lens = inputs[0].lens();
-
-        lens[axis] = 1;
-
-        return {shape::int64_type, lens};
+        check_shapes{inputs, *this, true}.has(1);
+        const auto& s0 = inputs[0];
+        if(s0.dynamic())
+        {
+            auto dyn_dims  = s0.dyn_dims();
+            dyn_dims[axis] = {1, 1, 0};
+            return {shape::int64_type, dyn_dims};
+        }
+        else
+        {
+            auto lens  = s0.lens();
+            lens[axis] = 1;
+            return {shape::int64_type, lens};
+        }
     }
 
     template <class T>
@@ -79,19 +88,18 @@ struct argmax
                 max_index = i;
             }
         }
-
         return max_index;
     }
 
-    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 batch_item_num = args.front().get_shape().lens()[axis];
 
         result.visit([&](auto output) {
             args[0].visit([&](auto input) {
-                par_for(output_shape.elements(), [&](auto i) {
-                    auto data_idx = output_shape.multi(i);
+                par_for(dyn_out.computed_shape.elements(), [&](auto i) {
+                    auto data_idx = dyn_out.computed_shape.multi(i);
                     output[i]     = this->calc_argmax(input, data_idx, batch_item_num);
                 });
             });

src/include/migraphx/op/dot.hpp

@@ -28,6 +28,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/gemm.hpp>
+#include <migraphx/dyn_output.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -38,41 +39,69 @@ struct dot
     std::string name() const { return "dot"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.same_type().has(2);
+        check_shapes{inputs, *this, true}.same_type().same_ndims().has(2);
         const shape& a = inputs.at(0);
         const shape& b = inputs.at(1);
         auto t         = a.type();
 
-        if(not std::all_of(
-               inputs.begin(), inputs.end(), [](auto s) { return s.lens().size() >= 2; }))
+        if(not std::all_of(inputs.begin(), inputs.end(), [](auto s) { return s.ndim() >= 2; }))
         {
-            MIGRAPHX_THROW("DOT: dot only accept 2 or more dims operands");
+            MIGRAPHX_THROW("DOT: dot only accepts operands with 2 or more dimensions ");
         }
-
-        // only handle the case that the batch size of a and b are the same
-        if(not std::equal(
-               a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
+        if(a.dynamic() or b.dynamic())
         {
-            MIGRAPHX_THROW("DOT: batch size of A and B mismatch: {" + to_string_range(a.lens()) +
-                           "} x {" + to_string_range(b.lens()) + "}");
+            auto s0 = a.to_dynamic();
+            auto s1 = b.to_dynamic();
+            if(not std::equal(s0.dyn_dims().rbegin() + 2,
+                              s0.dyn_dims().rend(),
+                              s1.dyn_dims().rbegin() + 2,
+                              s1.dyn_dims().rend()))
+            {
+                MIGRAPHX_THROW("DOT: dynamic outer dimensions of A and B mismatch: {" +
+                               to_string_range(s0.dyn_dims()) + "} x {" +
+                               to_string_range(s1.dyn_dims()) + "}");
+            }
+            std::size_t dim_0 = s0.ndim() - 2;
+            std::size_t dim_1 = s0.ndim() - 1;
+            if(s0.dyn_dims()[dim_1] != s1.dyn_dims()[dim_0])
+            {
+                MIGRAPHX_THROW("DOT: dynamic inner dimensions do not match: {" +
+                               to_string_range(s0.dyn_dims()) + "} x {" +
+                               to_string_range(s1.dyn_dims()) + "}");
+            }
+            auto out_dyn_dims   = s0.dyn_dims();
+            out_dyn_dims[dim_1] = s1.dyn_dims()[dim_1];
+            return {t, out_dyn_dims};
         }
-
-        std::size_t dim_0 = a.lens().size() - 2;
-        std::size_t dim_1 = a.lens().size() - 1;
-        if(a.lens()[dim_1] != b.lens()[dim_0])
+        else
         {
-            MIGRAPHX_THROW("DOT: inner dimensions do not match: {" + to_string_range(a.lens()) +
-                           "} x {" + to_string_range(b.lens()) + "}");
-        }
+            // only handle the case that all the dimensions except the last two are the same
+            if(not std::equal(
+                   a.lens().rbegin() + 2, a.lens().rend(), b.lens().rbegin() + 2, b.lens().rend()))
+            {
+                MIGRAPHX_THROW("DOT: static outer dimensions of A and B mismatch: {" +
+                               to_string_range(a.lens()) + "} x {" + to_string_range(b.lens()) +
+                               "}");
+            }
 
-        auto out_lens   = a.lens();
-        out_lens[dim_1] = b.lens()[dim_1];
-        return {t, out_lens};
+            std::size_t dim_0 = a.ndim() - 2;
+            std::size_t dim_1 = a.ndim() - 1;
+            if(a.lens()[dim_1] != b.lens()[dim_0])
+            {
+                MIGRAPHX_THROW("DOT: static inner dimensions do not match: {" +
+                               to_string_range(a.lens()) + "} x {" + to_string_range(b.lens()) +
+                               "}");
+            }
+
+            auto out_lens   = a.lens();
+            out_lens[dim_1] = b.lens()[dim_1];
+            return {t, out_lens};
+        }
     }
 
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result = argument{output_shape};
+        argument result = argument{dyn_out.computed_shape};
         visit_all(result, args[0], args[1])(
             [&](auto cmat, auto amat, auto bmat) { gemm(cmat, amat, bmat, 1.0f, 0.0f); });
         return result;

src/include/migraphx/op/flatten.hpp

@@ -55,17 +55,47 @@ struct flatten
     std::string name() const { return "flatten"; }
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1).standard();
-        auto&& lens = inputs.front().lens();
-        auto x =
-            std::accumulate(lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
-        auto y =
-            std::accumulate(lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
-        return {inputs.at(0).type(), {x, y}};
+        check_shapes{inputs, *this, true}.has(1);
+        auto s = inputs[0];
+        if(s.dynamic())
+        {
+            auto min_lens = s.min_lens();
+            auto max_lens = s.max_lens();
+            auto opt_lens = s.opt_lens();
+            // If any of the opt values is 0, output opt will be 0
+            shape::dynamic_dimension x = {
+                std::accumulate(
+                    min_lens.begin(), min_lens.begin() + axis, std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    max_lens.begin(), max_lens.begin() + axis, std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(opt_lens.begin(),
+                                opt_lens.begin() + axis,
+                                std::size_t{1},
+                                std::multiplies<>{})};
+            shape::dynamic_dimension y = {
+                std::accumulate(
+                    min_lens.begin() + axis, min_lens.end(), std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    max_lens.begin() + axis, max_lens.end(), std::size_t{1}, std::multiplies<>{}),
+                std::accumulate(
+                    opt_lens.begin() + axis, opt_lens.end(), std::size_t{1}, std::multiplies<>{}),
+            };
+            return {s.type(), {x, y}};
+        }
+        else
+        {
+            check_shapes{inputs, *this}.standard();
+            auto&& lens = s.lens();
+            auto x      = std::accumulate(
+                lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<>{});
+            auto y = std::accumulate(
+                lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<>{});
+            return {s.type(), {x, y}};
+        }
     }
-    argument compute(shape output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        return args[0].reshape(output_shape);
+        return args[0].reshape(dyn_out.computed_shape);
     }
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };

src/include/migraphx/op/pad.hpp

@@ -59,18 +59,29 @@ struct pad
     std::string name() const { return "pad"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
-        auto&& idims = inputs.front().lens();
-        std::vector<std::size_t> rdims(idims.begin(), idims.end());
-        std::size_t num_dims = rdims.size();
-
-        for(std::size_t i = 0; i < num_dims; i++)
+        check_shapes{inputs, *this, true}.has(1);
+        const auto& s0 = inputs.front();
+        if(s0.dynamic())
         {
-            rdims[i] += pads[i] + pads[i + num_dims];
+            auto out_dyn_dims = s0.dyn_dims();
+            for(std::size_t i = 0; i < s0.ndim(); ++i)
+            {
+                out_dyn_dims[i] += pads[i] + pads[i + s0.ndim()];
+            }
+            return {s0.type(), out_dyn_dims};
+        }
+        else
+        {
+            auto&& idims = s0.lens();
+            std::vector<std::size_t> rdims(idims.begin(), idims.end());
+            std::size_t num_dims = rdims.size();
+            for(std::size_t i = 0; i < num_dims; i++)
+            {
+                rdims[i] += pads[i] + pads[i + num_dims];
+            }
+            shape s{s0.type(), rdims};
+            return s;
         }
-
-        shape s{inputs.front().type(), rdims};
-        return s;
     }
 
     std::size_t pad_ndims() const

src/include/migraphx/op/pooling.hpp

@@ -31,7 +31,7 @@
 #include <migraphx/argument.hpp>
 #include <migraphx/par_for.hpp>
 #include <migraphx/shape_for_each.hpp>
-#include <migraphx/int_divide.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <cmath>
 #include <utility>
 
@@ -49,6 +49,9 @@ struct pooling
     bool ceil_mode                   = false;
     int lp_order                     = 2;
 
+    // Global pooling with dynamic shape input
+    bool dyn_global = false;
+
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
@@ -57,7 +60,8 @@ struct pooling
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"),
                     f(self.ceil_mode, "ceil_mode"),
-                    f(self.lp_order, "lp_order"));
+                    f(self.lp_order, "lp_order"),
+                    f(self.dyn_global, "dyn_global"));
     }
 
     std::string name() const { return "pooling"; }
@@ -65,51 +69,111 @@ struct pooling
     void check_attribute_size() const
     {
         if((padding.size() != stride.size() and (padding.size() / 2) != stride.size()) or
-           stride.size() != lengths.size())
+           (not dyn_global and stride.size() != lengths.size()))
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
     }
 
+    size_t kdims() const
+    {
+        check_attribute_size();
+        return stride.size();
+    }
+
     value attributes() const { return {{"normalize_padding", "padding"}}; }
 
+    std::vector<std::size_t> calc_spatial_dim_out(const std::vector<std::size_t>& input_lens,
+                                                  std::size_t kdims) const
+    {
+        std::vector<std::size_t> output_lens{};
+        for(size_t i = 0; i < kdims; ++i)
+        {
+            if(input_lens[i + 2] == 0)
+            {
+                // handle opt = 0
+                output_lens.push_back(0);
+            }
+            else
+            {
+                std::size_t padding_factor = 2 * padding[i];
+                if(padding.size() == 2 * kdims)
+                    padding_factor = padding[i] + padding[i + kdims];
+                assert(input_lens[i + 2] + padding_factor >= lengths[i]);
+                std::size_t dim_size = input_lens[i + 2] + padding_factor - lengths[i];
+                std::size_t len =
+                    (ceil_mode)
+                        ? dim_size / stride[i] + static_cast<std::size_t>((dim_size % stride[i] !=
+                                                                           0)) // ceil uint divide
+                        : dim_size / stride[i];                                // floor divide
+                output_lens.push_back(len + 1);
+            }
+        }
+        return output_lens;
+    }
+
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(1);
+        check_shapes{inputs, *this, true}.has(1);
+        check_attribute_size();
 
         const shape& input = inputs.at(0);
-
-        auto input_lens   = input.lens();
-        size_t kdims      = input_lens.size() - 2;
-        auto input_size   = inputs[0].lens().size();
-        auto padding_size = padding.size();
-        if(input_size != padding_size / 2 + 2 and input_size != padding_size + 2)
+        auto padding_size  = padding.size();
+        size_t kdims       = input.ndim() - 2;
+        if(input.ndim() != padding_size / 2 + 2 and input.ndim() != padding_size + 2)
         {
             MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
         }
 
-        std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
-
-        for(size_t i = 0; i < kdims; i++)
+        if(input.dynamic())
         {
-            std::ptrdiff_t dim_size;
-            auto padding_factor = 2 * padding[i];
-            if(padding_size == 2 * kdims)
-                padding_factor = padding[i] + padding[i + kdims];
-            dim_size = input_lens[i + 2] + padding_factor - lengths[i];
-            assert(dim_size >= 0);
-            std::size_t len = (ceil_mode) ? ceil_divide<std::ptrdiff_t>(dim_size, stride[i])
-                                          : floor_divide<std::ptrdiff_t>(dim_size, stride[i]);
-
-            output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(1, len + 1)));
+            auto input_dyn_dims = input.dyn_dims();
+            std::vector<shape::dynamic_dimension> output_dyn_dims(input_dyn_dims.begin(),
+                                                                  input_dyn_dims.begin() + 2);
+            if(dyn_global)
+            {
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_dyn_dims.push_back(shape::dynamic_dimension{1, 1, 1});
+                }
+                return {input.type(), output_dyn_dims};
+            }
+            else
+            {
+                auto min_spatial_dims = calc_spatial_dim_out(input.min_lens(), kdims);
+                auto max_spatial_dims = calc_spatial_dim_out(input.max_lens(), kdims);
+                auto opt_spatial_dims = calc_spatial_dim_out(input.opt_lens(), kdims);
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_dyn_dims.push_back(shape::dynamic_dimension{
+                        min_spatial_dims[i], max_spatial_dims[i], opt_spatial_dims[i]});
+                }
+                return {input.type(), output_dyn_dims};
+            }
         }
-        return inputs[0].with_lens(output_lens);
-    }
+        else
+        {
+            auto input_lens = input.lens();
 
-    size_t kdims() const
-    {
-        check_attribute_size();
-        return stride.size();
+            std::vector<std::size_t> output_lens(input_lens.begin(), input_lens.begin() + 2);
+            // Used for when normalize_compute_shape() is called again at model eval time
+            // for an originally dynamic shape. Since kernel shape is not used with dyn_global.
+            if(dyn_global)
+            {
+                for(size_t i = 0; i < kdims; ++i)
+                {
+                    output_lens.push_back(1);
+                }
+                return {input.type(), output_lens};
+            }
+            else
+            {
+                auto output_spatial_lens = calc_spatial_dim_out(input_lens, kdims);
+                output_lens.insert(
+                    output_lens.end(), output_spatial_lens.begin(), output_spatial_lens.end());
+                return inputs[0].with_lens(output_lens);
+            }
+        }
     }
 
     struct lpnorm_pool
@@ -158,7 +222,11 @@ struct pooling
     };
 
     template <class Type, class Out, class In, class Op>
-    void calc_pooling(const shape& output_shape, Out& output, const In& input, Op op) const
+    void calc_pooling(const shape& output_shape,
+                      Out& output,
+                      const In& input,
+                      const std::vector<std::size_t>& kernel_dims,
+                      Op op) const
     {
         auto in_s    = input.get_shape();
         auto in_lens = in_s.lens();
@@ -172,7 +240,7 @@ struct pooling
                 auto d_2 = dim - 2;
                 int start =
                     static_cast<int>(idx_o[dim] * stride[d_2]) - static_cast<int>(padding[d_2]);
-                int end = std::min(start + lengths[d_2], in_lens[dim]);
+                int end = std::min(start + kernel_dims[d_2], in_lens[dim]);
                 start   = std::max(start, 0);
                 win_start.push_back(start);
                 win_size.push_back(end - start);
@@ -198,21 +266,32 @@ struct pooling
         });
     }
 
-    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 input_lens = args[0].get_shape().lens();
+        std::vector<std::size_t> kernel_dims;
+        if(dyn_global)
+        {
+            kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
+        }
+        else
+        {
+            kernel_dims = this->lengths;
+        }
         visit_all(result, args[0])([&](auto output, auto input) {
             using type = typename decltype(output)::value_type;
             switch(mode)
             {
             case migraphx::op::pooling_mode::average:
-                calc_pooling<type>(output_shape, output, input, avg_pool{});
+                calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, avg_pool{});
                 break;
             case migraphx::op::pooling_mode::max:
-                calc_pooling<type>(output_shape, output, input, max_pool{});
+                calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, max_pool{});
                 break;
             case migraphx::op::pooling_mode::lpnorm:
-                calc_pooling<type>(output_shape, output, input, lpnorm_pool{lp_order});
+                calc_pooling<type>(
+                    dyn_out.computed_shape, output, input, kernel_dims, lpnorm_pool{lp_order});
                 break;
             }
         });

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);
             });
         });