Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into argmax_min

src/eliminate_pad.cpp

@@ -43,9 +43,7 @@ void eliminate_pad::update_op(T,
     std::vector<int64_t> pads = pad_op.pads;
     std::array<size_t, 2> new_pads{static_cast<size_t>(pads[2]), static_cast<size_t>(pads[3])};
 
-    T op = any_cast<T>(ins->get_operator());
-    if(op.padding_mode != op::padding_mode_t::default_)
-        return;
+    T op       = any_cast<T>(ins->get_operator());
     op.padding = new_pads;
 
     std::vector<instruction_ref> new_inputs{ins->inputs()};

src/include/migraphx/op/binary.hpp

@@ -28,8 +28,10 @@ struct binary : op_name<Derived>
     argument compute(const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
+        auto s1 = args[0].get_shape();
+        auto s2 = args[1].get_shape();
         visit_all(result, args[0], args[1])([&](auto output, auto input1, auto input2) {
-            if(input1.get_shape().packed() and input2.get_shape().packed())
+            if(s1 == s2 and input1.get_shape().packed() and input2.get_shape().packed())
             {
                 std::transform(input1.begin(),
                                input1.end(),

src/include/migraphx/op/convolution.hpp

@@ -44,51 +44,24 @@ struct convolution
         const shape& input   = inputs.at(0);
         const shape& weights = inputs.at(1);
         auto t               = input.type();
-        if(padding_mode == default_)
-        {
-            return {t,
-                    {
-                        input.lens()[0],
-                        weights.lens()[0],
-                        std::size_t(std::max<std::ptrdiff_t>(
-                            1,
-                            (input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
-                             2 * padding[0]) /
-                                    stride[0] +
-                                1)),
-                        std::size_t(std::max<std::ptrdiff_t>(
-                            1,
-                            (input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
-                             2 * padding[1]) /
-                                    stride[1] +
-                                1)),
-                    }};
-        }
-        else if(padding_mode == same)
-        {
-            return {t,
-                    {input.lens()[0],
-                     weights.lens()[0],
-                     static_cast<std::size_t>(
-                         std::ceil(static_cast<double>(input.lens()[2]) / stride[0])),
-                     static_cast<std::size_t>(
-                         std::ceil(static_cast<double>(input.lens()[3]) / stride[1]))}};
-        }
-        else if(padding_mode == valid)
-        {
-            return {
-                t,
-                {input.lens()[0],
-                 weights.lens()[0],
-                 static_cast<std::size_t>(std::ceil(
-                     static_cast<double>(input.lens()[2] - weights.lens()[2] + 1) / stride[0])),
-                 static_cast<std::size_t>(std::ceil(
-                     static_cast<double>(input.lens()[3] - weights.lens()[3] + 1) / stride[1]))}};
-        }
-        else
-        {
-            MIGRAPHX_THROW("Invalid padding mode");
-        }
+
+        return {t,
+                {
+                    input.lens()[0],
+                    weights.lens()[0],
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        (input.lens()[2] - (1 + dilation[0] * (weights.lens()[2] - 1)) +
+                         2 * padding[0]) /
+                                stride[0] +
+                            1)),
+                    std::size_t(std::max<std::ptrdiff_t>(
+                        1,
+                        (input.lens()[3] - (1 + dilation[1] * (weights.lens()[3] - 1)) +
+                         2 * padding[1]) /
+                                stride[1] +
+                            1)),
+                }};
     }
 };
 

src/include/migraphx/op/pooling.hpp

@@ -48,51 +48,21 @@ struct pooling
         assert(lengths[0] <= (input.lens()[2] + 2 * padding[0]));
         assert(lengths[1] <= (input.lens()[3] + 2 * padding[1]));
 
-        if(padding_mode == default_)
-        {
-            return {t,
-                    {
-                        input.lens()[0],
-                        input.lens()[1],
-                        std::size_t(std::max<std::ptrdiff_t>(
-                            1,
-                            floor_divide<std::ptrdiff_t>(
-                                input.lens()[2] + 2 * padding[0] - lengths[0], stride[0]) +
-                                1)),
-                        std::size_t(std::max<std::ptrdiff_t>(
-                            1,
-                            floor_divide<std::ptrdiff_t>(
-                                input.lens()[3] + 2 * padding[1] - lengths[1], stride[1]) +
-                                1)),
-                    }};
-        }
-        else if(padding_mode == same)
-        {
-            return {t,
-                    {input.lens()[0],
-                     input.lens()[1],
-                     ceil_divide<std::size_t>(input.lens()[2], stride[0]),
-                     ceil_divide<std::size_t>(input.lens()[3], stride[1])}};
-        }
-        else if(padding_mode == valid)
-        {
-            return {
-                t,
+        return {t,
                 {
                     input.lens()[0],
                     input.lens()[1],
                     std::size_t(std::max<std::ptrdiff_t>(
                         1,
-                        floor_divide<std::ptrdiff_t>(input.lens()[2] - lengths[0], stride[0]) + 1)),
+                        floor_divide<std::ptrdiff_t>(input.lens()[2] + 2 * padding[0] - lengths[0],
+                                                     stride[0]) +
+                            1)),
                     std::size_t(std::max<std::ptrdiff_t>(
                         1,
-                        floor_divide<std::ptrdiff_t>(input.lens()[3] - lengths[1], stride[1]) + 1)),
+                        floor_divide<std::ptrdiff_t>(input.lens()[3] + 2 * padding[1] - lengths[1],
+                                                     stride[1]) +
+                            1)),
                 }};
-        }
-        else
-        {
-            MIGRAPHX_THROW("Invalid padding mode");
-        }
     }
 };
 

src/include/migraphx/pad_calc.hpp

@@ -2,13 +2,24 @@
 #define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
 
 #include <utility>
+#include <cstdint>
+#include <vector>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-inline std::size_t calculate_padding(std::size_t weight_dim, std::size_t dilation)
+inline void calculate_padding(int64_t idx,
+                              std::vector<int64_t>& pads,
+                              int64_t input_dim,
+                              int64_t stride,
+                              int64_t dilation,
+                              int64_t weight_dim)
 {
-    return (dilation * (weight_dim - 1)) / 2;
+    int64_t output_dim = input_dim / stride;
+    int64_t pad        = std::max(static_cast<int64_t>(0),
+                           (output_dim - 1) * stride + dilation * weight_dim - input_dim);
+    pads[idx]          = pad / 2;
+    pads[idx + 2]      = pad - pad / 2;
 }
 
 } // namespace MIGRAPHX_INLINE_NS

src/include/migraphx/stringutils.hpp

@@ -52,6 +52,8 @@ inline std::string transform_string(std::string s, F f)
 
 inline std::string to_upper(std::string s) { return transform_string(std::move(s), ::toupper); }
 
+inline std::string to_lower(std::string s) { return transform_string(std::move(s), ::tolower); }
+
 inline bool starts_with(const std::string& value, const std::string& prefix)
 {
     if(prefix.size() > value.size())

src/onnx/CMakeLists.txt

@@ -19,7 +19,7 @@ rocm_install_targets(
 
 add_executable(read_onnx read_onnx.cpp)
 rocm_clang_tidy_check(read_onnx)
-target_link_libraries(read_onnx migraphx_onnx)
+target_link_libraries(read_onnx migraphx_cpu migraphx_onnx)
 
 
 if(MIGRAPHX_ENABLE_GPU)

src/onnx/onnx.cpp

@@ -100,6 +100,7 @@ struct onnx_parser
 
     void init_actv_func()
     {
+        // Support name format of all lower case or the first letter capital
         map_actv_funcs.insert(std::make_pair("tanh", op::tanh{}));
         map_actv_funcs.insert(std::make_pair("relu", op::relu{}));
         map_actv_funcs.insert(std::make_pair("sigmoid", op::sigmoid{}));
@@ -352,7 +353,8 @@ struct onnx_parser
             {
                 // insert zeros for pad op (args[0] has 4 dims)
                 padding = {0, 0, padding[0], padding[1], 0, 0, padding[2], padding[3]};
-                l0      = prog.add_instruction(op::pad{padding}, l0);
+                l0 = prog.add_instruction(op::pad{padding, std::numeric_limits<float>::lowest()},
+                                          l0);
             }
             else
             {
@@ -870,7 +872,9 @@ struct onnx_parser
             auto names = attributes.at("activations").strings();
             vec_names.clear();
             vec_names.resize(names.size());
-            std::copy(names.begin(), names.end(), vec_names.begin());
+            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
+                return to_lower(name);
+            });
         }
 
         auto name_it = std::find_if(vec_names.begin(), vec_names.end(), [&](auto& name) {
@@ -961,7 +965,9 @@ struct onnx_parser
             auto names = attributes.at("activations").strings();
             vec_names.clear();
             vec_names.resize(names.size());
-            std::copy(names.begin(), names.end(), vec_names.begin());
+            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
+                return to_lower(name);
+            });
         }
 
         // need 4 activation functions
@@ -1088,7 +1094,9 @@ struct onnx_parser
             auto names = attributes.at("activations").strings();
             vec_names.clear();
             vec_names.resize(names.size());
-            std::copy(names.begin(), names.end(), vec_names.begin());
+            std::transform(names.begin(), names.end(), vec_names.begin(), [](auto name) {
+                return to_lower(name);
+            });
         }
 
         // need 6 activation functions for bidirectional directions

src/py/migraphx_py.cpp

@@ -8,6 +8,7 @@
 #include <migraphx/stringutils.hpp>
 #include <migraphx/tf.hpp>
 #include <migraphx/onnx.hpp>
+#include <migraphx/type_name.hpp>
 
 #ifdef HAVE_GPU
 #include <migraphx/gpu/target.hpp>
@@ -101,8 +102,13 @@ migraphx::shape to_shape(const py::buffer_info& info)
             t = as.type_enum();
             n = sizeof(as());
         }
-
     });
+
+    if(n == 0)
+    {
+        MIGRAPHX_THROW("MIGRAPHX PYTHON: Unsupported data type" + info.format);
+    }
+
     auto strides = info.strides;
     std::transform(strides.begin(), strides.end(), strides.begin(), [&](auto i) -> std::size_t {
         return n > 0 ? i / n : 0;

src/targets/gpu/device/include/migraphx/gpu/device/types.hpp

@@ -64,9 +64,9 @@ host_type<T>* host_cast(T* x)
 }
 
 template <class T>
-device_type<T> device_cast(T x)
+device_type<T> device_cast(const T& x)
 {
-    return reinterpret_cast<device_type<T>>(x);
+    return reinterpret_cast<const device_type<T>&>(x);
 }
 
 template <class T>

src/targets/gpu/device/pad.cpp

@@ -4,6 +4,7 @@
 #include <migraphx/gpu/device/pad.hpp>
 #include <migraphx/gpu/device/tensor.hpp>
 #include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/float_equal.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -14,8 +15,17 @@ argument
 pad(hipStream_t stream, argument result, argument arg1, float value, std::vector<std::int64_t> pads)
 {
     std::size_t nelements = arg1.get_shape().elements();
+    visit_all(result)([&](auto output) {
+        auto* outptr                 = device_cast(output.data());
+        using type                   = typename decltype(output)::value_type;
+        device_type<type> device_val = value;
+        if(float_equal(value, std::numeric_limits<float>::lowest()))
+        {
+            device_val = device_cast(std::numeric_limits<type>::lowest());
+        }
+        gs_launch(stream, result.get_shape().elements())([=](auto i) { outptr[i] = device_val; });
+    });
 
-    nary(stream, result)([=] { return value; });
     visit_all(result, arg1)([&](auto output, auto input) {
         visit_tensor_size(result.get_shape().lens().size(), [&](auto ndim) {
             std::size_t offsets[ndim];

src/targets/gpu/include/migraphx/gpu/softmax.hpp

@@ -34,7 +34,7 @@ struct miopen_softmax
         return migraphx::reflect(self.op, f);
     }
 
-    std::string name() const { return "gpu::softmax"; }
+    std::string name() const { return "miopen::softmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const;
     argument
     compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;

src/tf/CMakeLists.txt

@@ -31,7 +31,7 @@ rocm_install_targets(
 
 add_executable(read_tf read_tf.cpp)
 rocm_clang_tidy_check(read_tf)
-target_link_libraries(read_tf migraphx_tf)
+target_link_libraries(read_tf migraphx_tf migraphx_cpu)
 
 if(MIGRAPHX_ENABLE_GPU)
 add_executable(verify_tf verify_tf.cpp)

src/tf/tf.cpp

@@ -317,6 +317,7 @@ struct tf_parser
             }
         }
 
+        auto l0 = args[0];
         if(contains(attributes, "padding"))
         {
             const std::string& pad_mode = attributes.at("padding").s();
@@ -326,8 +327,24 @@ struct tf_parser
                 std::vector<size_t> weight_dims = weights->get_shape().lens();
                 size_t weight_h                 = weight_dims[2];
                 size_t weight_w                 = weight_dims[3];
-                op.padding[0]                   = calculate_padding(weight_h, op.dilation[0]);
-                op.padding[1]                   = calculate_padding(weight_w, op.dilation[1]);
+
+                auto input_dims = l0->get_shape().lens();
+                size_t input_h  = input_dims[2];
+                size_t input_w  = input_dims[3];
+                std::vector<int64_t> pads(input_dims.size());
+                calculate_padding(0, pads, input_h, op.stride[0], op.dilation[0], weight_h);
+                calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);
+
+                if(pads[0] != pads[2] || pads[1] != pads[3])
+                {
+                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
+                    l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
+                }
+                else
+                {
+                    op.padding[0] = pads[0];
+                    op.padding[1] = pads[1];
+                }
             }
             else if(pad_mode.find("VALID") != std::string::npos)
             {
@@ -350,7 +367,7 @@ struct tf_parser
             }
         }
 
-        return prog.add_instruction(op, {args[0], weights});
+        return prog.add_instruction(op, {l0, weights});
     }
 
     instruction_ref parse_depthwiseconv(const std::string&,
@@ -400,17 +417,35 @@ struct tf_parser
             }
         }
 
+        auto l0 = args[0];
         if(contains(attributes, "padding"))
         {
-            const std::string& pad_mode     = attributes.at("padding").s();
-            std::vector<size_t> weight_dims = weights->get_shape().lens();
-            size_t weight_h                 = weight_dims[2];
-            size_t weight_w                 = weight_dims[3];
+            const std::string& pad_mode = attributes.at("padding").s();
+
             if(pad_mode.find("SAME") != std::string::npos)
             {
-                op.padding_mode = op::padding_mode_t::same;
-                op.padding[0]   = calculate_padding(weight_h, op.dilation[0]);
-                op.padding[1]   = calculate_padding(weight_w, op.dilation[1]);
+                op.padding_mode                 = op::padding_mode_t::same;
+                std::vector<size_t> weight_dims = weights->get_shape().lens();
+                size_t weight_h                 = weight_dims[2];
+                size_t weight_w                 = weight_dims[3];
+
+                auto input_dims = l0->get_shape().lens();
+                size_t input_h  = input_dims[2];
+                size_t input_w  = input_dims[3];
+                std::vector<int64_t> pads(input_dims.size());
+                calculate_padding(0, pads, input_h, op.stride[0], op.dilation[0], weight_h);
+                calculate_padding(1, pads, input_w, op.stride[1], op.dilation[1], weight_w);
+
+                if(pads[0] != pads[2] || pads[1] != pads[3])
+                {
+                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
+                    l0 = prog.add_instruction(migraphx::op::pad{padding}, l0);
+                }
+                else
+                {
+                    op.padding[0] = pads[0];
+                    op.padding[1] = pads[1];
+                }
             }
             else if(pad_mode.find("VALID") != std::string::npos)
             {
@@ -432,7 +467,7 @@ struct tf_parser
         auto cweights    = prog.add_instruction(op::contiguous{}, weights);
         auto new_weights = prog.add_instruction(op::reshape{new_weights_shape}, cweights);
 
-        return prog.add_instruction(op, {args[0], new_weights});
+        return prog.add_instruction(op, {l0, new_weights});
     }
 
     instruction_ref
@@ -567,21 +602,39 @@ struct tf_parser
             op.lengths[0] = ksize[2];
             op.lengths[1] = ksize[3];
         }
+
+        auto l0 = args[0];
         if(contains(attributes, "padding"))
         {
             const std::string& pad_mode = attributes.at("padding").s();
             if(pad_mode.find("SAME") != std::string::npos)
             {
                 op.padding_mode = op::padding_mode_t::same;
-                op.padding[0]   = calculate_padding(op.lengths[0], 1);
-                op.padding[1]   = calculate_padding(op.lengths[1], 1);
+                auto input_dims = l0->get_shape().lens();
+                size_t input_h  = input_dims[2];
+                size_t input_w  = input_dims[3];
+                std::vector<int64_t> pads(input_dims.size());
+                calculate_padding(0, pads, input_h, op.stride[0], 1, op.lengths[0]);
+                calculate_padding(1, pads, input_w, op.stride[1], 1, op.lengths[1]);
+
+                if(pads[0] != pads[2] || pads[1] != pads[3])
+                {
+                    std::vector<int64_t> padding = {0, 0, pads[0], pads[1], 0, 0, pads[2], pads[3]};
+                    l0                           = prog.add_instruction(
+                        migraphx::op::pad{padding, std::numeric_limits<float>::lowest()}, l0);
+                }
+                else
+                {
+                    op.padding[0] = pads[0];
+                    op.padding[1] = pads[1];
+                }
             }
             else if(pad_mode.find("VALID") != std::string::npos)
             {
                 op.padding_mode = op::padding_mode_t::valid;
             }
         }
-        return prog.add_instruction(op, args[0]);
+        return prog.add_instruction(op, l0);
     }
 
     instruction_ref

test/CMakeLists.txt

@@ -119,7 +119,7 @@ foreach(ONNX_TEST ${ONNX_TESTS})
     set(TEST_NAME test_${BASE_NAME})
     add_executable(${TEST_NAME} ${TES_ONNX_DIR}/${ONNX_TEST})
     rocm_clang_tidy_check(${TEST_NAME})
-    target_link_libraries(${TEST_NAME} migraphx_onnx)
+    target_link_libraries(${TEST_NAME} migraphx_onnx migraphx_cpu)
     target_include_directories(${TEST_NAME} PUBLIC include)
     add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/onnx) 
     add_dependencies(tests ${TEST_NAME})
@@ -129,7 +129,7 @@ endforeach()
 # tf test
 add_executable(test_tf tf/tf_test.cpp)
 rocm_clang_tidy_check(test_tf)
-target_link_libraries(test_tf migraphx_tf)
+target_link_libraries(test_tf migraphx_tf migraphx_cpu)
 target_include_directories(test_tf PUBLIC include)
 add_test(NAME test_tf COMMAND $<TARGET_FILE:test_tf> WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/tf) 
 add_dependencies(tests test_tf)

test/eliminate_pad_test.cpp

@@ -83,23 +83,4 @@ TEST_CASE(rewrite_test_asymmetric)
         p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
 }
 
-TEST_CASE(rewrite_test_same_padding)
-{
-    migraphx::program p;
-    size_t img_dim[2] = {2, 2};
-    size_t channels   = 1;
-    std::vector<int32_t> input(channels * img_dim[0] * img_dim[1]);
-    std::iota(input.begin(), input.end(), 0);
-
-    migraphx::shape s_img{migraphx::shape::int32_type, {1, channels, img_dim[0], img_dim[1]}};
-    auto l_img      = p.add_literal(migraphx::literal{s_img, input});
-    auto padded_img = p.add_instruction(migraphx::op::pad{{0, 0, 1, 1, 0, 0, 1, 1}}, l_img);
-
-    create_conv(padded_img, channels, p, migraphx::op::padding_mode_t::same);
-
-    p.compile(eliminate_pad_target{});
-    EXPECT(std::any_of(
-        p.begin(), p.end(), [](const migraphx::instruction& ins) { return ins.name() == "pad"; }));
-}
-
 int main(int argc, const char* argv[]) { test::run(argc, argv); }

test/gpu/miopen.cpp

@@ -1460,6 +1460,22 @@ struct test_pad : verify_program<test_pad>
     }
 };
 
+struct test_pad_int8 : verify_program<test_pad_int8>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        std::vector<int8_t> data0 = {0, 1, 2, 3};
+        migraphx::shape s0{migraphx::shape::float_type, {2, 2}};
+        auto l0 = p.add_literal(migraphx::literal{s0, data0});
+        migraphx::op::pad op{};
+        op.value = std::numeric_limits<int8_t>::lowest();
+        op.pads  = {0, 0, 1, 1};
+        p.add_instruction(op, l0);
+        return p;
+    }
+};
+
 struct test_pooling_autopad : verify_program<test_pooling_autopad>
 {
     migraphx::program create_program() const
@@ -2650,10 +2666,11 @@ struct test_lstm_forward_last : verify_program<test_lstm_forward_last>
         auto und  = p.add_instruction(migraphx::op::undefined{});
 
         auto output = p.add_instruction(
-            migraphx::op::gru{hidden_size,
-                              {migraphx::op::sigmoid{}, migraphx::op::tanh{}, migraphx::op::tanh{}},
-                              migraphx::op::rnn_direction::forward,
-                              clip},
+            migraphx::op::lstm{
+                hidden_size,
+                {migraphx::op::sigmoid{}, migraphx::op::tanh{}, migraphx::op::tanh{}},
+                migraphx::op::rnn_direction::forward,
+                clip},
             seq,
             w,
             r,