Merge branch 'develop' into uif2-initial

.github/workflows/ci.yaml

@@ -465,7 +465,7 @@ jobs:
     - name: Upload code coverage
       if: "matrix.configuration == 'codecov'"
       env:
-        CODECOV_TOKEN: "8545af1c-f90b-4345-92a5-0d075503ca56"
+        CODECOV_TOKEN: "f5d5a10b-3177-4c76-b25f-9b1c2f165e8b"
       run: |
         sudo apt-get install -y lcov
         cd build

src/include/migraphx/op/pooling.hpp

@@ -70,7 +70,8 @@ struct pooling
     // 2 smaller than the input tensor rank (NCHW layout)
     std::vector<std::size_t> lengths = {1, 1};
 
-    // Dilations are not supported at this time.
+    // Spacing between the elements of the pooling kernel. Must be the same ndim as lengths.
+    std::vector<std::size_t> dilations = {1, 1};
 
     // ceiling mode is a flag affecting output size
     // or equivalently, placements of the pooling kernel.
@@ -99,6 +100,7 @@ struct pooling
                     f(self.padding_mode, "padding_mode"),
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"),
+                    f(self.dilations, "dilations"),
                     f(self.ceil_mode, "ceil_mode"),
                     f(self.lp_order, "lp_order"),
                     f(self.dyn_global, "dyn_global"));
@@ -112,14 +114,17 @@ struct pooling
             return;
         if((padding_mode != default_ and padding.size() != stride.size() and
             (padding.size()) != stride.size() * 2) or
-           stride.size() != lengths.size())
+           stride.size() != lengths.size() or dilations.size() != lengths.size())
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
-        if(std::any_of(lengths.begin(), lengths.end(), [&](auto i) { return (i == 0); }) or
-           std::any_of(stride.begin(), stride.end(), [&](auto i) { return (i == 0); }))
+
+        const auto is_zero = [](auto el) { return el == 0; };
+        if(std::any_of(lengths.begin(), lengths.end(), is_zero) or
+           std::any_of(stride.begin(), stride.end(), is_zero) or
+           std::any_of(dilations.begin(), dilations.end(), is_zero))
         {
-            MIGRAPHX_THROW("POOLING: size 0 pooling kernel or stride");
+            MIGRAPHX_THROW("POOLING: size 0 pooling kernel or stride or dilations");
         }
 
         // TODO:  update lowering to run the reference
@@ -142,6 +147,11 @@ struct pooling
 
     value attributes() const { return {{"normalize_padding", "padding"}}; }
 
+    inline std::size_t dilate_dim(std::size_t dim, std::size_t dilation) const
+    {
+        return 1 + dilation * (dim - 1);
+    }
+
     std::vector<std::size_t> calc_spatial_dim_out(const std::vector<std::size_t>& input_lens,
                                                   std::size_t kdims) const
     {
@@ -151,8 +161,9 @@ struct pooling
             std::size_t padding_factor = 2 * padding[i];
             if(padding.size() == 2 * kdims)
                 padding_factor = padding[i] + padding[i + kdims];
+            std::size_t dilated_length = dilate_dim(lengths[i], dilations[i]);
             std::size_t dim_size;
-            if(input_lens[i + 2] + padding_factor < lengths[i])
+            if(input_lens[i + 2] + padding_factor < dilated_length)
             {
                 if(padding_mode == default_)
                     MIGRAPHX_THROW("POOLING: not enough padding for the given kernel size");
@@ -162,7 +173,7 @@ struct pooling
             }
             else
             {
-                dim_size = input_lens[i + 2] + padding_factor - lengths[i];
+                dim_size = input_lens[i + 2] + padding_factor - dilated_length;
             }
             std::size_t len =
                 (ceil_mode)
@@ -331,6 +342,7 @@ struct pooling
                 int start = static_cast<int>(idx_o[dim] * stride[d_2]) -
                             static_cast<int>(padding_vals[d_2]);
                 int end;
+                std::size_t dilated_kernel_dim = dilate_dim(kernel_dims[d_2], dilations[d_2]);
                 // NOLINT
                 if(count_include_pad and ceil_mode and (mode != pooling_mode::max))
                 {
@@ -340,15 +352,14 @@ struct pooling
                     // padding.  Clip out-of-bounds indexes but not padding.
 
                     // Check if this kernel extends beyond the padding at end of dimension
-                    end = std::min(start + kernel_dims[d_2],
+                    end = std::min(start + dilated_kernel_dim,
                                    in_lens[dim] + static_cast<int>(padding_vals[d_2]));
                 }
                 else
                 {
                     // In non-ceiling mode, when
                     // count_include_pad is false, or for max pooling, clip off padding.
-                    end   = std::min(start + kernel_dims[d_2], in_lens[dim]);
-                    start = std::max(start, 0);
+                    end = std::min(start + dilated_kernel_dim, in_lens[dim]);
                 }
                 win_start.push_back(start);
                 if(end < start)
@@ -366,6 +377,16 @@ struct pooling
 
             // for each element in the window...
             shape_for_each(win_shape, [&](const auto& idx_w) {
+                // Skip elements that belong to the dilated area
+                for(size_t axis = 0; axis < idx_w.size(); ++axis)
+                {
+                    if(idx_w[axis] % dilations[axis])
+                    {
+                        pool_size -= 1;
+                        return;
+                    }
+                }
+
                 // the coordinates of this element
                 auto idx = idx_o;
 
@@ -390,7 +411,15 @@ struct pooling
                     // this is a padding element.  Padding locations
                     // don't contribute to average or max pooling total but can play in
                     // lpnorm pooling.
-                    output_val = op(output_val, 0);
+                    if(mode == pooling_mode::lpnorm)
+                    {
+                        output_val = op(output_val, op.template init<Type>());
+                    }
+                    if(mode == pooling_mode::average)
+                    {
+                        // Ignore padding
+                        pool_size -= 1;
+                    }
                 }
             });
             output[i] = Type(op.final(output_val, pool_size));

src/include/migraphx/rewrite_pooling.hpp

@@ -26,6 +26,7 @@
 
 #include <string>
 #include <migraphx/config.hpp>
+#include <migraphx/instruction_ref.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/onnx/parse_pooling.cpp

@@ -91,6 +91,14 @@ struct parse_pooling : op_parser<parse_pooling>
                 kdims, values["lengths"].size(), "PARSE_POOLING: inconsistent lengths");
         }
 
+        if(contains(info.attributes, "dilations"))
+        {
+            values["dilations"].clear();
+            copy(info.attributes["dilations"].ints(), std::back_inserter(values["dilations"]));
+            check_attr_sizes(
+                kdims, values["dilations"].size(), "PARSE_POOLING: inconsistent dilations");
+        }
+
         // lp_order attribute
         if(contains(info.attributes, "p"))
         {
@@ -169,10 +177,15 @@ struct parse_pooling : op_parser<parse_pooling>
             std::fill_n(values["stride"].begin(), kdims, 1);
         }
 
+        if(values["dilations"].size() != kdims)
+        {
+            values["dilations"].resize(kdims);
+            std::fill_n(values["dilations"].begin(), kdims, 1);
+        }
+
         // used to calculate the supposed output shape
         std::vector<int64_t> orig_padding = paddings;
 
-        // TODO:  add parsing for dilations
         if(contains(info.attributes, "auto_pad") and
            to_upper(info.attributes["auto_pad"].s()) != "NOTSET")
         {
@@ -189,11 +202,10 @@ struct parse_pooling : op_parser<parse_pooling>
             else
             {
                 // Calculate auto padding
-                // dilations (argument 4) not supported; default to all 1's
                 cal_auto_padding_size(info,
                                       values,
                                       values["lengths"].to_vector<std::size_t>(),
-                                      std::vector<size_t>(in_shape.ndim() - 2, 1),
+                                      values["dilations"].to_vector<std::size_t>(),
                                       in_shape.lens(),
                                       paddings);
                 values["padding"] = paddings;

src/onnx/parse_qlinearunary.cpp

+/*
+ * 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.
+ */
+
+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/common.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/onnx/checks.hpp>
+#include <migraphx/onnx/broadcast_qdq.hpp>
+#include <migraphx/op/pooling.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+/*
+ *********************************************************************************
+ *  Reference: see QLinearSigmoid, QLinearLeakyRelu in                           *
+ *  https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md  *
+ *********************************************************************************
+
+com.microsoft.QLinearSigmoid
+QLinearSigmoid takes quantized input data (Tensor), and quantize parameter for output, and produces
+one output data (Tensor) where the function f(x) = quantize(Sigmoid(dequantize(x))), is applied to
+the data tensor elementwise. Where the function Sigmoid(x) = 1 / (1 + exp(-x))
+
+Version
+This version of the operator has been available since version 1 of the 'com.microsoft' operator
+set.
+
+*****************************************************************************************************
+
+com.microsoft.QLinearLeakyRelu
+QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for
+output, and produces one output data (Tensor) where the function f(x) = quantize(alpha *
+dequantize(x)) for dequantize(x) < 0, f(x) = quantize(dequantize(x)) for dequantize(x) >= 0, is
+applied to the data tensor elementwise.
+
+Version
+This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
+
+Attributes
+alpha : float
+Coefficient of leakage.
+
+******************************************************************************************************
+
+Generic input layout of QLinear unary operators:
+
+Inputs (4 - 5)
+X : T
+Input tensor
+
+X_scale : tensor(float)
+Input X's scale. It's a scalar, which means a per-tensor/layer quantization.
+
+X_zero_point (optional) : T
+Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a
+per-tensor/layer quantization.
+
+Y_scale : tensor(float) Output Y's scale. It's a scalar, which means
+a per-tensor/layer quantization.
+
+Y_zero_point (optional) : T Output Y's zero point. Default value is
+0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.
+
+Outputs
+Y : T
+Output tensor
+
+Type Constraints
+T : tensor(uint8), tensor(int8)
+Constrain input and output types to 8 bit tensors.
+
+*/
+
+struct parse_qlinearunary : op_parser<parse_qlinearunary>
+{
+    std::vector<op_desc> operators() const
+    {
+        return {{"QLinearSigmoid", "sigmoid"}, {"QLinearLeakyRelu", "leaky_relu"}};
+    }
+
+    void check_inputs(const op_desc& opd, const std::vector<instruction_ref>& args) const
+    {
+        if(args.size() < 4)
+            MIGRAPHX_THROW(opd.op_name + ": missing inputs");
+
+        const auto& in_x = args[0];
+
+        auto sh_x   = in_x->get_shape();
+        auto type_x = sh_x.type();
+        if(type_x != migraphx::shape::int8_type and type_x != migraphx::shape::uint8_type)
+            MIGRAPHX_THROW(opd.op_name + ": unsupported input type");
+    }
+
+    instruction_ref parse(const op_desc& opd,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          const std::vector<instruction_ref>& args) const
+    {
+        check_inputs(opd, args);
+
+        // X
+        const auto& in_x         = args[0];
+        const auto& in_scale_x   = args[1];
+        const auto& in_zero_pt_x = args[2];
+
+        auto dquant_x = bcast_qdq_instr("dequantizelinear", in_x, in_scale_x, in_zero_pt_x, info);
+
+        // Y = (op(dequantizelinear(x))
+        auto op = parser.load(opd.op_name, info);
+        auto y  = info.add_instruction(op, dquant_x);
+
+        const auto& in_scale_y = args[3];
+
+        // zero_pt for Y is supplied as the last optional argument..
+        if(args.size() == 5)
+            return (bcast_qdq_instr("quantizelinear", y, in_scale_y, args[4], info));
+
+        // if no zero_pt: just broadcast the scale..
+        auto bcast_scale_sigm = bcast_scalar_instr(y->get_shape(), in_scale_y, info);
+        return (info.add_instruction(migraphx::make_op("quantizelinear"), y, bcast_scale_sigm));
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/rewrite_pooling.cpp

@@ -35,25 +35,14 @@
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 
-void rewrite_pooling::apply(module& m) const
+static void replace_with_reduce(module& m, instruction_ref ins)
 {
-    for(auto ins : iterator_for(m))
-    {
-        if(ins->name() != "pooling")
-            continue;
-        if(ins->inputs().empty())
-            continue;
     auto&& s  = ins->inputs().front()->get_shape();
     auto&& op = any_cast<op::pooling>(ins->get_operator());
-        if(not std::all_of(op.padding.begin(), op.padding.end(), [](auto i) { return i == 0; }))
-            continue;
-        if(not std::all_of(op.stride.begin(), op.stride.end(), [](auto i) { return i == 1; }))
-            continue;
     auto lens = s.lens();
-        if(not std::equal(lens.begin() + 2, lens.end(), op.lengths.begin(), op.lengths.end()))
-            continue;
     std::vector<std::int64_t> axes(lens.size() - 2);
     std::iota(axes.begin(), axes.end(), 2);
+
     // average pooling
     if(op.mode == op::pooling_mode::average)
     {
@@ -64,6 +53,131 @@ void rewrite_pooling::apply(module& m) const
     {
         m.replace_instruction(ins, make_op("reduce_max", {{"axes", axes}}), ins->inputs());
     }
+}
+
+static void replace_dilations_with_gather_pooling(module& m, instruction_ref ins)
+{
+    // TODO remove this when MIOpen supports dilated pooling
+    auto&& s  = ins->inputs().front()->get_shape();
+    auto&& op = any_cast<op::pooling>(ins->get_operator());
+    // Ignore N, C axes
+    std::vector<size_t> dims = {s.lens().cbegin() + 2, s.lens().cend()};
+
+    bool default_padding =
+        std::all_of(op.padding.cbegin(), op.padding.cend(), [](auto i) { return i == 0; });
+
+    if(not default_padding)
+    {
+        for(size_t idx{0}; idx < op.padding.size(); ++idx)
+        {
+            // We need to pad both ends
+            dims[idx] += op.padding.at(idx) * 2;
+        }
+    }
+    std::vector<size_t> kernels   = op.lengths;
+    std::vector<size_t> strides   = op.stride;
+    std::vector<size_t> dilations = op.dilations;
+
+    std::vector<std::vector<int>> axis_indices;
+    axis_indices.resize(dims.size());
+
+    for(auto idx{0}; idx < dims.size(); ++idx)
+    {
+        // Only consider if iw fits into the window
+        for(size_t stride{0}; stride < dims.at(idx) - dilations.at(idx) * (kernels.at(idx) - 1);
+            stride += strides.at(idx))
+        {
+            for(size_t step{0}; step < kernels.at(idx); ++step)
+            {
+                axis_indices.at(idx).push_back(stride + dilations.at(idx) * step);
+            }
+        }
+    }
+
+    auto elements = ins->inputs().front();
+    if(not default_padding)
+    {
+        // Pad supports asym, we need to provide both ends
+        std::vector<size_t> padding(2 * s.lens().size(), 0);
+        // Format will be e.g {N, C, P1, P2, N, C, P1, P2}
+        for(size_t idx{0}; idx < op.padding.size(); ++idx)
+        {
+            // Ignore N, C axes
+            padding.at(2 + idx)                   = op.padding.at(idx);
+            padding.at(2 + idx + s.lens().size()) = op.padding.at(idx);
+        }
+
+        // Default value needed for Max pooling
+        elements = m.insert_instruction(
+            ins,
+            make_op("pad", {{"pads", padding}, {"value", std::numeric_limits<float>::lowest()}}),
+            elements);
+    }
+
+    for(auto idx{0}; idx < axis_indices.size(); ++idx)
+    {
+        migraphx::shape s_indices{migraphx::shape::int32_type, {axis_indices.at(idx).size()}};
+        auto indices = m.add_literal(migraphx::literal{s_indices, axis_indices.at(idx)});
+        elements     = m.insert_instruction(
+            ins, make_op("gather", {{"axis", idx + 2 /*ignore N,C*/}}), elements, indices);
+    }
+
+    // Ignore padding
+    std::vector<size_t> new_padding(kernels.size(), 0);
+    // The kernel window elements are places next to each other. E.g. {x1, y1, x2, y2, ...}
+    // We need to skip them to not overlap
+    std::vector<size_t> new_strides(kernels);
+    // Ignore dilations
+    std::vector<size_t> new_dilations(kernels.size(), 1);
+    m.replace_instruction(ins,
+                          make_op("pooling",
+                                  {{"mode", op.mode},
+                                   {"padding", new_padding},
+                                   {"stride", new_strides},
+                                   {"lengths", kernels},
+                                   {"dilations", new_dilations}}),
+                          elements);
+}
+
+void rewrite_pooling::apply(module& m) const
+{
+    for(auto ins : iterator_for(m))
+    {
+        if(ins->name() != "pooling")
+            continue;
+        if(ins->inputs().empty())
+            continue;
+        auto&& s                  = ins->inputs().front()->get_shape();
+        auto&& op                 = any_cast<op::pooling>(ins->get_operator());
+        bool same_kernel_as_shape = std::equal(
+            s.lens().cbegin() + 2, s.lens().cend(), op.lengths.cbegin(), op.lengths.cend());
+        bool default_strides =
+            std::all_of(op.stride.cbegin(), op.stride.cend(), [](auto i) { return i == 1; });
+        bool default_padding =
+            std::all_of(op.padding.cbegin(), op.padding.cend(), [](auto i) { return i == 0; });
+        bool default_dilations =
+            std::all_of(op.dilations.cbegin(), op.dilations.cend(), [](auto i) { return i == 1; });
+        if(same_kernel_as_shape and default_strides and default_padding and default_dilations)
+        {
+            replace_with_reduce(m, ins);
+        }
+        else if(not default_dilations)
+        {
+            // Dilated AvgPool with padding is not supported
+            if(not default_padding and op.mode == op::pooling_mode::average)
+            {
+                continue;
+            }
+            auto size =
+                std::accumulate(s.lens().cbegin(), s.lens().cend(), 1, std::multiplies<size_t>());
+            // Can't handle too much size because of literal size
+            if(size > 100000)
+            {
+                continue;
+            }
+
+            replace_dilations_with_gather_pooling(m, ins);
+        }
     }
 }
 

src/simplify_dyn_ops.cpp

@@ -22,6 +22,7 @@
  * THE SOFTWARE.
  */
 #include <migraphx/simplify_dyn_ops.hpp>
+#include <migraphx/op/slice.hpp>
 #include <migraphx/matcher.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/literal.hpp>
@@ -33,6 +34,10 @@ inline namespace MIGRAPHX_INLINE_NS {
  * Convert 2 input static shape broadcast/multibroadcast into 1 input version.
  * Some compiler passes (ex. simplify_algebra) only support the 1 input versions
  * of the broadcasting operators.
+ * From:
+ * broadcast_op(argument_with_static_shape, argument_with_static_shape)
+ * To:
+ * broadcast_op(argument_with_static_shape); broadcast_op.out_lens = constant_output_dims
  */
 struct find_static_2in_broadcasts
 {
@@ -61,8 +66,65 @@ struct find_static_2in_broadcasts
 };
 
 /**
- * Simplify slice with variable `starts` and `ends` to the constant version if
- * the `input_starts` and `input_ends` inputs are constant.
+ * Simplify slice with 2 inputs to the 1 input version if inputs[1] is constant.
+ * From:
+ * slice(data, constant_input); two attributes set
+ * To:
+ * slice(data); slice.starts, slice.ends. slice.axes set
+ */
+struct find_const_2in_slice
+{
+    auto matcher() const
+    {
+        return match::name("slice")(match::nargs(2), match::arg(1)(match::is_constant()));
+    }
+
+    void apply(module& m, const match::matcher_result& mr) const
+    {
+        auto ins       = mr.result;
+        auto inputs    = ins->inputs();
+        auto slice_op  = any_cast<op::slice>(ins->get_operator());
+        auto set_attrs = slice_op.get_set_attributes();
+        std::vector<int64_t> starts_vec;
+        std::vector<int64_t> ends_vec;
+        std::vector<int64_t> axes_vec;
+        if(set_attrs == op::slice::ends_axes)
+        {
+            // slice(data, starts)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { starts_vec.assign(output.begin(), output.end()); });
+            ends_vec = slice_op.ends;
+            axes_vec = slice_op.axes;
+        }
+        else if(set_attrs == op::slice::starts_axes)
+        {
+            // slice(data, ends)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { ends_vec.assign(output.begin(), output.end()); });
+            starts_vec = slice_op.starts;
+            axes_vec   = slice_op.axes;
+        }
+        else
+        {
+            // slice(data, axes)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { axes_vec.assign(output.begin(), output.end()); });
+            starts_vec = slice_op.starts;
+            ends_vec   = slice_op.ends;
+        }
+        m.replace_instruction(
+            ins,
+            make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
+            inputs.at(0));
+    }
+};
+
+/**
+ * Simplify slice with 3 inputs to the 1 input version if inputs[1:2] are constant.
+ * From:
+ * slice(data, constant_input1, constant_input2); one attribute set
+ * To:
+ * slice(data); slice.starts, slice.ends. slice.axes set
  */
 struct find_const_3in_slice
 {
@@ -77,27 +139,51 @@ struct find_const_3in_slice
     {
         auto ins            = mr.result;
         auto inputs         = ins->inputs();
-        argument starts_arg = inputs.at(1)->eval();
-        argument ends_arg   = inputs.at(2)->eval();
-        if(not starts_arg.empty() and not ends_arg.empty())
-        {
+        auto slice_op       = any_cast<op::slice>(ins->get_operator());
+        auto set_attrs      = slice_op.get_set_attributes();
         std::vector<int64_t> starts_vec;
         std::vector<int64_t> ends_vec;
-            starts_arg.visit([&](auto output) { starts_vec.assign(output.begin(), output.end()); });
-            ends_arg.visit([&](auto output) { ends_vec.assign(output.begin(), output.end()); });
-            auto slice_val = ins->get_operator().to_value();
-            auto axes_vec  = slice_val.at("axes").to_vector<int64_t>();
+        std::vector<int64_t> axes_vec;
+        if(set_attrs == op::slice::axes_only)
+        {
+            // slice(data, starts, ends)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { starts_vec.assign(output.begin(), output.end()); });
+            inputs.at(2)->eval().visit(
+                [&](auto output) { ends_vec.assign(output.begin(), output.end()); });
+            axes_vec = slice_op.axes;
+        }
+        else if(set_attrs == op::slice::ends_only)
+        {
+            // slice(data, starts, axes)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { starts_vec.assign(output.begin(), output.end()); });
+            inputs.at(2)->eval().visit(
+                [&](auto output) { axes_vec.assign(output.begin(), output.end()); });
+            ends_vec = slice_op.ends;
+        }
+        else
+        {
+            // slice(data, ends, axes)
+            inputs.at(1)->eval().visit(
+                [&](auto output) { ends_vec.assign(output.begin(), output.end()); });
+            inputs.at(2)->eval().visit(
+                [&](auto output) { axes_vec.assign(output.begin(), output.end()); });
+            starts_vec = slice_op.starts;
+        }
         m.replace_instruction(
             ins,
             make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
             inputs.at(0));
     }
-    }
 };
 
 /**
- * Simplify slice with variable `starts`, `ends`, and `input_axes` to the constant version if
- * the `input_starts`, `input_ends`, and `input_axes` inputs are constant.
+ * Simplify slice with 4 inputs to the 1 input version if inputs[1:3] are constant.
+ * From:
+ * slice(data, constant_starts, constant_ends, constant_axes)
+ * To:
+ * slice(data); slice.starts, slice.ends. slice.axes set
  */
 struct find_const_4in_slice
 {
@@ -113,9 +199,9 @@ struct find_const_4in_slice
     {
         auto ins            = mr.result;
         auto inputs         = ins->inputs();
-        argument starts_arg = inputs.at(1)->eval();
-        argument ends_arg   = inputs.at(2)->eval();
-        argument axes_arg   = inputs.at(3)->eval();
+        argument starts_arg = inputs.at(1)->eval(false);
+        argument ends_arg   = inputs.at(2)->eval(false);
+        argument axes_arg   = inputs.at(3)->eval(false);
         if(not starts_arg.empty() and not ends_arg.empty() and not axes_arg.empty())
         {
             std::vector<int64_t> starts_vec;
@@ -172,13 +258,76 @@ struct find_static_dimensions_of
     }
 };
 
+/**
+ * Simplify allocate into 2 argument reshape that has constant output dimensions into a static 1
+ * argument reshape. Intended to simplify what ONNX parse_reshape creates for dynamic reshapes.
+ * This matcher can be generalized to matching reshape(data, static_shape_output_tensor).
+ * From:
+ * x = allocate(constant_output_dims) -> reshape(data, x)
+ * To:
+ * reshape(data); reshape.dims = constant_output_dims
+ */
+struct find_const_alloc_reshapes
+{
+    auto matcher() const
+    {
+        return match::name("reshape")(match::nargs(2),
+                                      match::arg(1)(match::name("allocate")(match::is_constant())));
+    }
+
+    void apply(module& m, const match::matcher_result& mr) const
+    {
+        auto reshape_ins         = mr.result;
+        auto reshape_inputs      = reshape_ins->inputs();
+        auto alloc_ins           = reshape_inputs.at(1);
+        argument output_dims_arg = alloc_ins->inputs().at(0)->eval(false);
+        std::vector<int64_t> output_dims_vec;
+        output_dims_arg.visit(
+            [&](auto output) { output_dims_vec.assign(output.begin(), output.end()); });
+        m.replace_instruction(
+            reshape_ins, make_op("reshape", {{"dims", output_dims_vec}}), reshape_inputs.at(0));
+        // have dead_code_elimination remove the previous allocate
+    }
+};
+
+/**
+ * Simplify allocate into fill operator that has constant output dimensions and constant value.
+ * The allocate into fill instructions is what is produced when parsing the ONNX
+ * ConstantOfShape operator. This replacement could be handled with propagate_constant, but
+ * would rather have the simplification happen earlier during compiling.
+ * This matcher can be generalized to matching fill(constant_value, static_shape_output_tensor).
+ * From:
+ * x = allocate(constant_ouptut_dims) -> fill(constant_value, x)
+ * To:
+ * literal
+ */
+struct find_const_alloc_fill
+{
+    auto matcher() const
+    {
+        return match::name("fill")(match::arg(0)(match::is_constant()),
+                                   match::arg(1)(match::name("allocate")(match::is_constant())));
+    }
+
+    void apply(module& m, const match::matcher_result& mr) const
+    {
+        auto fill_ins = mr.result;
+        auto fill_arg = fill_ins->eval(false);
+        auto l        = m.add_literal(fill_arg.get_shape(), fill_arg.data());
+        m.replace_instruction(fill_ins, l);
+    }
+};
+
 void simplify_dyn_ops::apply(module& m) const
 {
     match::find_matches(m,
-                        find_static_2in_broadcasts{},
                         find_static_dimensions_of{},
+                        find_const_alloc_reshapes{},
+                        find_static_2in_broadcasts{},
+                        find_const_2in_slice{},
                         find_const_3in_slice{},
-                        find_const_4in_slice{});
+                        find_const_4in_slice{},
+                        find_const_alloc_fill{});
 }
 
 } // namespace MIGRAPHX_INLINE_NS

src/targets/cpu/pooling.cpp

@@ -34,23 +34,32 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace cpu {
 
-struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_forward, op::pooling>
+struct dnnl_pooling : dnnl_extend_op<dnnl_pooling, dnnl::pooling_v2_forward, op::pooling>
 {
     std::vector<int> arg_map(int) const { return {MIGRAPHX_DNNL_PREFIX(ARG_SRC)}; }
 
-    dnnl::pooling_forward::desc get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
+    dnnl::pooling_v2_forward::desc
+    get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
     {
         auto algo  = op.mode == op::pooling_mode::max ? dnnl::algorithm::pooling_max
                                                       : dnnl::algorithm::pooling_avg;
         auto kdims = op.kdims();
         std::vector<size_t> padding_l(op.padding.begin(), op.padding.begin() + kdims);
         std::vector<size_t> padding_r(op.padding.begin() + kdims, op.padding.end());
+        // Note: It is not documented, but the default dilation seems to be 0 instead of 1.
+        //       We need to offset dilations with -1.
+        std::vector<size_t> dilations;
+        std::transform(op.dilations.cbegin(),
+                       op.dilations.cend(),
+                       std::back_inserter(dilations),
+                       [](size_t d) { return d - 1; });
         return {dnnl::prop_kind::forward_inference,
                 algo,
                 m.at(MIGRAPHX_DNNL_PREFIX(ARG_SRC)),
                 m.at(MIGRAPHX_DNNL_PREFIX(ARG_DST)),
                 to_dnnl_dims(op.stride),
                 to_dnnl_dims(op.lengths),
+                to_dnnl_dims(dilations),
                 to_dnnl_dims(padding_l),
                 to_dnnl_dims(padding_r)};
     }

src/targets/gpu/compile_hip.cpp

@@ -194,7 +194,7 @@ struct hiprtc_program
 };
 
 std::vector<std::vector<char>> compile_hip_src_with_hiprtc(std::vector<hiprtc_src_file> srcs,
-                                                           std::string params,
+                                                           const std::string& params,
                                                            const std::string& arch)
 {
     hiprtc_program prog(std::move(srcs));
@@ -238,8 +238,9 @@ bool hip_has_flags(const std::vector<std::string>& flags)
     }
 }
 
-std::vector<std::vector<char>>
-compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std::string& arch)
+std::vector<std::vector<char>> compile_hip_src(const std::vector<src_file>& srcs,
+                                               const std::string& params,
+                                               const std::string& arch)
 {
     std::vector<hiprtc_src_file> hsrcs{srcs.begin(), srcs.end()};
     if(enabled(MIGRAPHX_GPU_DUMP_SRC{}))
@@ -281,13 +282,13 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
         if(fs::exists(out))
             return {read_buffer(out.string())};
     }
-    return compile_hip_src_with_hiprtc(std::move(hsrcs), std::move(params), arch);
+    return compile_hip_src_with_hiprtc(std::move(hsrcs), params, arch);
 }
 
 #else // MIGRAPHX_USE_HIPRTC
 
 std::vector<std::vector<char>> compile_hip_src_with_hiprtc(std::vector<hiprtc_src_file>, // NOLINT
-                                                           std::string,                  // NOLINT
+                                                           const std::string&,           // NOLINT
                                                            const std::string&)
 {
     MIGRAPHX_THROW("Not using hiprtc");
@@ -316,29 +317,15 @@ src_compiler assemble(src_compiler compiler)
     return compiler;
 }
 
-std::vector<std::vector<char>>
-compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std::string& arch)
+std::vector<std::vector<char>> compile_hip_src(const std::vector<src_file>& srcs,
+                                               const std::string& params,
+                                               const std::string& arch)
 {
     assert(not srcs.empty());
+
     if(not is_hip_clang_compiler())
         MIGRAPHX_THROW("Unknown hip compiler: " MIGRAPHX_HIP_COMPILER);
 
-    if(params.find("-std=") == std::string::npos)
-        params += " --std=c++17";
-    params += " -fno-gpu-rdc";
-    if(enabled(MIGRAPHX_GPU_DEBUG_SYM{}))
-        params += " -g";
-    params += " -c";
-    params += " --offload-arch=" + arch;
-    params += " --cuda-device-only";
-    params += " -O" + string_value_of(MIGRAPHX_GPU_OPTIMIZE{}, "3") + " ";
-
-    if(enabled(MIGRAPHX_GPU_DEBUG{}))
-        params += " -DMIGRAPHX_DEBUG";
-
-    params += " -Wno-unused-command-line-argument -Wno-cuda-compat ";
-    params += MIGRAPHX_HIP_COMPILER_FLAGS;
-
     src_compiler compiler;
     compiler.flags    = params;
     compiler.compiler = MIGRAPHX_HIP_COMPILER;
@@ -346,6 +333,23 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
     if(has_compiler_launcher())
         compiler.launcher = MIGRAPHX_HIP_COMPILER_LAUNCHER;
 #endif
+
+    if(params.find("-std=") == std::string::npos)
+        compiler.flags += " --std=c++17";
+    compiler.flags += " -fno-gpu-rdc";
+    if(enabled(MIGRAPHX_GPU_DEBUG_SYM{}))
+        compiler.flags += " -g";
+    compiler.flags += " -c";
+    compiler.flags += " --offload-arch=" + arch;
+    compiler.flags += " --cuda-device-only";
+    compiler.flags += " -O" + string_value_of(MIGRAPHX_GPU_OPTIMIZE{}, "3") + " ";
+
+    if(enabled(MIGRAPHX_GPU_DEBUG{}))
+        compiler.flags += " -DMIGRAPHX_DEBUG";
+
+    compiler.flags += " -Wno-unused-command-line-argument -Wno-cuda-compat ";
+    compiler.flags += MIGRAPHX_HIP_COMPILER_FLAGS;
+
     if(enabled(MIGRAPHX_GPU_DUMP_SRC{}))
     {
         for(const auto& src : srcs)

src/targets/gpu/compile_hip_code_object.cpp

@@ -200,7 +200,7 @@ operation compile_hip_code_object(const std::string& content, hip_compile_option
     options.params += " " + join_strings(compiler_warnings(), " ");
     options.params += " -ftemplate-backtrace-limit=0";
     options.params += " -Werror";
-    auto cos = compile_hip_src(srcs, std::move(options.params), get_device_name());
+    auto cos = compile_hip_src(srcs, options.params, get_device_name());
     if(cos.size() != 1)
         MIGRAPHX_THROW("No code object");
     return code_object_op{value::binary{cos.front()},

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

@@ -43,24 +43,32 @@ template <index_int N,
 __device__ void block_scan(index idx, Op op, T init, ForStride fs, Input input, Output output)
 {
     using type = decltype(input(deduce_for_stride(fs)));
-    MIGRAPHX_DEVICE_SHARED type buffer[N];
+    MIGRAPHX_DEVICE_SHARED type buffer[2][N];
     type x = init;
     fs([&](auto i) {
+        index_int iout = 0;
+        index_int iin  = 1;
         if(idx.local == 0)
-            buffer[idx.local] = op(input(i), x);
+            buffer[iout][idx.local] = op(input(i), x);
         else
-            buffer[idx.local] = input(i);
+            buffer[iout][idx.local] = input(i);
         __syncthreads();
         for(index_int s = 1; s < idx.nlocal(); s *= 2)
         {
-            if(idx.local + s < idx.nlocal())
+            iout = 1 - iout;
+            iin  = 1 - iin;
+            if(idx.local >= s)
             {
-                buffer[idx.local + s] = op(buffer[idx.local], buffer[idx.local + s]);
+                buffer[iout][idx.local] = op(buffer[iin][idx.local], buffer[iin][idx.local - s]);
+            }
+            else
+            {
+                buffer[iout][idx.local] = buffer[iin][idx.local];
             }
             __syncthreads();
         }
-        x = buffer[idx.nlocal() - 1];
-        output(i, buffer[idx.local]);
+        x = buffer[iout][idx.nlocal() - 1];
+        output(i, buffer[iout][idx.local]);
     });
 }
 

src/targets/gpu/driver/precompile_op.cpp

+/*
+ * 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.
+ */
+#include <migraphx/gpu/driver/action.hpp>
+#include <migraphx/gpu/time_op.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/lowering.hpp>
+#include <migraphx/gpu/compile_ops.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/pass_manager.hpp>
+#include <migraphx/program.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace driver {
+
+struct precompile_op : action<precompile_op>
+{
+    static program create_preop_program(const operation& preop, std::vector<shape> inputs)
+    {
+        program p;
+        auto* mm = p.get_main_module();
+        std::vector<instruction_ref> args;
+        inputs.pop_back();
+        transform(inputs, range(inputs.size()), std::back_inserter(args), [&](auto input, auto i) {
+            return mm->add_parameter("x" + std::to_string(i), input);
+        });
+        mm->add_instruction(preop, args);
+        return p;
+    }
+
+    static operation get_code_object(const program& p)
+    {
+        MIGRAPHX_TIDY_CONST auto* mm = p.get_main_module();
+        auto it                      = std::find_if(mm->begin(), mm->end(), [](const auto& ins) {
+            return (ins.name() == "gpu::code_object");
+        });
+        if(it == mm->end())
+            MIGRAPHX_THROW("Failed to create code object");
+        return it->get_operator();
+    }
+    static void apply(const parser& p, const value& v)
+    {
+        context ctx;
+        auto inputs = p.parse_shapes(v.at("inputs"));
+        auto name   = v.at("name").to<std::string>();
+        auto preop  = make_op(name);
+        if(v.contains("fields"))
+            preop.from_value(v.at("fields"));
+        bool exhaustive = v.get("exhaustive", false);
+        auto prog       = create_preop_program(preop, inputs);
+        run_passes(prog, {lowering{}, compile_ops{&ctx, exhaustive}});
+        auto op = get_code_object(prog);
+        auto t  = time_op(ctx, op, inputs, p.get(v, "iterations", 100));
+        std::cout << preop << ": " << t << "ms" << std::endl;
+    }
+};
+
+} // namespace driver
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

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

@@ -58,10 +58,10 @@ struct hiprtc_src_file
 MIGRAPHX_GPU_EXPORT bool hip_has_flags(const std::vector<std::string>& flags);
 
 MIGRAPHX_GPU_EXPORT std::vector<std::vector<char>> compile_hip_src_with_hiprtc(
-    std::vector<hiprtc_src_file> srcs, std::string params, const std::string& arch);
+    std::vector<hiprtc_src_file> srcs, const std::string& params, const std::string& arch);
 
-MIGRAPHX_GPU_EXPORT std::vector<std::vector<char>>
-compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std::string& arch);
+MIGRAPHX_GPU_EXPORT std::vector<std::vector<char>> compile_hip_src(
+    const std::vector<src_file>& srcs, const std::string& params, const std::string& arch);
 
 MIGRAPHX_GPU_EXPORT std::string enum_params(std::size_t count, std::string param);
 

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

@@ -211,6 +211,12 @@ inline pooling_descriptor make_pooling(const migraphx::op::pooling& op)
         ss << op.mode;
         MIGRAPHX_THROW(ss.str());
     }
+    if(not std::all_of(
+           op.dilations.cbegin(), op.dilations.cend(), [](std::size_t d) { return d == 1; }))
+    {
+        MIGRAPHX_THROW("Unsupported dilations for pooling: [" + to_string_range(op.dilations) +
+                       "]");
+    }
     auto p = make_obj<pooling_descriptor>(&miopenCreatePoolingDescriptor);
 
     int kdims = op.kdims();

test/onnx/averagepool_dilate_test.onnx

+averagepool_dilate_test:

+Y
+
x
y"AveragePool*
+	dilations@
*
+kernel_shape@
*
+pads@
@

*
+strides@

averagepool_dilate_testZ
+
x
+

+

+
+b
+
y
+

+

+
+B
\ No newline at end of file

test/onnx/gen_onnx.py

@@ -276,6 +276,22 @@ def averagepool_1d_test():
     return ([node], [x], [out])
 
 
+@onnx_test()
+def averagepool_dilate_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4, 2])
+
+    node = onnx.helper.make_node('AveragePool',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 kernel_shape=[2],
+                                 strides=[1],
+                                 pads=[1, 1],
+                                 dilations=[3])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def averagepool_3d_test():
     x = helper.make_tensor_value_info('0', TensorProto.FLOAT, [1, 3, 5, 5, 5])
@@ -4882,6 +4898,22 @@ def maxpool_notset_test():
     return ([node], [x], [y])
 
 
+@onnx_test()
+def maxpool_dilate_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 4, 3])
+    y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [1, 4, 2])
+
+    node = onnx.helper.make_node('MaxPool',
+                                 inputs=['x'],
+                                 outputs=['y'],
+                                 kernel_shape=[2],
+                                 strides=[1],
+                                 pads=[1, 1],
+                                 dilations=[3])
+
+    return ([node], [x], [y])
+
+
 @onnx_test()
 def maxpool_same_upper_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [1, 1, 5, 5])
@@ -6094,6 +6126,26 @@ def qlinearglobalavgpool_test():
     return ([n], [x], [y], [sc_x, z_pt_x, sc_y, z_pt_y])
 
 
+@onnx_test()
+def qlinearleakyrelu_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT8, [64])
+    sc_x = helper.make_tensor('X_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_x = helper.make_tensor('X_zero_point', TensorProto.INT8, [], [0])
+
+    sc_y = helper.make_tensor('Y_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_y = helper.make_tensor('Y_zero_point', TensorProto.INT8, [], [10])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.INT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearLeakyRelu',
+        inputs=['X', 'X_scale', 'X_zero_point', 'Y_scale', 'Y_zero_point'],
+        outputs=['Y'],
+        alpha=1.1,
+    )
+    return ([node], [x], [y], [sc_x, zero_pt_x, sc_y, zero_pt_y])
+
+
 def qlinearmatmul_1D_test():
     a = helper.make_tensor_value_info('A', TensorProto.UINT8, [8])
     sc_a = helper.make_tensor('A_scale', TensorProto.FLOAT, [], [0.05])
@@ -6234,6 +6286,26 @@ def qlinearmul_bcast_test():
             [sc_a, zero_pt_a, sc_b, zero_pt_b, sc_c, zero_pt_c])
 
 
+@onnx_test()
+def qlinearsigmoid_test():
+    x = helper.make_tensor_value_info('X', TensorProto.INT8, [64])
+    sc_x = helper.make_tensor('X_scale', TensorProto.FLOAT, [], [0.05])
+    zero_pt_x = helper.make_tensor('X_zero_point', TensorProto.INT8, [], [0])
+
+    sc_y = helper.make_tensor('Y_scale', TensorProto.FLOAT, [], [0.0035])
+    zero_pt_y = helper.make_tensor('Y_zero_point', TensorProto.INT8, [],
+                                   [-128])
+
+    y = helper.make_tensor_value_info('Y', TensorProto.INT8, [64])
+
+    node = onnx.helper.make_node(
+        'QLinearSigmoid',
+        inputs=['X', 'X_scale', 'X_zero_point', 'Y_scale', 'Y_zero_point'],
+        outputs=['Y'],
+    )
+    return ([node], [x], [y], [sc_x, zero_pt_x, sc_y, zero_pt_y])
+
+
 @onnx_test()
 def quantizelinear_test():
     arg0 = helper.make_tensor_value_info('0', TensorProto.FLOAT, [5])

test/onnx/maxpool_dilate_test.onnx

+maxpool_dilate_test:

+U
+
x
y"MaxPool*
+	dilations@
*
+kernel_shape@
*
+pads@
@

*
+strides@

maxpool_dilate_testZ
+
x
+

+

+
+b
+
y
+

+

+
+B
\ No newline at end of file

test/onnx/onnx_test.cpp

@@ -296,13 +296,32 @@ TEST_CASE(averagepool_1d_test)
                                           {{"mode", migraphx::op::pooling_mode::average},
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
-                                           {"lengths", {3}}}),
+                                           {"lengths", {3}},
+                                           {"dilations", {1}}}),
                         l0);
 
     auto prog = optimize_onnx("averagepool_1d_test.onnx");
     EXPECT(p == prog);
 }
 
+TEST_CASE(averagepool_dilate_test)
+{
+    migraphx::program p;
+    auto* mm   = p.get_main_module();
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1, 4, 3}});
+    mm->add_instruction(migraphx::make_op("pooling",
+                                          {{"mode", migraphx::op::pooling_mode::average},
+                                           {"padding", {1, 1}},
+                                           {"stride", {1}},
+                                           {"lengths", {2}},
+                                           {"dilations", {3}}}),
+                        input);
+
+    auto prog = optimize_onnx("averagepool_dilate_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(averagepool_3d_test)
 {
     migraphx::program p;
@@ -312,7 +331,8 @@ TEST_CASE(averagepool_3d_test)
                                           {{"mode", migraphx::op::pooling_mode::average},
                                            {"padding", {0, 0, 0, 0, 0, 0}},
                                            {"stride", {1, 1, 1}},
-                                           {"lengths", {3, 3, 3}}}),
+                                           {"lengths", {3, 3, 3}},
+                                           {"dilations", {1, 1, 1}}}),
                         l0);
 
     auto prog = optimize_onnx("averagepool_3d_test.onnx");
@@ -332,6 +352,7 @@ TEST_CASE(averagepool_dyn_test)
                                                   {"mode", migraphx::op::pooling_mode::average},
                                                   {"stride", {2, 2, 2}},
                                                   {"lengths", {3, 3, 3}},
+                                                  {"dilations", {1, 1, 1}},
                                                   {"padding", {1, 1, 1, 1, 1, 1}},
                                                   {"padding_mode", 0},
                                               }),
@@ -357,6 +378,7 @@ TEST_CASE(averagepool_dyn_autopad_test)
                               {"mode", migraphx::op::pooling_mode::average},
                               {"stride", {2, 2, 2}},
                               {"lengths", {3, 3, 3}},
+                              {"dilations", {1, 1, 1}},
                               {"padding", {0, 0, 0, 0, 0, 0}},
                               {"padding_mode", migraphx::op::padding_mode_t::same_upper},
                           }),
@@ -394,7 +416,8 @@ TEST_CASE(averagepool_notset_test)
                                                        {{"mode", migraphx::op::pooling_mode::average},
                                                         {"padding", {2, 2, 2, 2}},
                                                         {"stride", {2, 2}},
-                                                        {"lengths", {6, 6}}}),
+                                                        {"lengths", {6, 6}},
+                                                        {"dilations", {1, 1}}}),
                                    input);
     auto ret   = mm->add_instruction(
         migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {2, 2}}}), ins);
@@ -415,7 +438,8 @@ TEST_CASE(averagepool_nt_cip_test)
                                                                       {{"mode", migraphx::op::pooling_mode::average},
                                                                        {"padding", {0, 0, 0, 0}},
                                                                        {"stride", {2, 2}},
-                                                                       {"lengths", {6, 6}}}),
+                                                                       {"lengths", {6, 6}},
+                                                                       {"dilations", {1, 1}}}),
                                    ins_pad);
     mm->add_return({ret});
 
@@ -437,6 +461,7 @@ TEST_CASE(averagepool_same_lower_test)
                               {"padding", {1, 1, 1, 1}},
                               {"stride", {1, 1}},
                               {"lengths", {2, 2}},
+                              {"dilations", {1, 1}},
                               {"padding_mode", migraphx::op::padding_mode_t::default_},
                           }),
         input);
@@ -459,7 +484,8 @@ TEST_CASE(averagepool_sl_cip_test)
                                                                       {{"mode", migraphx::op::pooling_mode::average},
                                                                        {"padding", {0, 0, 0, 0}},
                                                                        {"stride", {1, 1}},
-                                                                       {"lengths", {2, 2}}}),
+                                                                       {"lengths", {2, 2}},
+                                                                       {"dilations", {1, 1}}}),
                                    ins_pad);
     mm->add_return({ret});
     auto prog = migraphx::parse_onnx("averagepool_sl_cip_test.onnx");
@@ -476,7 +502,8 @@ TEST_CASE(averagepool_same_upper_test)
                                                        {{"mode", migraphx::op::pooling_mode::average},
                                                         {"padding", {1, 1, 1, 1}},
                                                         {"stride", {1, 1}},
-                                                        {"lengths", {2, 2}}}),
+                                                        {"lengths", {2, 2}},
+                                                        {"dilations", {1, 1}}}),
                                    input);
     auto ret   = mm->add_instruction(
         migraphx::make_op("slice", {{"axes", {2, 3}}, {"starts", {1, 1}}, {"ends", {6, 6}}}), ins);
@@ -1307,7 +1334,8 @@ TEST_CASE(conv_bn_relu_maxpool_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l7);
 
     auto prog = optimize_onnx("conv_bn_relu_maxpool_test.onnx");
@@ -1505,7 +1533,8 @@ TEST_CASE(conv_relu_maxpool_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l6);
 
     auto prog = optimize_onnx("conv_relu_maxpool_test.onnx");
@@ -1530,7 +1559,8 @@ TEST_CASE(conv_relu_maxpool_x2_test)
                                                     {{"mode", migraphx::op::pooling_mode::max},
                                                      {"padding", {0, 0, 0, 0}},
                                                      {"stride", {2, 2}},
-                                                     {"lengths", {2, 2}}}),
+                                                     {"lengths", {2, 2}},
+                                                     {"dilations", {1, 1}}}),
                                   l6);
 
     auto l8 = mm->add_parameter("3", {migraphx::shape::float_type, {1, 5, 5, 5}});
@@ -1546,7 +1576,8 @@ TEST_CASE(conv_relu_maxpool_x2_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 0, 0}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         l13);
 
     auto prog = optimize_onnx("conv_relu_maxpool_x2_test.onnx");
@@ -4245,6 +4276,7 @@ TEST_CASE(lppool_l1_test)
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
                                            {"lengths", {3}},
+                                           {"dilations", {1}},
                                            {"lp_order", 1}}),
                         l0);
     auto prog = optimize_onnx("lppool_l1_test.onnx");
@@ -4261,6 +4293,7 @@ TEST_CASE(lppool_l2_test)
                                            {"padding", {0, 0}},
                                            {"stride", {1}},
                                            {"lengths", {3}},
+                                           {"dilations", {1}},
                                            {"lp_order", 2}}),
                         l0);
     auto prog = optimize_onnx("lppool_l2_test.onnx");
@@ -4513,7 +4546,8 @@ TEST_CASE(maxpool_notset_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 1, 1}},
                                            {"stride", {2, 2}},
-                                           {"lengths", {6, 6}}}),
+                                           {"lengths", {6, 6}},
+                                           {"dilations", {1, 1}}}),
                         input);
 
     auto prog = optimize_onnx("maxpool_notset_test.onnx");
@@ -4521,6 +4555,24 @@ TEST_CASE(maxpool_notset_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(maxpool_dilate_test)
+{
+    migraphx::program p;
+    auto* mm   = p.get_main_module();
+    auto input = mm->add_parameter("x", migraphx::shape{migraphx::shape::float_type, {1, 4, 3}});
+    mm->add_instruction(migraphx::make_op("pooling",
+                                          {{"mode", migraphx::op::pooling_mode::max},
+                                           {"padding", {1, 1}},
+                                           {"stride", {1}},
+                                           {"lengths", {2}},
+                                           {"dilations", {3}}}),
+                        input);
+
+    auto prog = optimize_onnx("maxpool_dilate_test.onnx");
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(maxpool_same_upper_test)
 {
     migraphx::program p;
@@ -4530,7 +4582,8 @@ TEST_CASE(maxpool_same_upper_test)
                                           {{"mode", migraphx::op::pooling_mode::max},
                                            {"padding", {0, 0, 1, 1}},
                                            {"stride", {1, 1}},
-                                           {"lengths", {2, 2}}}),
+                                           {"lengths", {2, 2}},
+                                           {"dilations", {1, 1}}}),
                         input);
 
     auto prog = optimize_onnx("maxpool_same_upper_test.onnx");
@@ -5642,6 +5695,46 @@ TEST_CASE(qlinearglobalavgpool_test)
     EXPECT(p.sort() == prog.sort());
 }
 
+TEST_CASE(qlinearleakyrelu_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto x = mm->add_parameter("X", {migraphx::shape::int8_type, {64}});
+
+    auto sc_x   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
+
+    auto sc_y   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {10}});
+
+    auto scale_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_x);
+
+    auto z_pt_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_x);
+
+    auto fp_x =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
+
+    auto fp_y = mm->add_instruction(migraphx::make_op("leaky_relu", {{"alpha", 1.1}}), fp_x);
+
+    auto scale_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_y);
+
+    auto z_pt_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_y);
+
+    auto y =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
+
+    mm->add_return({y});
+
+    auto prog = migraphx::parse_onnx("qlinearleakyrelu_test.onnx");
+
+    EXPECT(p.sort() == prog.sort());
+}
+
 TEST_CASE(qlinearmatmul_1D_test)
 {
     migraphx::program p;
@@ -5807,6 +5900,46 @@ TEST_CASE(qlinearmul_test)
     EXPECT(p.sort() == prog.sort());
 }
 
+TEST_CASE(qlinearsigmoid_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto x = mm->add_parameter("X", {migraphx::shape::int8_type, {64}});
+
+    auto sc_x   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.05}});
+    auto z_pt_x = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {0}});
+
+    auto sc_y   = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.0035}});
+    auto z_pt_y = mm->add_literal(migraphx::literal{migraphx::shape::int8_type, {-128}});
+
+    auto scale_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_x);
+
+    auto z_pt_x_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_x);
+
+    auto fp_x =
+        mm->add_instruction(migraphx::make_op("dequantizelinear"), x, scale_x_bcast, z_pt_x_bcast);
+
+    auto fp_y = mm->add_instruction(migraphx::make_op("sigmoid"), fp_x);
+
+    auto scale_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), sc_y);
+
+    auto z_pt_y_bcast =
+        mm->add_instruction(migraphx::make_op("multibroadcast", {{"out_lens", {64}}}), z_pt_y);
+
+    auto y =
+        mm->add_instruction(migraphx::make_op("quantizelinear"), fp_y, scale_y_bcast, z_pt_y_bcast);
+
+    mm->add_return({y});
+
+    auto prog = migraphx::parse_onnx("qlinearsigmoid_test.onnx");
+
+    EXPECT(p.sort() == prog.sort());
+}
+
 migraphx::instruction_ref insert_quantizelinear_clip(migraphx::module& m,
                                                      const migraphx::instruction_ref ins,
                                                      const migraphx::instruction_ref round,