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

src/include/migraphx/op/convolution_backwards.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -164,7 +164,7 @@ struct convolution_backwards
             shape win_shape{dyn_out.computed_shape.type(), win_size};
 
             par_dfor(in_n, wei_c)([&](int o, int k) {
-                shape_for_each(win_shape, [&](auto idx_win) {
+                shape_for_each(win_shape, [&](const auto& idx_win) {
                     const int w = idx_win[0];
 
                     auto input_dims_start = idx_win.begin() + 1;

src/include/migraphx/op/fill.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#ifndef MIGRAPHX_GUARD_OPERATORS_FILL_HPP
+#define MIGRAPHX_GUARD_OPERATORS_FILL_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
+#include <migraphx/par_for.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+/**
+ * fill(default_value, output_buffer)
+ * Fill an output buffer with the given default_value.
+ * Note that if the default_value is a literal and the output_buffer
+ * has a static shape this operator can be replaced with a literal.
+ */
+struct fill
+{
+    std::string name() const { return "fill"; }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this, true}.has(2).same_type();
+        if(inputs.at(0).dynamic() or inputs.at(0).elements() != 1)
+        {
+            MIGRAPHX_THROW("FILL: default_value is dynamic or more than one element");
+        }
+        return inputs.back();
+    }
+
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
+    {
+        visit_all(args[0], args[1])([&](auto value, auto output) {
+            par_for(dyn_out.computed_shape.elements(), [&](auto i) { output[i] = value.front(); });
+        });
+        return args[1];
+    }
+
+    std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 1; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/gather.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -125,13 +125,12 @@ struct gather
                     auto out_lens  = data.get_shape().lens();
                     out_lens[axis] = indices.get_shape().elements();
                     migraphx::shape out_comp_shape{data.get_shape().type(), out_lens};
-                    shape_for_each(out_comp_shape, [&](const auto& out_idx) {
-                        auto data_idx  = out_idx;
+                    shape_for_each(out_comp_shape, [&](const auto& out_idx_v, size_t out_idx) {
+                        auto data_idx   = out_idx_v;
                         auto in_index   = indices[data_idx[axis]];
                         in_index        = (in_index < 0) ? in_index + axis_dim_size : in_index;
                         data_idx[axis]  = in_index;
-                        output[out_comp_shape.index(out_idx.begin(), out_idx.end())] =
-                            data(data_idx.begin(), data_idx.end());
+                        output[out_idx] = data(data_idx.begin(), data_idx.end());
                     });
                 }
             });

src/include/migraphx/op/if_op.hpp

@@ -71,7 +71,7 @@ struct if_op
         std::unordered_map<std::string, argument> params;
 
         std::set<std::string> pnames;
-        for(const auto& smod : mods)
+        for(const_module_ref smod : mods)
         {
             auto names = smod->get_parameter_names();
             pnames.insert(names.begin(), names.end());

src/include/migraphx/op/loop.hpp

@@ -59,7 +59,7 @@ struct loop
             MIGRAPHX_THROW("LOOP: operator should have one submodule.");
         }
 
-        const auto& mod     = mods.front();
+        const_module_ref mod = mods.front();
         auto mod_out_shapes  = mod->get_output_shapes();
         auto dep_param_num   = inputs.size() - 2;
 

src/include/migraphx/op/nonmaxsuppression.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -258,7 +258,7 @@ struct nonmaxsuppression
         selected_boxes_inside_class.reserve(max_output_shape.elements());
         // iterate over batches and classes
         shape comp_s{shape::double_type, {num_batches, num_classes}};
-        shape_for_each(comp_s, [&](auto idx) {
+        shape_for_each(comp_s, [&](const auto& idx) {
             auto batch_idx = idx[0];
             auto class_idx = idx[1];
             // index offset for this class

src/include/migraphx/op/nonzero.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -56,10 +56,10 @@ struct nonzero
         std::vector<std::vector<std::size_t>> vec_idx;
         auto s = args.front().get_shape();
         args.front().visit([&](auto v) {
-            shape_for_each(s, [&](auto idx) {
-                if(not float_equal(v[s.index(idx)], 0))
+            shape_for_each(s, [&](const auto& idx_v, size_t idx) {
+                if(not float_equal(v[idx], 0))
                 {
-                    vec_idx.push_back(idx);
+                    vec_idx.push_back(idx_v);
                 }
             });
         });

src/include/migraphx/op/pooling.hpp

@@ -29,6 +29,7 @@
 #include <migraphx/config.hpp>
 #include <migraphx/value.hpp>
 #include <migraphx/argument.hpp>
+#include <migraphx/pad_calc.hpp>
 #include <migraphx/par_for.hpp>
 #include <migraphx/shape_for_each.hpp>
 #include <migraphx/dyn_output.hpp>
@@ -40,10 +41,20 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+// The Pooling operator mostly follows the specifications for the Onnx pooling op.
+// It assumes an NCHW layout, extended to support any number of spatial dimensions
+// from 1 on up; dimensions are <batch index, channels, spatial dimensions...>
+//
 struct pooling
 {
+    //  Class members mode, ceil_mode, padding_mode have similar names but refer to separate
+    //  concepts.
     pooling_mode mode = {pooling_mode::average};
 
+    // If the input has rank other than 4 then padding, stride, lengths must all be specified
+    // since the defaults have 2-dimensions.  Exception: padding not required if
+    // padding_mode != default_
+
     // Padding along each spatial input dimension
     // Can be ndim or 2*ndim values where ndim is size of lengths
     // ndim values means pad the same before and after each dimension
@@ -63,13 +74,14 @@ struct pooling
 
     // ceiling mode is a flag affecting output size
     // or equivalently, placements of the pooling kernel.
-    // When true, round the size upwards, possibly
-    // including partial placements where the kernel extends beyond the edge
-    // of input and even padding.  When false, round down so that all
+    // When true, round the size upwards.  When false, round down so that all
     // kernel placements fit but some input values may be dropped.
     bool ceil_mode = false;
     int lp_order   = 2;
 
+    // Mode for auto padding.  default_ indicates no auto padding.
+    padding_mode_t padding_mode = padding_mode_t::default_;
+
     // Global pooling with dynamic shape input
     bool dyn_global = false;
 
@@ -84,6 +96,7 @@ struct pooling
     {
         return pack(f(self.mode, "mode"),
                     f(self.padding, "padding"),
+                    f(self.padding_mode, "padding_mode"),
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"),
                     f(self.ceil_mode, "ceil_mode"),
@@ -97,7 +110,8 @@ struct pooling
     {
         if(dyn_global)
             return;
-        if((padding.size() != stride.size() and (padding.size()) != stride.size() * 2) or
+        if((padding_mode != default_ and padding.size() != stride.size() and
+            (padding.size()) != stride.size() * 2) or
            stride.size() != lengths.size())
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
@@ -137,8 +151,19 @@ struct pooling
             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 dim_size;
+            if(input_lens[i + 2] + padding_factor < lengths[i])
+            {
+                if(padding_mode == default_)
+                    MIGRAPHX_THROW("POOLING: not enough padding for the given kernel size");
+                // lengths can be legitimately larger only if we're doing auto padding
+                // with a dynamic shape, in which case given padding is ignored.  Set a dummy value.
+                dim_size = 2;
+            }
+            else
+            {
+                dim_size = input_lens[i + 2] + padding_factor - lengths[i];
+            }
             std::size_t len =
                 (ceil_mode)
                     ? dim_size / stride[i] +
@@ -151,17 +176,13 @@ struct pooling
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this, true}.has(1);
+        check_shapes{inputs, *this, true}.has(1).min_ndims(3);
         check_attribute_size();
 
         const shape& input = inputs.at(0);
-        auto padding_size  = padding.size();
+        auto stride_size   = stride.size();
         size_t kdims       = input.ndim() - 2;
-        if(input.ndim() < 3)
-        {
-            MIGRAPHX_THROW("POOLING: input must have 3 or more dimensions and be nonempty");
-        }
-        if(input.ndim() * 2 != padding_size + 4 and input.ndim() != padding_size + 2)
+        if(input.ndim() != stride_size + 2)
         {
             MIGRAPHX_THROW("POOLING: input and attribute size mismatch!");
         }
@@ -179,6 +200,28 @@ struct pooling
                 }
                 return {input.type(), output_dyn_dims};
             }
+            else if(padding_mode != default_)
+            {
+                const size_t num_spatial_dims = inputs[0].ndim() - 2;
+                const shape& x_shape          = inputs[0];
+                // same as convolution::dynamic_compute_shape()
+
+                for(std::size_t i = 0; i < num_spatial_dims; ++i)
+                {
+                    auto ceil_div = [](std::size_t x, std::size_t y) { return (x + y - 1) / y; };
+                    auto s        = stride[i];
+
+                    auto x = x_shape.dyn_dims()[i + 2];
+                    std::set<std::size_t> optimals{};
+                    std::transform(x.optimals.begin(),
+                                   x.optimals.end(),
+                                   std::inserter(optimals, optimals.begin()),
+                                   [&](auto o) { return ceil_div(o, s); });
+                    output_dyn_dims.push_back(
+                        shape::dynamic_dimension{ceil_div(x.min, s), ceil_div(x.max, s), optimals});
+                }
+                return {input.type(), output_dyn_dims};
+            }
             else
             {
                 // does not compute optimals
@@ -267,6 +310,7 @@ struct pooling
                       Out& output,
                       const In& input,
                       const std::vector<std::size_t>& kernel_dims,
+                      const std::vector<std::size_t>& padding_vals,
                       Op op) const
     {
         auto in_s    = input.get_shape();
@@ -284,8 +328,8 @@ struct pooling
             for(std::size_t dim = 2; dim < n_dim; ++dim)
             {
                 auto d_2  = dim - 2;
-                int start =
-                    static_cast<int>(idx_o[dim] * stride[d_2]) - static_cast<int>(padding[d_2]);
+                int start = static_cast<int>(idx_o[dim] * stride[d_2]) -
+                            static_cast<int>(padding_vals[d_2]);
                 int end;
                 // NOLINT
                 if(count_include_pad and ceil_mode and (mode != pooling_mode::max))
@@ -297,7 +341,7 @@ struct pooling
 
                     // Check if this kernel extends beyond the padding at end of dimension
                     end = std::min(start + kernel_dims[d_2],
-                                   in_lens[dim] + static_cast<int>(padding[d_2]));
+                                   in_lens[dim] + static_cast<int>(padding_vals[d_2]));
                 }
                 else
                 {
@@ -316,11 +360,12 @@ struct pooling
             }
 
             shape win_shape{output_shape.type(), win_size};
+
             auto pool_size    = win_shape.elements();
             double output_val = op.template init<Type>();
 
             // for each element in the window...
-            shape_for_each(win_shape, [&](auto idx_w) {
+            shape_for_each(win_shape, [&](const auto& idx_w) {
                 // the coordinates of this element
                 auto idx = idx_o;
 
@@ -354,30 +399,65 @@ struct pooling
 
     argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{dyn_out.computed_shape};
+        argument result;
         auto input_lens = args[0].get_shape().lens();
         std::vector<std::size_t> kernel_dims;
+        shape output_shape;
+        // If we have to auto-calculate padding, it will be passed to calc_pooling() as an argument
+        // instead of the member variable padding.
+        std::vector<std::size_t> temp_padding(padding);
         if(dyn_global)
         {
+            // for dynamic GlobalPooling, there's no padding
             kernel_dims.insert(kernel_dims.end(), input_lens.begin() + 2, input_lens.end());
+            output_shape = dyn_out.computed_shape;
+            result       = dyn_out.computed_shape;
         }
-        else
+        else if((padding_mode != op::padding_mode_t::default_))
+        {
+            // if padding_mode is set, input was a dynamic size.  Calculate padded size now.
+
+            // kernel_lens is the same as kernel_dims, but prepended with the 2 non-
+            // spatial dimensions.  For size computations, it's used like the weights
+            // tensor for convolutions.
+            std::vector<std::size_t> kernel_lens;
+            kernel_lens.insert(kernel_lens.end(), input_lens.begin(), input_lens.begin() + 2);
+            kernel_lens.insert(kernel_lens.end(), lengths.begin(), lengths.end());
+            kernel_dims = this->lengths;
+
+            auto type = args[0].get_shape().type();
+            // dilation not currently supported for pooling, so default to all 1's
+            temp_padding = calc_dyn_auto_pad(
+                input_lens, kernel_lens, stride, {1, 1}, bool(padding_mode == op::same_upper));
+
+            output_shape = compute_padded_pool_shape(
+                args[0].get_shape(), shape(type, kernel_dims), temp_padding, stride, {1, 1});
+
+            result = argument(output_shape);
+        }
+        else // fixed/static input
         {
             kernel_dims  = this->lengths;
+            output_shape = dyn_out.computed_shape;
+            result       = dyn_out.computed_shape;
         }
+
+        // Perform the computation and populate result
         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>(dyn_out.computed_shape, output, input, kernel_dims, avg_pool{});
+                calc_pooling<type>(
+                    output_shape, output, input, kernel_dims, temp_padding, avg_pool{});
                 break;
             case migraphx::op::pooling_mode::max:
-                calc_pooling<type>(dyn_out.computed_shape, output, input, kernel_dims, max_pool{});
+                calc_pooling<type>(
+                    output_shape, output, input, kernel_dims, temp_padding, max_pool{});
                 break;
             case migraphx::op::pooling_mode::lpnorm:
                 calc_pooling<type>(
-                    dyn_out.computed_shape, output, input, kernel_dims, lpnorm_pool{lp_order});
+                    output_shape, output, input, kernel_dims, temp_padding, lpnorm_pool{lp_order});
                 break;
             }
         });

src/include/migraphx/op/random_seed.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+#ifndef MIGRAPHX_GUARD_OPERATORS_RANDOM_SEED_HPP
+#define MIGRAPHX_GUARD_OPERATORS_RANDOM_SEED_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/argument.hpp>
+#include <random>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+/**
+ *    Generates a random seed for the use of random number generators.  Generating the seed
+ * at runtime guarantees there will be a different random sequence on every execution.
+ * This operation has no inputs or attributes, and outputs an unsigned integer tensor with
+ * a single value.
+ */
+struct random_seed
+{
+    shape::type_t dtype = shape::type_t::uint64_type;
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.dtype, "dtype"));
+    }
+
+    std::string name() const { return "random_seed"; }
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        check_shapes{inputs, *this}.has(0);
+        return shape{dtype};
+    }
+
+    argument compute(const shape& output_shape, const std::vector<argument>&) const
+    {
+        argument result(output_shape);
+
+        result.visit([&](auto output) { output.front() = std::random_device{}(); });
+        return result;
+    }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/random_uniform.hpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+/**
+ * Random Uniform distribution operator.  Given a shape, populate it with random
+ * values.  Calls to random_uniform using the same randomization seed as a
+ * literal input will
+ * always generate the same pseudo-random sequence.
+ *
+ *      Inputs:   (1) randomization seed (any type is allowed)
+ *                (2) output buffer argument to be populated.
+ *
+ *      Attributes:  none
+ *
+ *      Output:   Returns the buffer from input #2.
+ *
+ */
+#ifndef MIGRAPHX_GUARD_OPERATORS_RANDOM_UNIFORM_HPP
+#define MIGRAPHX_GUARD_OPERATORS_RANDOM_UNIFORM_HPP
+
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/argument.hpp>
+#include <random>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
+
+/**
+ * random_uniform populates the passed shape with random numbers, in a uniform
+ * distribution.  Range for floating-point data types is (0, 1);
+ * for integer types it is [0, <max value for the type>]
+ */
+struct random_uniform
+{
+    // The random_uniform operation needs the random number generator seed
+    // to be passed as a runtime input.
+
+    std::string name() const { return "random_uniform"; }
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        check_shapes{inputs, *this, true}.has(2);
+
+        return inputs.at(1);
+    }
+
+    argument compute(const shape&, std::vector<argument> args) const
+    {
+        // Output goes into the passed buffer, not the shape output.
+        auto result = args[1];
+
+        uint64_t local_seed = args[0].at<uint64_t>(0);
+        std::mt19937 gen(local_seed);
+
+        result.visit([&](auto output) {
+            using type = typename decltype(output)::value_type;
+            if constexpr(std::is_integral<type>{})
+            {
+                // default range for all integer types is
+                // (0, std::uniform_int_distribution<type>::max()).
+                // Todo:  enable different ranges
+                std::uniform_int_distribution<type> dis;
+                std::generate(output.begin(), output.end(), [&] { return dis(gen); });
+            }
+            else
+            {
+                // default real distribution type is double with range (0, 1);
+                std::uniform_real_distribution<> dis;
+                std::generate(output.begin(), output.end(), [&] { return dis(gen); });
+            }
+        });
+        return result;
+    }
+
+    std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 1; }
+};
+
+} // namespace op
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/include/migraphx/op/reduce_op.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -163,7 +163,7 @@ struct reduce_op : op_name<Derived>
         auto& self        = static_cast<const Derived&>(*this);
         auto data_idx     = out_idx;
         accumulator val   = self.init();
-        shape_for_each(batch_shape, [&](auto b_idx) {
+        shape_for_each(batch_shape, [&](const auto& b_idx) {
             this->tune_dims(tuned_axes, b_idx, data_idx);
             accumulator x = input(data_idx.begin(), data_idx.end());
             val           = self.op()(accumulator{self.input()(x)}, val);

src/include/migraphx/op/reverse.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -70,13 +70,13 @@ struct reverse
         argument result{s};
         auto lens = s.lens();
         visit_all(result, args.front())([&](auto output, auto input) {
-            shape_for_each(s, [&](const auto& out_idx) {
-                auto in_idx = out_idx;
+            shape_for_each(s, [&](const auto& out_idx_v, size_t out_idx) {
+                auto in_idx = out_idx_v;
                 for(const auto& axis : axes)
                 {
-                    in_idx[axis] = lens[axis] - 1 - out_idx[axis];
+                    in_idx[axis] = lens[axis] - 1 - out_idx_v[axis];
                 }
-                output[s.index(out_idx)] = input[s.index(in_idx)];
+                output[out_idx] = input[s.index(in_idx)];
             });
         });
 

src/include/migraphx/op/roialign.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -113,10 +113,9 @@ struct roialign
     {
         std::vector<pos_weight> results(bin_grid_size[0] * bin_grid_size[1] * output_height *
                                         output_width);
-        shape_for_each(comp_s, [&](auto idx) {
-            std::array<std::size_t, 2> p = {idx[0], idx[1]};
-            std::array<std::size_t, 2> i = {idx[2], idx[3]};
-            auto index                   = comp_s.index(idx);
+        shape_for_each(comp_s, [&](const auto& idx_v, size_t index) {
+            std::array<std::size_t, 2> p = {idx_v[0], idx_v[1]};
+            std::array<std::size_t, 2> i = {idx_v[2], idx_v[3]};
 
             std::array<float, 2> xy{};
             std::array<int64_t, 2> low{};
@@ -125,7 +124,7 @@ struct roialign
             {
                 xy[ii] = roi_start[ii] + p[ii] * bin_size[ii] +
                          (i[ii] + .5f) * bin_size[ii] / bin_grid_size[ii];
-                xy[ii] = (coord_trans_mode == "output_half_pixel") ? (xy[ii] - 0.5f) : xy[ii];
+                xy[ii] = (coord_trans_mode == "half_pixel") ? (xy[ii] - 0.5f) : xy[ii];
                 if(xy[ii] < -1.0 or xy[ii] > dims[ii])
                 {
                     results[index] = pos_weight{};
@@ -255,7 +254,7 @@ struct roialign
                 std::vector<std::size_t> comp_lens1 = {channels, out_dims[0], out_dims[1]};
                 shape comp_s1{migraphx::shape::float_type, comp_lens1};
                 std::vector<int64_t> vec_index(channels, 0);
-                shape_for_each(comp_s1, [&](auto idx) {
+                shape_for_each(comp_s1, [&](const auto& idx) {
                     auto c  = idx[0];
                     auto ph = idx[1];
                     auto pw = idx[2];

src/include/migraphx/op/scatter.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal

src/include/migraphx/op/slice.hpp

@@ -27,19 +27,34 @@
 #include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
 #include <migraphx/config.hpp>
-#include <migraphx/dyn_output.hpp>
 #include <migraphx/value.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/op/normalize_attribute.hpp>
+#include <migraphx/normalize_attributes.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+/**
+ * Slice operator that accepts variable axes, starts and ends.
+ *
+ * Attributes:
+ * axes: constant axes to slice over (optional)
+ * starts: constant slice starting indices (optional)
+ * ends: constant slice ending indices (optional)
+ *
+ * Parameters:
+ * data: the input tensor to slice (dynamic or static shape)
+ * input_starts: starting indicies of slice (optional, static shape)
+ * input_ends: ending indicies of slice (optional, static shape)
+ * input_axes: axes to slice over (optional, static shape)
+ */
 struct slice
 {
-    std::vector<int64_t> axes;
-    std::vector<int64_t> starts;
-    std::vector<int64_t> ends;
+    std::vector<int64_t> axes{};
+    std::vector<int64_t> starts{};
+    std::vector<int64_t> ends{};
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
@@ -48,8 +63,8 @@ struct slice
     }
 
     /**
-     * Ensure that attribute vectors axes, starts, and ends are all the same size and values are in
-     * limits.
+     * Ensure that attribute vectors axes, starts, and ends are all the same size and values are
+     * within limits.
      */
     value attributes() const
     {
@@ -70,100 +85,235 @@ struct slice
 
     std::string name() const { return "slice"; }
 
-    auto compute_offset(const shape& s) const
-    {
-        const std::vector<std::size_t>& lens    = s.lens();
-        const std::vector<std::size_t>& strides = s.strides();
-        auto offset                             = 0;
-        if(not axes.empty())
-        {
-            for(std::size_t i = 0; i < axes.size(); i++)
-            {
-                auto axis = axes[i];
-                offset += starts[i] * strides[axis];
-            }
-        }
-        else
+    /**
+     * Computes the slice output shape dimensions for given starts, ends,and axes.
+     * Templated to also handle tensor views.
+     * Possibily different type between [in_starts, in_ends] and [in_axes] if in_axes is this
+     * object's axes attribute. Assumes in_starts and in_ends are normalized; in_axes are valid.
+     */
+    template <class A, class B>
+    std::vector<std::size_t>
+    lens_calc(const std::vector<std::size_t>& lengths, A in_starts, A in_ends, B in_axes) const
     {
-            for(std::size_t axis = 0; axis < lens.size(); axis++)
+        auto new_lens = lengths;
+        for(std::size_t i = 0; i < in_axes.size(); ++i)
         {
-                offset += starts[axis] * strides[axis];
+            auto axis      = in_axes[i];
+            new_lens[axis] = in_ends[i] - in_starts[i];
         }
-        }
-        return offset;
+        return new_lens;
     }
 
     shape normalize_compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this, true}.has(1);
+        check_shapes{inputs, *this, true}.has(1, 3, 4);
         auto input_shape = inputs[0];
+        if(inputs.size() == 1)
+        {
             auto t = input_shape.type();
-
-        // TODO:  When support for dynamic shapes is added to normalize_attributes,
-        //  remove this restriction.
             if(input_shape.dynamic() and std::any_of(axes.begin(), axes.end(), [&](auto axis) {
                    return not input_shape.dyn_dims()[axis].is_fixed();
                }))
             {
                 MIGRAPHX_THROW("SLICE: slicing is not allowed on non-fixed dynamic input axis ");
             }
-
-        // For a static shape, old_lens will be adjusted to a new size
-        // for those axes that are sliced.
-        // For dynamic shape, the adjusted old_lens become the new max values,
-        // while updating the old mins and optimals if possible.
-        std::vector<std::size_t> new_mins;
-        std::vector<std::size_t> old_lens;
-        std::vector<std::size_t> old_strides;
-        // Doesn't handle optimals
             if(input_shape.dynamic())
             {
-            old_lens = input_shape.max_lens();
-            new_mins = input_shape.min_lens();
+                return shape{t,
+                             lens_calc(input_shape.min_lens(), starts, ends, axes),
+                             lens_calc(input_shape.max_lens(), starts, ends, axes),
+                             {}};
+            }
+            else
+            {
+                return shape{
+                    t, lens_calc(input_shape.lens(), starts, ends, axes), input_shape.strides()};
+            }
+        }
+        else
+        {
+            // check that starts, ends, and optionally input_axes are all 1D, have the same
+            // dimension, and are static
+            check_shapes{inputs.begin() + 1,
+                         inputs.end(),
+                         std::string("SLICE: inputs (starts, ends, and input_axes)"),
+                         false}
+                .only_dims(1)
+                .same_dims();
+            auto dds = input_shape.to_dynamic().dyn_dims();
+            if(inputs.size() == 3)
+            {
+                if(inputs[1].lens().at(0) != axes.size())
+                {
+                    MIGRAPHX_THROW("SLICE: inputs starts and ends do not have the same dimension "
+                                   "as the axes attribute");
+                }
+                std::for_each(axes.cbegin(), axes.cend(), [&](const auto& axis) {
+                    dds.at(axis) = {0, dds.at(axis).max};
+                });
             }
             else
             {
-            old_lens = input_shape.lens();
-            // For static shape (including during eval step after a dynamic input) the strides are
-            // indexed into the pre-slice array, so they are larger than the apparent size of the
-            // resulting shape.
-            old_strides = input_shape.strides();
+                // if axes is an input, then all the output dimensions could be 0 to the max value
+                std::transform(dds.begin(), dds.end(), dds.begin(), [](auto dd) {
+                    return shape::dynamic_dimension{0, dd.max};
+                });
+            }
+            return shape{input_shape.type(), dds};
+        }
     }
 
-        std::vector<std::size_t> new_lens = old_lens;
+    /**
+     * Calculates the starting offset for the sliced tensor.
+     * Used in compute when only data input and all other information are in the attributes.
+     *
+     * \param s static input shape
+     */
+    auto compute_offset(const shape& s) const
+    {
+        const std::vector<std::size_t>& lens    = s.lens();
+        const std::vector<std::size_t>& strides = s.strides();
+        auto offset                             = 0;
+        if(not axes.empty())
+        {
             for(std::size_t i = 0; i < axes.size(); i++)
             {
                 auto axis = axes[i];
-            size_t sliced_length = ends[i] - starts[i];
-            // A Numpy indexing convention: a slice size larger than the actual dimension
-            // is legal and the "ends" value is clipped to the axis size
-            new_lens[axis] = std::min(new_lens[axis], sliced_length);
-            if(input_shape.dynamic())
+                offset += starts[i] * strides[axis];
+            }
+        }
+        else
+        {
+            for(std::size_t axis = 0; axis < lens.size(); axis++)
             {
-                // TODO: when non-fixed shape slicing is allowed, this will be different than
-                // sliced_length, making use of TBD start/end values.
-                std::size_t sliced_min_length = ends[i] - starts[i];
-                // if the slice size is smaller than maxes but larger than mins
-                new_mins[axis] = std::min(sliced_min_length, new_mins[axis]);
+                offset += starts[axis] * strides[axis];
             }
         }
-        if(input_shape.dynamic())
+        return offset * s.type_size();
+    }
+
+    /**
+     * Calculates the starting offset for the sliced tensor (for aliasing).
+     * Used when the starts and/or the axes are inputs.
+     *
+     * \param s static input shape
+     * \param input_starts starting indices of slice
+     * \param ax_vec axes to slice on
+     */
+    template <class IndView, class Axes>
+    auto compute_offset(const shape& s, const IndView& input_starts, const Axes& ax_vec) const
     {
-            return shape{t, new_mins, new_lens, {}};
+        auto ret = 0;
+        for(std::size_t i = 0; i < ax_vec.size(); ++i)
+        {
+            auto axis = ax_vec[i];
+            ret += input_starts[i] * s.strides().at(axis);
         }
-        else
+        return ret * s.type_size();
+    }
+
+    std::unordered_map<std::string, std::vector<int64_t>>
+    normalize_inputs(const shape& input_shape,
+                     const std::vector<int64_t>& input_starts,
+                     const std::vector<int64_t>& input_ends) const
     {
-            return shape{t, new_lens, old_strides};
+        auto attrs = this->attributes().at("normalize_axes");
+        return {{"input_starts",
+                 normalize_indices(input_starts,
+                                   this->axes,
+                                   input_shape,
+                                   attrs.at("starts"),
+                                   "Slice variable input_starts")},
+                {"input_ends",
+                 normalize_indices(input_ends,
+                                   this->axes,
+                                   input_shape,
+                                   attrs.at("ends"),
+                                   "Slice variable input_ends")}};
     }
+
+    /**
+     * Three input version of the normalize_inputs.
+     * This one also checks that the input_axes are valid.
+     */
+    std::unordered_map<std::string, std::vector<int64_t>>
+    normalize_inputs(shape input_shape,
+                     const std::vector<int64_t>& input_starts,
+                     const std::vector<int64_t>& input_ends,
+                     const std::vector<int64_t>& input_axes) const
+    {
+        auto attrs = this->attributes().at("normalize_axes");
+        auto norm_axes =
+            normalize_axes(input_axes, input_shape, attrs.at("axes"), "Slice variable input_axes");
+        return {{"input_starts",
+                 normalize_indices(input_starts,
+                                   norm_axes,
+                                   input_shape,
+                                   attrs.at("starts"),
+                                   "Slice variable input_starts")},
+                {"input_ends",
+                 normalize_indices(input_ends,
+                                   norm_axes,
+                                   input_shape,
+                                   attrs.at("ends"),
+                                   "Slice variable input ends")},
+                {"input_axes", norm_axes}};
     }
 
     argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
         auto input       = args[0];
-
-        auto offset = compute_offset(input.get_shape()) * dyn_out.computed_shape.type_size();
+        auto input_shape = input.get_shape();
+        switch(args.size())
+        {
+        case 1: {
+            std::size_t offset = compute_offset(input_shape);
             return {dyn_out.computed_shape, [=] { return input.data() + offset; }};
         }
+        case 3: {
+            shape calc_shape;
+            std::size_t offset = 0;
+            visit_all(args[1], args[2])([&](auto input_starts, auto input_ends) {
+                auto norm_inputs = normalize_inputs(input_shape,
+                                                    input_starts.template to_vector<int64_t>(),
+                                                    input_ends.template to_vector<int64_t>());
+                offset = compute_offset(input_shape, norm_inputs.at("input_starts"), this->axes);
+                calc_shape = {input_shape.type(),
+                              lens_calc(input_shape.lens(),
+                                        norm_inputs.at("input_starts"),
+                                        norm_inputs.at("input_ends"),
+                                        this->axes),
+                              input_shape.strides()};
+            });
+            return {calc_shape, [=] { return input.data() + offset; }};
+        }
+        case 4: {
+            shape calc_shape;
+            std::size_t offset = 0;
+            visit_all(args[1], args[2], args[3])(
+                [&](auto input_starts, auto input_ends, auto input_axes) {
+                    auto norm_inputs = normalize_inputs(input_shape,
+                                                        input_starts.template to_vector<int64_t>(),
+                                                        input_ends.template to_vector<int64_t>(),
+                                                        input_axes.template to_vector<int64_t>());
+                    offset           = compute_offset(
+                        input_shape, norm_inputs.at("input_starts"), norm_inputs.at("input_axes"));
+                    calc_shape = shape{input_shape.type(),
+                                       lens_calc(input_shape.lens(),
+                                                 norm_inputs.at("input_starts"),
+                                                 norm_inputs.at("input_ends"),
+                                                 norm_inputs.at("input_axes")),
+                                       input_shape.strides()};
+                });
+            return {calc_shape, [=] { return input.data() + offset; }};
+        }
+        default: {
+            // Should never get here; covering in case some code change occurs
+            MIGRAPHX_THROW("SLICE: invalid number of inputs");
+        }
+        }
+    }
+
     std::ptrdiff_t output_alias(const std::vector<shape>&) const { return 0; }
 };
 

src/include/migraphx/operators.hpp

@@ -55,6 +55,7 @@
 #include <migraphx/op/equal.hpp>
 #include <migraphx/op/erf.hpp>
 #include <migraphx/op/exp.hpp>
+#include <migraphx/op/fill.hpp>
 #include <migraphx/op/flatten.hpp>
 #include <migraphx/op/floor.hpp>
 #include <migraphx/op/fmod.hpp>

src/include/migraphx/pad_calc.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -62,6 +62,14 @@ shape compute_padded_shape(const shape& input,
                            const std::vector<std::size_t>& stride,
                            const std::vector<std::size_t>& dilation);
 
+// Used for dynamic auto padding of pooling operators where padding needs to be computed at
+// evaulation time.
+shape compute_padded_pool_shape(const shape& input,
+                                const shape& kernel,
+                                const std::vector<std::size_t>& padding,
+                                const std::vector<std::size_t>& stride,
+                                const std::vector<std::size_t>& dilation);
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/ranges.hpp

@@ -205,7 +205,7 @@ void transform(Range1&& r1, Range2&& r2, Iterator it, F f)
 }
 
 template <class Range>
-auto reverse(Range& r)
+auto reverse(Range&& r)
 {
     return range(std::make_reverse_iterator(r.end()), std::make_reverse_iterator(r.begin()));
 }

src/include/migraphx/shape.hpp

@@ -263,7 +263,7 @@ struct MIGRAPHX_EXPORT shape
     /// no padding
     bool packed() const;
 
-    /// Returns true is the shape has been transposed. That is the strides are not in descending
+    /// Returns true if the shape has been transposed. That is the strides are not in descending
     /// order
     bool transposed() const;
 

src/include/migraphx/shape_for_each.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -37,11 +37,11 @@ inline namespace MIGRAPHX_INLINE_NS {
 template <class F>
 void shape_for_each(const migraphx::shape& s, F f)
 {
-    // Ensure calls to f use const ref to vector
-    auto call = [&f](const std::vector<std::size_t>& i) { f(i); };
     std::vector<std::size_t> indices(s.lens().size());
+    const auto& index_const_ref = indices;
     shape ss{s.type(), s.lens()};
-    for(std::size_t i = 0; i < ss.elements(); i++)
+    size_t max = ss.elements();
+    for(std::size_t i = 0; i < max; i++)
     {
         std::transform(ss.strides().begin(),
                        ss.strides().end(),
@@ -51,9 +51,13 @@ void shape_for_each(const migraphx::shape& s, F f)
                            assert(len > 0 and stride > 0);
                            return (i / stride) % len;
                        });
-        call(indices);
+        if constexpr(std::is_invocable<F, decltype(index_const_ref), decltype(i)>{})
+            f(index_const_ref, i);
+        else
+            f(index_const_ref);
     }
 }
+
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx