Merge branch 'develop' into driver

src/include/migraphx/op/logsoftmax.hpp

@@ -30,7 +30,7 @@ struct logsoftmax
     shape compute_shape(std::vector<shape> inputs) const
     {
         check_shapes{inputs}.has(1).standard();
-        if(axis < 0 || axis > inputs[0].lens().size())
+        if(axis < 0 || axis >= inputs[0].lens().size())
         {
             MIGRAPHX_THROW("LogSoftMax: input axis value " + std::to_string(axis) +
                            " is out of range");

src/include/migraphx/op/softmax.hpp

@@ -18,10 +18,23 @@ namespace op {
 
 struct softmax
 {
+    int axis = 1;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.axis, "axis"));
+    }
+
     std::string name() const { return "softmax"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs}.has(1).only_dims(4);
+        check_shapes{inputs}.has(1).standard();
+        if(axis < 0 || axis >= inputs[0].lens().size())
+        {
+            MIGRAPHX_THROW("SoftMax: input axis value " + std::to_string(axis) +
+                           " is out of range");
+        }
         return inputs.at(0);
     }
 };

src/include/migraphx/pad_calc.hpp

+#ifndef MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
+#define MIGRAPHX_GUARD_OPERATORS_PAD_CALC_HPP
+
+#include <utility>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+inline std::size_t calculate_padding(std::size_t weight_dim, std::size_t dilation)
+{
+    return (dilation * (weight_dim - 1)) / 2;
+}
+
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/cpu/lowering.cpp

@@ -517,40 +517,60 @@ struct cpu_unary
     }
 };
 
-struct softmax2d
+struct cpu_softmax
 {
-    std::string name() const { return "cpu::softmax2d"; }
-    shape compute_shape(const std::vector<shape>& inputs) const { return inputs.front(); }
-    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
-    {
-        argument result{output_shape};
-        visit_all(result, args[0])([&](auto output, auto input) {
-            using value_type = typename decltype(input)::value_type;
-            auto nb          = input.get_shape().lens()[0];
-            auto nc          = input.get_shape().lens()[1];
-            auto nh          = input.get_shape().lens()[2];
-            auto nw          = input.get_shape().lens()[3];
-            dfor(nb, nh, nw)([&](std::size_t b, std::size_t i, std::size_t j) {
-                value_type cmax = std::numeric_limits<value_type>::lowest();
-                for(std::size_t c = 0; c < nc; c++)
-                {
-                    cmax = std::max(cmax, input(b, c, i, j));
-                }
-                for(std::size_t c = 0; c < nc; c++)
+    op::softmax op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
     {
-                    output(b, c, i, j) = std::exp(input(b, c, i, j) - cmax);
+        return migraphx::reflect(self.op, f);
     }
-                value_type sum = value_type(0);
-                for(std::size_t c = 0; c < nc; c++)
+
+    std::string name() const { return "cpu::softmax"; }
+    shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
+
+    template <typename T>
+    std::size_t compute_batch_index(T idx, shape& batch_shape, int axis) const
     {
-                    sum += output(b, c, i, j);
+        idx[axis] = 0;
+        return batch_shape.index(idx);
     }
-                for(std::size_t c = 0; c < nc; c++)
+
+    argument compute(context&, const shape& output_shape, std::vector<argument> args) const
     {
-                    output(b, c, i, j) = output(b, c, i, j) / sum;
-                }
+        argument result{output_shape};
+        auto batch_lens     = output_shape.lens();
+        batch_lens[op.axis] = 1;
+        shape batch_shape{shape::int32_type, batch_lens};
+
+        visit_all(result, args[0])([&](auto output, auto input) {
+            using value_type = typename decltype(input)::value_type;
+            std::vector<value_type> batch_max(batch_shape.elements(),
+                                              std::numeric_limits<value_type>::lowest());
+            shape_for_each(output_shape, [&](auto idx) {
+                auto index       = this->compute_batch_index(idx, batch_shape, op.axis);
+                batch_max[index] = std::max(batch_max[index], input(idx.begin(), idx.end()));
+            });
+
+            shape_for_each(output_shape, [&](auto idx) {
+                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
+                output(idx.begin(), idx.end()) =
+                    std::exp(input(idx.begin(), idx.end()) - batch_max[index]);
+            });
+
+            std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
+            shape_for_each(output_shape, [&](auto idx) {
+                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
+                batch_sum[index] += output(idx.begin(), idx.end());
+            });
+
+            shape_for_each(output_shape, [&](auto idx) {
+                auto index = this->compute_batch_index(idx, batch_shape, op.axis);
+                output(idx.begin(), idx.end()) /= batch_sum[index];
             });
         });
+
         return result;
     }
 };
@@ -569,33 +589,19 @@ struct cpu_logsoftmax
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
 
     template <typename T>
-    std::size_t compute_batch_index(const T& idx, shape& batch_shape, int axis) const
+    std::size_t compute_batch_index(T idx, const shape& batch_shape, int axis) const
     {
-        if(axis == 0)
-        {
-            return 0;
-        }
-        else
-        {
-            std::vector<std::size_t> batch_idx(idx.begin(), idx.begin() + axis);
-            return batch_shape.index(batch_idx.begin(), batch_idx.end());
-        }
+        idx[axis] = 0;
+        return batch_shape.index(idx);
     }
 
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
-        auto lens = output_shape.lens();
-        std::vector<std::size_t> batch_lens{};
-        if(op.axis == 0)
-        {
-            batch_lens.push_back(1);
-        }
-        else
-        {
-            batch_lens.insert(batch_lens.begin(), lens.begin(), lens.begin() + op.axis);
-        }
-        shape batch_shape{migraphx::shape::uint32_type, batch_lens};
+        auto batch_lens     = output_shape.lens();
+        batch_lens[op.axis] = 1;
+        shape batch_shape{shape::int32_type, batch_lens};
+
         visit_all(result, args[0])([&](auto output, auto input) {
             using value_type = typename decltype(input)::value_type;
             std::vector<value_type> batch_max(batch_shape.elements(),
@@ -660,7 +666,7 @@ struct cpu_apply
         apply_map["logsoftmax"]  = extend_op<cpu_logsoftmax, op::logsoftmax>();
         apply_map["lrn"]         = extend_op<cpu_lrn, op::lrn>();
         apply_map["pad"]         = extend_op<cpu_pad, op::pad>();
-        apply_map["softmax"]     = simple_op<softmax2d>();
+        apply_map["softmax"]     = extend_op<cpu_softmax, op::softmax>();
     }
 
     void apply()

src/targets/gpu/CMakeLists.txt

@@ -27,6 +27,7 @@ add_library(migraphx_device
     device/add_relu.cpp
     device/contiguous.cpp
     device/logsoftmax.cpp
+    device/softmax.cpp
     device/convert.cpp
     device/mul.cpp
     device/concat.cpp

src/targets/gpu/device/logsoftmax.cpp

@@ -18,48 +18,57 @@ argument logsoftmax(hipStream_t stream,
 {
 
     auto lens         = output_shape.lens();
-    std::size_t batch_size = std::accumulate(
-        lens.begin(), lens.begin() + axis, std::size_t{1}, std::multiplies<std::size_t>());
-    std::size_t n_dims = std::accumulate(
-        lens.begin() + axis, lens.end(), std::size_t{1}, std::multiplies<std::size_t>());
-    migraphx::shape comp_shape{output_shape.type(), {batch_size, n_dims}};
+    auto num_in_batch = lens[axis];
+    auto batch_lens   = lens;
+    batch_lens[axis]  = 1;
+    migraphx::shape batch_shape{output_shape.type(), batch_lens};
 
     visit_all(args.back(), args.front())([&](auto output, auto input) {
         const auto* input_ptr = device_cast(input.data());
         auto* output_ptr      = device_cast(output.data());
+        visit_tensor_size(batch_shape.lens().size(), [&](auto n_dim) {
+            hip_tensor_descriptor<n_dim> desc_batch(batch_shape);
+            hip_tensor_descriptor<n_dim> desc_data(output_shape);
 
             // each thread is for one item in the batch
-        gs_launch(stream, batch_size)([=](auto i) {
-            std::size_t row_start = i * n_dims;
+            gs_launch(stream, batch_shape.elements())([=](auto i) {
+                auto batch_idx = desc_batch.multi(i);
+                auto data_idx  = batch_idx;
+
                 // get max
-            auto batch_max = input_ptr[row_start];
-            for(std::size_t j = 1; j < n_dims; ++j)
+                auto batch_max = input_ptr[desc_data.linear(batch_idx)];
+                for(std::size_t j = 1; j < num_in_batch; ++j)
                 {
-                auto ind  = row_start + j;
-                batch_max = std::max(to_hip_type(batch_max), to_hip_type(input_ptr[ind]));
+                    data_idx[axis] = j;
+                    size_t idx     = desc_data.linear(data_idx);
+                    batch_max      = std::max(to_hip_type(batch_max), to_hip_type(input_ptr[idx]));
                 }
 
-            for(std::size_t j = 0; j < n_dims; ++j)
+                for(std::size_t j = 0; j < num_in_batch; ++j)
                 {
-                auto ind        = row_start + j;
-                output_ptr[ind] = input_ptr[ind] - batch_max;
+                    data_idx[axis]  = j;
+                    size_t idx      = desc_data.linear(data_idx);
+                    output_ptr[idx] = input_ptr[idx] - batch_max;
                 }
 
-            auto batch_sum = ::exp(to_hip_type(output_ptr[row_start]));
-            for(std::size_t j = 1; j < n_dims; ++j)
+                auto batch_sum = ::exp(to_hip_type(output_ptr[desc_data.linear(batch_idx)]));
+                for(std::size_t j = 1; j < num_in_batch; ++j)
                 {
-                auto ind = row_start + j;
-                batch_sum += ::exp(to_hip_type(output_ptr[ind]));
+                    data_idx[axis] = j;
+                    size_t idx     = desc_data.linear(data_idx);
+                    batch_sum += ::exp(to_hip_type(output_ptr[idx]));
                 }
                 batch_sum = ::log(to_hip_type(batch_sum));
 
-            for(std::size_t j = 0; j < n_dims; ++j)
+                for(std::size_t j = 0; j < num_in_batch; ++j)
                 {
-                auto ind = row_start + j;
-                output_ptr[ind] -= batch_sum;
+                    data_idx[axis] = j;
+                    size_t idx     = desc_data.linear(data_idx);
+                    output_ptr[idx] -= batch_sum;
                 }
             });
         });
+    });
 
     return args.back();
 }

src/targets/gpu/device/softmax.cpp

+#include <migraphx/shape.hpp>
+#include <migraphx/argument.hpp>
+#include <migraphx/dfor.hpp>
+#include <migraphx/gpu/device/softmax.hpp>
+#include <migraphx/gpu/device/tensor.hpp>
+#include <migraphx/gpu/device/launch.hpp>
+#include <migraphx/gpu/device/types.hpp>
+#include <migraphx/gpu/hip.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+argument softmax(hipStream_t stream,
+                 const migraphx::shape& output_shape,
+                 std::vector<migraphx::argument> args,
+                 int axis)
+{
+    auto lens        = output_shape.lens();
+    auto batch_lens  = lens;
+    size_t n_dims    = lens[axis];
+    batch_lens[axis] = 1;
+    migraphx::shape batch_shape{shape::int32_type, batch_lens};
+
+    visit_all(args.back(), args.front())([&](auto output, auto input) {
+        const auto* input_ptr = device_cast(input.data());
+        auto* output_ptr      = device_cast(output.data());
+        visit_tensor_size(batch_shape.lens().size(), [&](auto n_dim) {
+            hip_tensor_descriptor<n_dim> desc_batch(batch_shape);
+            hip_tensor_descriptor<n_dim> desc_data(output_shape);
+
+            // each thread is for one item in the batch
+            gs_launch(stream, batch_shape.elements())([=](auto i) {
+                auto batch_idx = desc_batch.multi(i);
+                auto data_idx  = batch_idx;
+                // get max
+                auto batch_max = input_ptr[desc_data.linear(batch_idx)];
+                for(std::size_t j = 1; j < n_dims; ++j)
+                {
+                    data_idx[axis] = j;
+                    batch_max      = std::max(to_hip_type(batch_max),
+                                         to_hip_type(input_ptr[desc_data.linear(data_idx)]));
+                }
+
+                for(std::size_t j = 0; j < n_dims; ++j)
+                {
+                    data_idx[axis]  = j;
+                    auto idx        = desc_data.linear(data_idx);
+                    output_ptr[idx] = input_ptr[idx] - batch_max;
+                }
+
+                for(std::size_t j = 0; j < n_dims; ++j)
+                {
+                    data_idx[axis]  = j;
+                    auto idx        = desc_data.linear(data_idx);
+                    output_ptr[idx] = exp(to_hip_type(output_ptr[idx]));
+                }
+
+                auto batch_sum = output_ptr[desc_data.linear(batch_idx)];
+                for(std::size_t j = 1; j < n_dims; ++j)
+                {
+                    data_idx[axis] = j;
+                    batch_sum += output_ptr[desc_data.linear(data_idx)];
+                }
+
+                for(std::size_t j = 0; j < n_dims; ++j)
+                {
+                    data_idx[axis]  = j;
+                    auto idx        = desc_data.linear(data_idx);
+                    output_ptr[idx] = output_ptr[idx] / batch_sum;
+                }
+            });
+        });
+    });
+
+    return args.back();
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

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

+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_SOFTMAX_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_SOFTMAX_HPP
+
+#include <migraphx/argument.hpp>
+#include <migraphx/config.hpp>
+#include <hip/hip_runtime_api.h>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+argument softmax(hipStream_t stream,
+                 const migraphx::shape& output_shape,
+                 std::vector<migraphx::argument> args,
+                 int axis);
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

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

 #ifndef MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 #define MIGRAPHX_GUARD_RTGLIB_SOFTMAX_HPP
 
-#include <migraphx/shape.hpp>
+#include <migraphx/gpu/lowering.hpp>
+#include <migraphx/manage_ptr.hpp>
+#include <migraphx/instruction.hpp>
 #include <migraphx/op/softmax.hpp>
+#include <migraphx/generate.hpp>
+#include <migraphx/shape_for_each.hpp>
+#include <migraphx/config.hpp>
+#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/hip.hpp>
+#include <migraphx/dfor.hpp>
+#include <migraphx/gpu/device/contiguous.hpp>
+#include <migraphx/gpu/device/add.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <migraphx/gpu/rocblas.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -30,6 +44,26 @@ struct miopen_softmax
     }
 };
 
+struct hip_softmax
+{
+    op::softmax op;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return migraphx::reflect(self.op, f);
+    }
+
+    std::string name() const { return "gpu::softmax"; }
+    shape compute_shape(const std::vector<shape>& inputs) const;
+    argument
+    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
+    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+    {
+        return shapes.size() - 1;
+    }
+};
+
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/targets/gpu/lowering.cpp

@@ -99,7 +99,7 @@ struct miopen_apply
         add_extend_op<miopen_gemm, op::dot>("dot");
         add_extend_op<miopen_contiguous, op::contiguous>("contiguous");
         add_extend_op<hip_concat, op::concat>("concat");
-        add_extend_op<miopen_softmax, op::softmax>("softmax");
+        add_extend_op<hip_softmax, op::softmax>("softmax");
         add_extend_op<hip_logsoftmax, op::logsoftmax>("logsoftmax");
         add_extend_op<hip_gather, op::gather>("gather");
         add_extend_op<hip_pad, op::pad>("pad");

src/targets/gpu/softmax.cpp

 #include <migraphx/gpu/softmax.hpp>
+#include <migraphx/gpu/device/softmax.hpp>
 #include <migraphx/gpu/context.hpp>
 
 namespace migraphx {
@@ -30,6 +31,19 @@ argument miopen_softmax::compute(context& ctx,
     return args[1];
 }
 
+shape hip_softmax::compute_shape(const std::vector<shape>& inputs) const
+{
+    check_shapes{inputs, *this}.has(2).standard();
+    return op.compute_shape({inputs.at(0)});
+}
+
+argument hip_softmax::compute(context& ctx,
+                              const shape& output_shape,
+                              const std::vector<argument>& args) const
+{
+    return device::softmax(ctx.get_stream().get(), output_shape, args, op.axis);
+}
+
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/tf/tf.cpp

@@ -17,6 +17,7 @@
 #include <migraphx/instruction.hpp>
 #include <migraphx/config.hpp>
 #include <migraphx/tf.hpp>
+#include <migraphx/pad_calc.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -24,7 +25,7 @@ inline namespace MIGRAPHX_INLINE_NS {
 struct tf_parser
 {
     using attribute_map = std::unordered_map<std::string, tensorflow::AttrValue>;
-    using node_map      = std::unordered_map<std::string, tensorflow::NodeDef>;
+    using node_map      = std::map<std::string, tensorflow::NodeDef>;
     // using input_node_map = std::unordered_map<std::string, std::unordered_set<std::string>>;
     using op_func = std::function<instruction_ref(attribute_map, std::vector<instruction_ref>)>;
 
@@ -277,29 +278,6 @@ struct tf_parser
     parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
         op::convolution op;
-        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;
-            }
-            else if(pad_mode.find("EXPLICIT") != std::string::npos)
-            {
-                std::vector<size_t> padding;
-                copy(attributes.at("explicit_paddings").list().i(), std::back_inserter(padding));
-                if(padding.size() != 4)
-                {
-                    MIGRAPHX_THROW("padding should have 4 values");
-                }
-                if(padding[0] != padding[2] || padding[1] != padding[3])
-                {
-                    MIGRAPHX_THROW("migraphx does not support asymetric padding");
-                }
-                op.padding[0] = padding[0];
-                op.padding[1] = padding[1];
-            }
-        }
         if(contains(attributes, "strides"))
         {
             std::vector<size_t> stride;
@@ -339,6 +317,39 @@ struct tf_parser
             }
         }
 
+        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;
+                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]);
+            }
+            else if(pad_mode.find("VALID") != std::string::npos)
+            {
+                op.padding_mode = op::padding_mode_t::valid;
+            }
+            else if(pad_mode.find("EXPLICIT") != std::string::npos)
+            {
+                std::vector<size_t> padding;
+                copy(attributes.at("explicit_paddings").list().i(), std::back_inserter(padding));
+                if(padding.size() != 4)
+                {
+                    MIGRAPHX_THROW("padding should have 4 values");
+                }
+                if(padding[0] != padding[2] || padding[1] != padding[3])
+                {
+                    MIGRAPHX_THROW("migraphx does not support asymetric padding");
+                }
+                op.padding[0] = padding[0];
+                op.padding[1] = padding[1];
+            }
+        }
+
         return prog.add_instruction(op, {args[0], weights});
     }
 
@@ -349,14 +360,7 @@ struct tf_parser
         op::convolution op;
         size_t num_channels = args[0]->get_shape().lens()[1];
         op.group            = num_channels;
-        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;
-            }
-        }
+
         if(contains(attributes, "strides"))
         {
             std::vector<size_t> stride;
@@ -369,6 +373,19 @@ struct tf_parser
             op.stride[0] = stride[2];
             op.stride[1] = stride[3];
         }
+        if(contains(attributes, "dilations"))
+        {
+            std::vector<size_t> dilation;
+            copy(attributes.at("dilations").list().i(), std::back_inserter(dilation));
+            reorder_data(dilation);
+            if(dilation.size() != 4)
+            {
+                MIGRAPHX_THROW("dilation should have 4 values");
+            }
+            op.dilation[0] = dilation[2];
+            op.dilation[1] = dilation[3];
+        }
+
         auto weights = args[1];
         // check if weights are from a constant
         if(weights->name() != "@param")
@@ -383,6 +400,24 @@ struct tf_parser
             }
         }
 
+        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];
+            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]);
+            }
+            else if(pad_mode.find("VALID") != std::string::npos)
+            {
+                op.padding_mode = op::padding_mode_t::valid;
+            }
+        }
+
         std::vector<int64_t> new_weights_shape;
         copy(weights->get_shape().lens(), std::back_inserter(new_weights_shape));
 
@@ -508,18 +543,6 @@ struct tf_parser
     {
         op::pooling op{starts_with(name, "Max") ? "max" : "average"};
 
-        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;
-            }
-            else if(pad_mode.find("VALID") != std::string::npos)
-            {
-                op.padding_mode = op::padding_mode_t::valid;
-            }
-        }
         if(contains(attributes, "strides"))
         {
             std::vector<size_t> stride;
@@ -544,6 +567,20 @@ struct tf_parser
             op.lengths[0] = ksize[2];
             op.lengths[1] = ksize[3];
         }
+        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);
+            }
+            else if(pad_mode.find("VALID") != std::string::npos)
+            {
+                op.padding_mode = op::padding_mode_t::valid;
+            }
+        }
         return prog.add_instruction(op, args[0]);
     }
 

test/cpu_ops_test.cpp

@@ -929,6 +929,24 @@ TEST_CASE(maxpool_test)
     EXPECT(migraphx::verify_range(results_vector, c));
 }
 
+TEST_CASE(softmax_simple_test)
+{
+    migraphx::program p;
+    std::vector<float> a = {0.25, 0.75};
+    std::vector<float> s = {0.377541, 0.622459};
+    migraphx::shape a_shape{migraphx::shape::float_type, {1, 2}};
+    auto al = p.add_literal(migraphx::literal{a_shape, a});
+    p.add_instruction(migraphx::op::softmax{1}, al);
+    p.compile(migraphx::cpu::target{});
+    auto result = p.eval({});
+    std::vector<float> results_vector(2);
+    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
+    for(auto v : results_vector)
+        std::cout << v << "\t";
+    std::cout << std::endl;
+    EXPECT(migraphx::verify_range(results_vector, s));
+}
+
 TEST_CASE(softmax_test)
 {
     migraphx::program p;
@@ -1002,14 +1020,13 @@ TEST_CASE(logsoftmax_test_axis_0)
         -0.99628491, 1.04314606,  -1.22943315, 0.76930403,  0.31106618};
 
     std::vector<float> s = {
-        -2.71138556, -5.85030702, -3.74063578, -4.22915517, -6.15821977, -5.96072346, -3.57208097,
-        -5.78313166, -5.51435497, -3.67224195, -3.88393048, -2.57061599, -5.54431083, -6.27880025,
-        -5.1878749,  -6.1318955,  -5.29178545, -4.22537886, -3.75693516, -7.07047099, -4.45763333,
-        -4.66281846, -6.18290503, -4.11886536, -6.17408292, -4.18030052, -4.64570814, -4.64354473,
-        -3.06629525, -3.80807681, -4.69162374, -5.53605222, -3.20969275, -4.82645674, -6.63942356,
-        -4.73634471, -3.86003866, -5.32738981, -4.22249802, -4.51258693, -2.41455206, -3.48343199,
-        -5.86215889, -4.93435935, -4.83713408, -2.97471885, -2.16666459, -3.69133151, -4.71640968,
-        -5.64652924, -3.60709827, -5.87967748, -3.8809403,  -4.33917815};
+        -0.135261, -2.843968, -0.659995, -0.488413, -1.051857, -2.812936, -0.250956, -0.353985,
+        -1.155980, -0.603651, -0.211969, -0.175371, -1.336552, -3.885010, -1.871544, -0.837083,
+        -0.887745, -0.433338, -1.158864, -4.911197, -1.147972, -0.666711, -0.996874, -0.981418,
+        -0.851145, -0.853988, -0.858112, -2.067420, -0.059956, -0.727436, -0.950881, -0.429689,
+        -0.061906, -1.505332, -1.210277, -0.377970, -0.791448, -1.655428, -1.827253, -0.304828,
+        -0.020762, -0.167101, -0.567346, -0.530319, -1.045094, -0.376648, -0.007391, -0.381670,
+        -0.720302, -0.460499, -0.469651, -0.556740, -0.554628, -0.551582};
 
     migraphx::shape a_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
     auto al  = p.add_literal(migraphx::literal{a_shape, a});
@@ -1036,14 +1053,13 @@ TEST_CASE(logsoftmax_test_axis_1)
         -0.99628491, 1.04314606,  -1.22943315, 0.76930403,  0.31106618};
 
     std::vector<float> s = {
-        -1.77931988, -4.91824134, -2.80857010, -3.29708949, -5.22615409, -5.02865778, -2.64001529,
-        -4.85106598, -4.58228929, -2.74017627, -2.95186480, -1.63855031, -4.61224515, -5.34673457,
-        -4.25580922, -5.19982982, -4.35971977, -3.29331318, -2.82486948, -6.13840531, -3.52556765,
-        -3.73075278, -5.25083935, -3.18679968, -5.24201724, -3.24823484, -3.71364246, -4.14309917,
-        -2.56584969, -3.30763125, -4.19117818, -5.03560666, -2.70924719, -4.32601118, -6.13897800,
-        -4.23589915, -3.35959310, -4.82694425, -3.72205246, -4.01214137, -1.91410650, -2.98298643,
-        -5.36171333, -4.43391379, -4.33668852, -2.47427329, -1.66621903, -3.19088595, -4.21596412,
-        -5.14608368, -3.10665271, -5.37923192, -3.38049474, -3.83873259};
+        -0.550468, -2.132973, -1.549746, -0.650533, -1.051529, -2.248570, -0.141017, -2.028357,
+        -1.947730, -1.511324, -0.166597, -0.379726, -1.965689, -1.172109, -1.475721, -2.700831,
+        -1.537011, -0.658754, -1.596017, -3.353137, -2.266743, -1.084197, -1.076214, -0.406712,
+        -2.743019, -0.425526, -1.079083, -2.139486, -1.270584, -1.024088, -1.154231, -3.201762,
+        -0.888957, -0.532855, -3.103583, -1.221339, -1.355980, -3.531678, -1.438510, -0.975194,
+        -0.080261, -1.162697, -1.568557, -1.398519, -1.322129, -0.470660, -0.370953, -0.907343,
+        -1.179017, -3.312239, -1.286363, -1.586076, -0.345100, -0.824173};
 
     migraphx::shape a_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
     auto al  = p.add_literal(migraphx::literal{a_shape, a});
@@ -1070,14 +1086,13 @@ TEST_CASE(logsoftmax_test_axis_2)
         -0.99628491, 1.04314606,  -1.22943315, 0.76930403,  0.31106618};
 
     std::vector<float> s = {
-        -0.79763715, -3.93655861, -1.82688737, -2.31540676, -4.24447136, -4.04697505, -1.65833256,
-        -3.86938325, -3.60060656, -1.81223672, -2.02392525, -0.71061076, -3.68430560, -4.41879502,
-        -3.32786967, -4.27189027, -3.43178022, -2.36537363, -1.35498658, -4.66852241, -2.05568475,
-        -2.26086988, -3.78095645, -1.71691678, -3.77213434, -1.77835194, -2.24375956, -2.74631770,
-        -1.16906822, -1.91084978, -2.79439671, -3.63882519, -1.31246572, -2.92922971, -4.74219653,
-        -2.83911768, -2.19738500, -3.66473615, -2.55984436, -2.84993327, -0.75189840, -1.82077833,
-        -4.19950523, -3.27170569, -3.17448042, -1.65286841, -0.84481415, -2.36948107, -3.39455924,
-        -4.32467880, -2.28524783, -4.55782704, -2.55908986, -3.01732771};
+        -0.495957, -1.031212, -0.245531, -2.013726, -1.339125, -2.465619, -1.356652, -0.964037,
+        -2.019250, -0.214522, -0.289569, -0.234392, -2.086591, -2.684439, -2.851651, -2.674176,
+        -1.697424, -1.889155, -0.401029, -3.064586, -1.173030, -1.306912, -2.177020, -0.834262,
+        -2.818177, -0.174415, -1.361105, -1.024571, -0.106766, -1.167645, -1.072650, -2.576522,
+        -0.569261, -1.207483, -3.679894, -2.095913, -0.504264, -3.039291, -1.290559, -1.156812,
+        -0.126453, -0.551493, -2.506384, -2.646261, -1.905195, -0.206994, -0.191369, -0.959754,
+        -1.948685, -3.671233, -0.875521, -3.111952, -1.905644, -1.6076011};
 
     migraphx::shape a_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
     auto al  = p.add_literal(migraphx::literal{a_shape, a});
@@ -1104,14 +1119,13 @@ TEST_CASE(logsoftmax_test_axis_3)
         -0.99628491, 1.04314606,  -1.22943315, 0.76930403,  0.31106618};
 
     std::vector<float> s = {
-        -0.33690375, -3.47582521, -1.36615397, -0.27936556, -2.20843016, -2.01093385, -0.22551114,
-        -2.43656183, -2.16778514, -1.57241522, -1.78410375, -0.47078926, -1.06745881, -1.80194823,
-        -0.71102288, -2.30719726, -1.46708721, -0.40068062, -0.42698261, -3.74051844, -1.12768078,
-        -1.07891856, -2.59900513, -0.53496546, -2.56139951, -0.56761711, -1.03302473, -2.09771276,
-        -0.52046328, -1.26224484, -1.76322959, -2.60765807, -0.28129860, -0.81424303, -2.62720985,
-        -0.72413100, -0.65570381, -2.12305496, -1.01816317, -2.48063402, -0.38259915, -1.45147908,
-        -1.84310238, -0.91530284, -0.81807757, -1.31692881, -0.50887455, -2.03354147, -1.48767160,
-        -2.41779116, -0.37836019, -2.56853147, -0.56979429, -1.02803214};
+        -0.336904, -3.475825, -1.366154, -0.279366, -2.208430, -2.010934, -0.225511, -2.436562,
+        -2.167785, -1.572415, -1.784104, -0.470789, -1.067459, -1.801948, -0.711023, -2.307197,
+        -1.467087, -0.400681, -0.426983, -3.740518, -1.127681, -1.078919, -2.599005, -0.534965,
+        -2.561400, -0.567617, -1.033025, -2.097713, -0.520463, -1.262245, -1.763230, -2.607658,
+        -0.281299, -0.814243, -2.627210, -0.724131, -0.655704, -2.123055, -1.018163, -2.480634,
+        -0.382599, -1.451479, -1.843102, -0.915303, -0.818078, -1.316929, -0.508875, -2.033541,
+        -1.487672, -2.417791, -0.378360, -2.568531, -0.569794, -1.028032};
 
     migraphx::shape a_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
     auto al  = p.add_literal(migraphx::literal{a_shape, a});
@@ -1124,40 +1138,6 @@ TEST_CASE(logsoftmax_test_axis_3)
     EXPECT(migraphx::verify_range(results_vector, s));
 }
 
-TEST_CASE(logsoftmax_test_axis_4)
-{
-    migraphx::program p;
-    std::vector<float> a = {
-        1.93885877,  -1.20006269, 0.90960855,  0.42108916,  -1.50797544, -1.31047913, 1.07816336,
-        -1.13288733, -0.86411064, 0.97800238,  0.76631385,  2.07962834,  -0.8940665,  -1.62855592,
-        -0.53763057, -1.48165117, -0.64154112, 0.42486547,  0.89330917,  -2.42022666, 0.192611,
-        -0.01257413, -1.5326607,  0.53137897,  -1.52383859, 0.46994381,  0.00453619,  0.0066996,
-        1.58394908,  0.84216752,  -0.04137941, -0.88580789, 1.44055158,  -0.17621241, -1.98917923,
-        -0.08610038, 0.79020567,  -0.67714548, 0.42774631,  0.1376574,   2.23569227,  1.16681234,
-        -1.21191456, -0.28411502, -0.18688975, 1.67552548,  2.48357974,  0.95891282,  -0.06616535,
-        -0.99628491, 1.04314606,  -1.22943315, 0.76930403,  0.31106618};
-
-    std::vector<float> s = {0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000,
-                            0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000};
-
-    migraphx::shape a_shape{migraphx::shape::float_type, {2, 3, 3, 3}};
-    auto al  = p.add_literal(migraphx::literal{a_shape, a});
-    int axis = 4;
-    p.add_instruction(migraphx::op::logsoftmax{axis}, al);
-    p.compile(migraphx::cpu::target{});
-    auto result = p.eval({});
-    std::vector<float> results_vector;
-    result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
-    EXPECT(migraphx::verify_range(results_vector, s));
-}
-
 TEST_CASE(conv2d_test)
 {
     migraphx::program p;

test/gpu/miopen.cpp

@@ -569,13 +569,13 @@ struct test_sub2 : verify_program<test_sub2>
     }
 };
 
-struct test_softmax : verify_program<test_softmax>
+struct test_softmax1 : verify_program<test_softmax1>
 {
     migraphx::program create_program() const
     {
         migraphx::program p;
-        auto x = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {5, 3, 4, 2}});
-        p.add_instruction(migraphx::op::softmax{}, x);
+        auto x = p.add_parameter("x", migraphx::shape{migraphx::shape::float_type, {5, 3, 3, 4}});
+        p.add_instruction(migraphx::op::softmax{0}, x);
         return p;
     }
 };
@@ -592,6 +592,25 @@ struct test_softmax2 : verify_program<test_softmax2>
     }
 };
 
+template <int Axis>
+struct test_softmax : verify_program<test_softmax<Axis>>
+{
+    migraphx::program create_program() const
+    {
+        migraphx::program p;
+        migraphx::shape s{migraphx::shape::float_type, {3, 4, 5, 6}};
+        auto param = p.add_parameter("0", s);
+        p.add_instruction(migraphx::op::softmax{Axis}, param);
+
+        return p;
+    }
+};
+
+template struct test_softmax<0>;
+template struct test_softmax<1>;
+template struct test_softmax<2>;
+template struct test_softmax<3>;
+
 struct test_conv : verify_program<test_conv>
 {
     migraphx::program create_program() const
@@ -3326,7 +3345,6 @@ template struct test_logsoftmax<0>;
 template struct test_logsoftmax<1>;
 template struct test_logsoftmax<2>;
 template struct test_logsoftmax<3>;
-template struct test_logsoftmax<4>;
 
 template <int Axis>
 struct test_logsoftmax_1 : verify_program<test_logsoftmax_1<Axis>>
@@ -3343,7 +3361,6 @@ struct test_logsoftmax_1 : verify_program<test_logsoftmax_1<Axis>>
 };
 
 template struct test_logsoftmax_1<0>;
-template struct test_logsoftmax_1<1>;
 
 struct test_fp32_fp16_lall : verify_program<test_fp32_fp16_lall>
 {

test/op_shape_test.cpp

@@ -346,60 +346,39 @@ TEST_CASE(gather)
     }
 }
 
-TEST_CASE(logsoftmax)
+template <class T>
+void test_softmax_variations()
 {
     {
         migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = 0;
-        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}},
-                     migraphx::op::logsoftmax{axis},
-                     input);
+        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}}, T{0}, input);
     }
 
     {
         migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = 1;
-        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}},
-                     migraphx::op::logsoftmax{axis},
-                     input);
+        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}}, T{1}, input);
     }
 
     {
         migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = 2;
-        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}},
-                     migraphx::op::logsoftmax{axis},
-                     input);
+        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}}, T{2}, input);
     }
 
     {
         migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = 3;
-        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}},
-                     migraphx::op::logsoftmax{axis},
-                     input);
+        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}}, T{3}, input);
     }
 
     {
         migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
         int axis = 4;
-        expect_shape(migraphx::shape{migraphx::shape::float_type, {2, 3, 4, 5}},
-                     migraphx::op::logsoftmax{axis},
-                     input);
+        throws_shape(T{axis}, input);
     }
+}
 
-    {
-        migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = 5;
-        throws_shape(migraphx::op::logsoftmax{axis}, input);
-    }
+TEST_CASE(softmax) { test_softmax_variations<migraphx::op::softmax>(); }
 
-    {
-        migraphx::shape input{migraphx::shape::float_type, {2, 3, 4, 5}};
-        int axis = -1;
-        throws_shape(migraphx::op::logsoftmax{axis}, input);
-    }
-}
+TEST_CASE(logsoftmax) { test_softmax_variations<migraphx::op::logsoftmax>(); }
 
 // 2 inputs arguments
 TEST_CASE(matmul)

test/tf/tf_test.cpp

@@ -109,6 +109,7 @@ TEST_CASE(conv_test)
 
     migraphx::op::convolution op;
     op.padding_mode = migraphx::op::padding_mode_t::same;
+    op.padding      = {1, 1};
     op.stride       = {1, 1};
     op.dilation     = {1, 1};
     auto l2         = p.add_instruction(migraphx::op::transpose{{0, 3, 1, 2}}, l1);
@@ -131,6 +132,7 @@ TEST_CASE(depthwiseconv_test)
 
     migraphx::op::convolution op;
     op.padding_mode = migraphx::op::padding_mode_t::same;
+    op.padding      = {1, 1};
     op.stride       = {1, 1};
     op.dilation     = {1, 1};
     op.group        = 3;
@@ -178,9 +180,9 @@ TEST_CASE(mean_test)
     p.add_literal(l);
     migraphx::op::pooling op;
     op.lengths = {16, 16};
+    p.add_instruction(op, l0);
     auto l3 = p.add_instruction(op, l0);
     p.add_instruction(migraphx::op::squeeze{{2, 3}}, l3);
-    p.add_instruction(op, l0);
     auto prog = migraphx::parse_tf("mean_test.pb", false);
 
     EXPECT(p == prog);
@@ -195,9 +197,9 @@ TEST_CASE(mean_test_nhwc)
     p.add_literal(l);
     migraphx::op::pooling op;
     op.lengths = {16, 16};
+    p.add_instruction(op, l0);
     auto l3 = p.add_instruction(op, l0);
     p.add_instruction(migraphx::op::squeeze{{2, 3}}, l3);
-    p.add_instruction(op, l0);
     auto prog = migraphx::parse_tf("mean_test_nhwc.pb", true);
 
     EXPECT(p == prog);
@@ -271,8 +273,8 @@ TEST_CASE(pooling_test)
     max_pool_op.stride       = {2, 2};
     avg_pool_op.lengths      = {2, 2};
     max_pool_op.lengths      = {2, 2};
-    p.add_instruction(max_pool_op, l0);
     p.add_instruction(avg_pool_op, l0);
+    p.add_instruction(max_pool_op, l0);
     auto prog = migraphx::parse_tf("pooling_test.pb", true);
 
     EXPECT(p == prog);