Merge branch 'develop' into add_parity_check_ci

src/include/migraphx/op/multibroadcast.hpp

@@ -69,7 +69,7 @@ struct multibroadcast
 
         auto make_bcast_strides = [&](std::vector<std::size_t> bcast_lens, std::size_t offset) {
             std::vector<size_t> bcast_strides(bcast_lens.size(), 0);
-            for(std::ptrdiff_t i = s0.lens().size() - 1; i >= 0; i--)
+            for(std::ptrdiff_t i = s0.ndim() - 1; i >= 0; i--)
             {
                 if(bcast_lens[i + offset] == s0.lens()[i])
                 {
@@ -84,13 +84,13 @@ struct multibroadcast
             if(s0.dynamic())
                 MIGRAPHX_THROW(
                     "MULTIBROADCAST: Single dynamic input shape not supported.  Use two inputs.");
-            if(s0.lens().size() > output_lens.size())
+            if(s0.ndim() > output_lens.size())
             {
                 MIGRAPHX_THROW("MULTIBROADCAST: input dimensions should <= output size");
             }
 
-            auto offset = output_lens.size() - s0.lens().size();
-            for(std::ptrdiff_t i = s0.lens().size() - 1; i >= 0; i--)
+            auto offset = output_lens.size() - s0.ndim();
+            for(std::ptrdiff_t i = s0.ndim() - 1; i >= 0; i--)
             {
                 if(output_lens[i + offset] != s0.lens()[i] and s0.lens()[i] != 1)
                 {
@@ -119,7 +119,7 @@ struct multibroadcast
             {
                 // output_lens will not be set for 2+ input version
                 auto bcast_lens    = compute_common_lens(inputs);
-                auto offset        = bcast_lens.size() - s0.lens().size();
+                auto offset        = bcast_lens.size() - s0.ndim();
                 auto bcast_strides = make_bcast_strides(bcast_lens, offset);
                 return {t, std::move(bcast_lens), std::move(bcast_strides)};
             }

src/include/migraphx/operation.hpp

@@ -143,7 +143,7 @@ auto compute_shape_op(rank<2>, const T& x, const std::vector<shape>& inputs)
     if(inputs.empty())
         MIGRAPHX_THROW("At least one input is required for " + x.name());
     dependent_type<operation, T> y = x;
-    normalize_attributes(y, inputs[0].max_lens());
+    normalize_attributes(y, inputs[0]);
     return any_cast<T>(y).normalize_compute_shape(inputs);
 }
 
@@ -251,9 +251,10 @@ auto compute_op(rank<1>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f)
-    -> decltype(
-        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
+                F f) -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
     return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }
@@ -309,9 +310,10 @@ auto compute_op(rank<3>,
                 const shape& output,
                 const std::vector<argument>& inputs,
                 const std::vector<module_ref>& module_args,
-                F f)
-    -> decltype(
-        x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f))
+                F f) -> decltype(x.compute(make_compute_output_shape(pack(x, output, inputs)),
+                                           inputs,
+                                           module_args,
+                                           f))
 {
     return x.compute(make_compute_output_shape(pack(x, output, inputs)), inputs, module_args, f);
 }

src/include/migraphx/operators.hpp

@@ -45,9 +45,10 @@
 #include <migraphx/op/contiguous.hpp>
 #include <migraphx/op/convert.hpp>
 #include <migraphx/op/convolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <migraphx/op/cosh.hpp>
 #include <migraphx/op/cos.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/dimensions_of.hpp>
 #include <migraphx/op/div.hpp>
 #include <migraphx/op/dot.hpp>
 #include <migraphx/op/elu.hpp>

src/include/migraphx/shape.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
@@ -240,6 +240,10 @@ struct MIGRAPHX_EXPORT shape
     template <class Iterator>
     std::size_t index(Iterator start, Iterator last) const
     {
+        if(this->dynamic())
+        {
+            MIGRAPHX_THROW("SHAPE: index() called on dynamic shape");
+        }
         assert(std::distance(start, last) <= this->lens().size());
         assert(this->lens().size() == this->strides().size());
         return std::inner_product(start, last, this->strides().begin(), std::size_t{0}); // NOLINT

src/include/migraphx/verify.hpp

@@ -35,6 +35,7 @@
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
+namespace verify {
 
 // Compute the value of a range
 template <class R>
@@ -196,6 +197,7 @@ bool verify_range(const R1& r1, const R2& r2, double tolerance = 80, double* out
     return error <= threshold;
 }
 
+} // namespace verify
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 #endif

src/instruction.cpp

@@ -467,7 +467,7 @@ operation instruction::normalized_operator() const
     if(this->need_normalization())
     {
         auto s = this->inputs().front()->get_shape();
-        if(not normalize_attributes(o, s.max_lens()))
+        if(not normalize_attributes(o, s))
             return this->get_operator();
     }
     return o;

src/normalize_attributes.cpp

 /*
  * 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
@@ -35,8 +35,9 @@ inline namespace MIGRAPHX_INLINE_NS {
  * vec: the vector attribute to normalize
  * axes: the operator's axes attribute if it exists, empty otherwise
  * val: the normalize_axes key and options. Ex: normalize["axes"] =
- * value::array{normalize_attribute::include_min}; lens: shape dimensions passed when calling
- * normalize_attributes(op&, lens)
+ * value::array{normalize_attribute::include_min};
+ * input_shape: input shape passed when calling
+ * normalize_attributes(op&, input_shape)
  *
  * See normalize_attribute.hpp for explaining the options.
  */
@@ -44,11 +45,11 @@ template <class Message>
 auto tune_attribute(const std::vector<int64_t>& vec,
                     const std::vector<int64_t>& axes,
                     const value& val,
-                    const std::vector<std::size_t>& lens,
+                    const shape& input_shape,
                     Message m)
 {
     std::vector<int64_t> result(vec);
-    int64_t n_rank                                 = lens.size();
+    int64_t n_rank                                 = input_shape.ndim();
     std::vector<op::normalize_attribute> vec_attrs = val.to_vector<op::normalize_attribute>();
     if(contains(vec_attrs, op::normalize_attribute::use_output))
     {
@@ -56,9 +57,28 @@ auto tune_attribute(const std::vector<int64_t>& vec,
     }
 
     std::vector<int64_t> max_vals(vec.size(), n_rank);
+
     if(contains(vec_attrs, op::normalize_attribute::use_len))
     {
-        std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) { return lens[i]; });
+        if(input_shape.dynamic())
+        {
+            std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
+                const auto& dd = input_shape.dyn_dims().at(i);
+                if(not dd.is_fixed())
+                {
+                    MIGRAPHX_THROW(
+                        "NORMALIZE_ATTR: 'use_lens' on a non-fixed dynamic dimension, axis=" +
+                        std::to_string(i));
+                }
+                return dd.max;
+            });
+        }
+        else
+        {
+            std::transform(axes.begin(), axes.end(), max_vals.begin(), [&](auto i) {
+                return input_shape.lens().at(i);
+            });
+        }
     }
 
     if(contains(vec_attrs, op::normalize_attribute::clip_max))
@@ -159,9 +179,9 @@ auto tune_pad_attribute(const value& val)
 /**
  * Assumptions:
  *  Dimensions to pad start from the third dimension (index 2).
- *  Called by compute_shape_op() with the `lens` of the first input.
+ *  Called by compute_shape_op() with the shape of the first input.
  */
-bool normalize_attributes(operation& op, const std::vector<std::size_t>& lens)
+bool normalize_attributes(operation& op, const shape& input_shape)
 {
     bool tuned = false;
     auto attrs = op.attributes();
@@ -172,9 +192,9 @@ bool normalize_attributes(operation& op, const std::vector<std::size_t>& lens)
         auto padding_size  = padding.size();
         auto padding_start = 2;
 
-        if(padding_size == 2 * (lens.size() - padding_start))
+        if(padding_size == 2 * (input_shape.ndim() - padding_start))
             tuned = true;
-        else if(padding_size != (lens.size() - padding_start))
+        else if(padding_size != (input_shape.ndim() - padding_start))
             MIGRAPHX_THROW("inconsistent padding size");
         else
         {
@@ -205,7 +225,7 @@ bool normalize_attributes(operation& op, const std::vector<std::size_t>& lens)
                     axes = val.at("axes").without_key().to_vector<int64_t>();
                 }
                 auto vec    = vv.to_vector<int64_t>();
-                auto result = tune_attribute(vec, axes, rv.without_key(), lens, message);
+                auto result = tune_attribute(vec, axes, rv.without_key(), input_shape, message);
                 val[key]    = result;
                 op.from_value(val);
                 val   = op.to_value();
@@ -214,7 +234,7 @@ bool normalize_attributes(operation& op, const std::vector<std::size_t>& lens)
             else
             {
                 auto num    = vv.to<int64_t>();
-                auto result = tune_attribute({num}, {num}, rv.without_key(), lens, message);
+                auto result = tune_attribute({num}, {num}, rv.without_key(), input_shape, message);
                 val[key]    = result.front();
                 op.from_value(val);
                 val   = op.to_value();

src/normalize_ops.cpp

@@ -45,7 +45,7 @@ void normalize_ops::apply(module& m) const
 
         auto s                       = inputs[0]->get_shape();
         migraphx::operation tuned_op = ins->get_operator();
-        if(normalize_attributes(tuned_op, s.max_lens()))
+        if(normalize_attributes(tuned_op, s))
         {
             m.replace_instruction(ins, tuned_op, inputs);
             ins->set_normalized();

src/onnx/parse_batchnorm.cpp

@@ -57,13 +57,12 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
         auto x_rank = x_lens.size();
         if(x_rank == 1 or x_rank == 2)
         {
-            auto rt      = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
-            auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
-            auto numer   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
-            auto var_eps = info.add_broadcastable_binary_op("add", args[4], eps);
-            auto denom   = info.add_broadcastable_binary_op("pow", var_eps, rt);
-            auto div0    = info.add_broadcastable_binary_op("div", numer, denom);
-            auto r0      = info.add_broadcastable_binary_op("mul", div0, args[1]);
+            auto eps = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
+            auto x_sub_mean = info.add_broadcastable_binary_op("sub", args[0], args[3]);
+            auto var_eps    = info.add_broadcastable_binary_op("add", args[4], eps);
+            auto rsqrt      = info.add_instruction(make_op("rsqrt"), var_eps);
+            auto mul0       = info.add_broadcastable_binary_op("mul", args[1], rsqrt);
+            auto r0         = info.add_broadcastable_binary_op("mul", x_sub_mean, mul0);
             return info.add_broadcastable_binary_op("add", r0, args[2]);
         }
         else if(x_rank > 2)
@@ -71,7 +70,6 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
             // unsqueeze tensors of shape (C) to broadcast correctly
             std::vector<int64_t> unsqueeze_axes(x_lens.size() - 2);
             std::iota(unsqueeze_axes.begin(), unsqueeze_axes.end(), 1);
-            auto rt  = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
             auto eps = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
             auto scale_unsqueeze = info.add_instruction(
                 migraphx::make_op("unsqueeze", {{"axes", unsqueeze_axes}}), args[1]);
@@ -81,11 +79,11 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
                 migraphx::make_op("unsqueeze", {{"axes", unsqueeze_axes}}), args[3]);
             auto var_unsqueeze = info.add_instruction(
                 migraphx::make_op("unsqueeze", {{"axes", unsqueeze_axes}}), args[4]);
-            auto numer   = info.add_broadcastable_binary_op("sub", args[0], mean_unsqueeze);
-            auto var_eps = info.add_broadcastable_binary_op("add", var_unsqueeze, eps);
-            auto denom   = info.add_broadcastable_binary_op("pow", var_eps, rt);
-            auto div0    = info.add_broadcastable_binary_op("div", numer, denom);
-            auto r0      = info.add_broadcastable_binary_op("mul", div0, scale_unsqueeze);
+            auto x_sub_mean = info.add_broadcastable_binary_op("sub", args[0], mean_unsqueeze);
+            auto var_eps    = info.add_broadcastable_binary_op("add", var_unsqueeze, eps);
+            auto rsqrt      = info.add_instruction(make_op("rsqrt"), var_eps);
+            auto mul0       = info.add_broadcastable_binary_op("mul", scale_unsqueeze, rsqrt);
+            auto r0         = info.add_broadcastable_binary_op("mul", x_sub_mean, mul0);
             return info.add_broadcastable_binary_op("add", r0, bias_unsqueeze);
         }
         else

src/onnx/parse_deconvolution.cpp → src/onnx/parse_conv_transpose.cpp

@@ -42,7 +42,7 @@ std::vector<int64_t> to_int64_vector(const std::vector<T>& input_vector)
     return output_vector;
 }
 
-struct parse_deconvolution : op_parser<parse_deconvolution>
+struct parse_conv_transpose : op_parser<parse_conv_transpose>
 {
     std::vector<op_desc> operators() const { return {{"ConvTranspose"}}; }
 
@@ -51,17 +51,15 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                           onnx_parser::node_info info,
                           std::vector<instruction_ref> args) const
     {
-        operation op = make_op("deconvolution");
+        operation op = make_op("convolution_backwards");
         value values = op.to_value();
-        // op::deconvolution op;
-        auto l0 = args[0];
+        auto l0      = args[0];
         std::vector<std::int64_t> padding;
         bool asym_padding = false;
-        auto in_lens      = l0->get_shape().lens();
-        assert(in_lens.size() > 2);
-        auto kdims = in_lens.size() - 2;
+        assert(l0->get_shape().ndim() > 2);
+        auto kdims = l0->get_shape().ndim() - 2;
 
-        // ensure pads availabe only when auto_pad is "NOT_SET"
+        // ensure pads available only when auto_pad is "NOT_SET"
         check_padding_mode(info, "CONV_TRANSPOSE");
 
         if(contains(info.attributes, "pads"))
@@ -70,9 +68,9 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
 
             asym_padding = is_asym_padding(padding);
 
+            size_t pad_ndims = padding.size() / 2;
             if(not asym_padding)
             {
-                size_t pad_ndims = padding.size() / 2;
                 check_attr_sizes(kdims, pad_ndims, "PARSE_CONV_TRANSPOSE: inconsistent paddings");
                 values["padding"].clear();
                 std::transform(padding.begin(),
@@ -80,7 +78,19 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                                std::back_inserter(values["padding"]),
                                [](auto pad_val) { return pad_val; });
             }
+            else if(l0->get_shape().dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: asymmetric padding (padding_L != padding_R) "
+                               "not supported with dynamic shapes");
+            }
+            else
+            {
+                // set padding to 0s, asym_padding handled by parser with slice
+                // TODO changing parser and op to do asym padding in op
+                values["padding"] = std::vector<std::size_t>(pad_ndims, 0);
+            }
         }
+
         if(contains(info.attributes, "strides"))
         {
             values["stride"].clear();
@@ -88,6 +98,7 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             check_attr_sizes(
                 kdims, values["stride"].size(), "PARSE_CONV_TRANSPOSE: inconsistent strides");
         }
+
         if(contains(info.attributes, "dilations"))
         {
             values["dilation"].clear();
@@ -97,21 +108,10 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
         }
 
         // TODO: auto padding needs to be implemented for this parser and operator
-        if(contains(info.attributes, "auto_pad"))
+        if(contains(info.attributes, "auto_pad") and
+           to_upper(info.attributes.at("auto_pad").s()) != "NOTSET")
         {
-            auto s = info.attributes["auto_pad"].s();
-            if(contains(info.attributes, "pads") and to_upper(s) != "NOTSET")
-            {
-                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: auto_pad and padding cannot be specified "
-                               "simultaneously");
-            }
-
-            if(s.find("SAME") != std::string::npos)
-            {
-                bool is_same_upper     = (s.find("SAME_UPPER") != std::string::npos);
-                values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
-                                                       : to_value(op::padding_mode_t::same_lower);
-            }
+            MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: auto padding not supported");
         }
 
         if(contains(info.attributes, "group"))
@@ -122,11 +122,11 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
         recalc_conv_attributes(values, kdims);
 
         op.from_value(values);
-        auto l1                   = info.add_instruction(op, l0, args[1]);
-        std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
-        std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
+        auto l1 = info.add_instruction(op, l0, args[1]);
         if(asym_padding)
         {
+            std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
+            std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
             std::vector<int64_t> axes(kdims);
             std::iota(axes.begin(), axes.end(), 2); // ignore first 2 dims
 
@@ -144,9 +144,11 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
                 make_op("slice", {{"axes", axes}, {"starts", starts}, {"ends", ends}}), l1);
         }
 
-        if(contains(info.attributes, "output_padding"))
+        // TODO, should check output_padding < (strides or dilations)
+        if(contains(info.attributes, "output_padding") and
+           not contains(info.attributes, "output_shape"))
         {
-            size_t non_kdims = dims.size() * 2 - kdims;
+            size_t non_kdims = l1->get_shape().ndim() * 2 - kdims;
             std::vector<int64_t> output_padding(non_kdims, 0);
             copy(info.attributes["output_padding"].ints(), std::back_inserter(output_padding));
             check_attr_sizes(kdims,
@@ -155,14 +157,21 @@ struct parse_deconvolution : op_parser<parse_deconvolution>
             l1 = info.add_instruction(make_op("pad", {{"pads", output_padding}}), l1);
         }
 
+        // TODO, doing unnecessary calcuations with this. Could instead
+        // calculate the padding to conv_transpose that would give the output_shape.
         if(contains(info.attributes, "output_shape"))
         {
+            if(l1->get_shape().dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_CONV_TRANSPOSE: output_shape attribute and dynamic shapes "
+                               "not supported");
+            }
+            std::vector<int64_t> dims = to_int64_vector(l1->get_shape().lens());
+            std::vector<int64_t> curr_shape(dims.begin() + 2, dims.end());
             std::vector<int64_t> output_shape;
             copy(info.attributes["output_shape"].ints(), std::back_inserter(output_shape));
             check_attr_sizes(
                 kdims, output_shape.size(), "PARSE_CONV_TRANSPOSE: inconsistent output shape");
-            dims = to_int64_vector(l1->get_shape().lens());
-            copy(dims.begin() + 2, dims.end(), curr_shape.begin());
             if(curr_shape != output_shape)
             {
                 std::vector<int64_t> target_padding(dims.size() * 2 - kdims, 0);

src/onnx/parse_instancenorm.cpp

@@ -79,13 +79,11 @@ struct parse_instancenorm : op_parser<parse_instancenorm>
         auto x     = args[0];
         auto scale = args[1];
         auto bias  = args[2];
-        auto dims  = x->get_shape().lens();
         if(not contains(valid_types, dtype))
             MIGRAPHX_THROW(opd.op_name + ": invalid output type: " + std::to_string(dtype) +
                            ". Valid types are 1 (float), 10 (half), and 11 (double).");
 
-        bool dyn_input = x->get_shape().dynamic();
-        auto ndims     = x->get_shape().ndim();
+        auto ndims = x->get_shape().ndim();
         assert(ndims >= 2);
         auto kdims = ndims - 2;
         std::vector<int64_t> axes(kdims);
@@ -102,6 +100,12 @@ struct parse_instancenorm : op_parser<parse_instancenorm>
             (dtype == shape::half_type and not convert_fp16) ? "reduce_sum" : "reduce_mean";
         if(dtype == shape::half_type and not convert_fp16)
         {
+            if(x->get_shape().dynamic())
+            {
+                MIGRAPHX_THROW("PARSE_INSTANCENORM: half type not supported with dynamic shape "
+                               "unless convert_fp16 is TRUE");
+            }
+            auto dims = x->get_shape().lens();
             double n =
                 std::accumulate(dims.begin() + 2, dims.end(), 1, [&](const auto& i, const auto& j) {
                     return i * j;
@@ -122,13 +126,14 @@ struct parse_instancenorm : op_parser<parse_instancenorm>
         // both scale and bias.
         instruction_ref scale_bcast;
         instruction_ref bias_bcast;
-        if(dyn_input)
+        if(x->get_shape().dynamic())
         {
             scale_bcast = info.add_instruction(make_op("broadcast", {{"axis", 1}}), scale, x);
             bias_bcast  = info.add_instruction(make_op("broadcast", {{"axis", 1}}), bias, x);
         }
         else
         {
+            auto dims   = x->get_shape().lens();
             scale_bcast = info.add_instruction(
                 make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), scale);
             bias_bcast =

src/onnx/parse_shape.cpp

@@ -30,8 +30,11 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace onnx {
 
-// Use a literal instruction to replace the shape since, output of
-// shape operator are literals in migraphx
+/**
+ * If static shape input, creates a literal in migraphx.
+ * If dynamic shape input, creates a dimensions_of operator in migraphx (runtime evaluation of
+ * shape).
+ */
 struct parse_shape : op_parser<parse_shape>
 {
     std::vector<op_desc> operators() const { return {{"Shape"}}; }
@@ -43,13 +46,54 @@ struct parse_shape : op_parser<parse_shape>
     {
         if(args.size() != 1)
             MIGRAPHX_THROW("Shape: operator should have 1 operand");
-        std::vector<std::size_t> arg_shape = args[0]->get_shape().lens();
-        std::vector<int64_t> vec_shape(arg_shape.size());
-        migraphx::shape s(migraphx::shape::int64_type, {arg_shape.size()});
-        std::transform(arg_shape.begin(), arg_shape.end(), vec_shape.begin(), [](auto i) {
-            return int64_t(i);
-        });
-        return info.add_literal(migraphx::literal{s, vec_shape});
+        auto input_shape  = args[0]->get_shape();
+        int input_ndim    = input_shape.ndim();
+        std::size_t start = 0;
+        std::size_t end   = input_ndim;
+        // Normalizing the start and end is handled here because of how the static shape version
+        // works. Clamping to [-r, r], where r is ndim of input and then making positive.
+        auto normalize_ind = [&](int64_t ind) {
+            if(ind < (-1 * input_ndim))
+            {
+                ind = -1 * input_ndim;
+            }
+            if(ind > input_ndim)
+            {
+                ind = input_ndim;
+            }
+            return (ind >= 0) ? ind : input_ndim + ind;
+        };
+        if(contains(info.attributes, "end"))
+        {
+            end = normalize_ind(info.attributes.at("end").i());
+        }
+        if(contains(info.attributes, "start"))
+        {
+            start = normalize_ind(info.attributes.at("start").i());
+        }
+        if(end <= start)
+        {
+            MIGRAPHX_THROW("PARSE_SHAPE: ending axis <= starting axis, end: " +
+                           std::to_string(end) + " start: " + std::to_string(start));
+        }
+
+        if(input_shape.dynamic())
+        {
+            return info.add_instruction(make_op("dimensions_of", {{"start", start}, {"end", end}}),
+                                        args[0]);
+        }
+        else
+        {
+            std::size_t output_ndim = end - start;
+            std::vector<int64_t> vec_shape(output_ndim);
+            migraphx::shape s(migraphx::shape::int64_type, {output_ndim});
+            std::vector<std::size_t> input_lens = input_shape.lens();
+            std::transform(input_lens.begin() + start,
+                           input_lens.begin() + end,
+                           vec_shape.begin(),
+                           [](auto i) { return int64_t(i); });
+            return info.add_literal(migraphx::literal{s, vec_shape});
+        }
     }
 };
 

src/quantization.cpp

@@ -29,6 +29,7 @@
 #include <migraphx/simplify_reshapes.hpp>
 #include <migraphx/simplify_qdq.hpp>
 #include <migraphx/eliminate_common_subexpression.hpp>
+#include <migraphx/optimize_module.hpp>
 #include <migraphx/dead_code_elimination.hpp>
 #include <migraphx/program.hpp>
 #include <migraphx/instruction.hpp>
@@ -48,19 +49,12 @@ MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_INT8_QUANTIZATION_PARAMS)
 
 // This function is to convert any instructions specified in the input
 // from double or float to float16 by inserting a convert operator.
-// For the conversion, there could be cases of overflowing, but it
-// is very rare in the area of deeping learning, so we just do a
-// truncate of the input to get the fp16.
+// For the conversion, there could be cases of overflowing or underflowing, but it
+// is uncommon. Run optimize_module() before converting to fp16 to const eval and fold in FP32 to
+// avoid loss of precision.
 void quantize_fp16(program& prog, const std::vector<std::string>& ins_names)
 {
-    run_passes(prog,
-               {quantize_fp16_pass{ins_names},
-                eliminate_common_subexpression{},
-                dead_code_elimination{},
-                simplify_reshapes{},
-                dead_code_elimination{},
-                simplify_qdq{},
-                dead_code_elimination{}});
+    run_passes(prog, {optimize_module{}, quantize_fp16_pass{ins_names}, optimize_module{}});
 }
 
 void quantize_int8(program& prog,

src/shape.cpp

 /*
  * 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
@@ -273,9 +273,23 @@ shape shape::from_permutation(type_t t,
 
 shape::type_t shape::type() const { return impl->m_type; }
 
-const std::vector<std::size_t>& shape::lens() const { return impl->m_lens; }
+const std::vector<std::size_t>& shape::lens() const
+{
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: lens() called on a dynamic shape");
+    }
+    return impl->m_lens;
+}
 
-const std::vector<std::size_t>& shape::strides() const { return impl->m_strides; }
+const std::vector<std::size_t>& shape::strides() const
+{
+    if(this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: strides() called on a dynamic shape");
+    }
+    return impl->m_strides;
+}
 
 std::size_t shape::ndim() const
 {
@@ -535,7 +549,14 @@ bool shape::any_of_dynamic() const
     });
 }
 
-const std::vector<shape::dynamic_dimension>& shape::dyn_dims() const { return impl->m_dyn_dims; }
+const std::vector<shape::dynamic_dimension>& shape::dyn_dims() const
+{
+    if(not this->dynamic())
+    {
+        MIGRAPHX_THROW("SHAPE: dyn_dims() called on a static shape");
+    }
+    return impl->m_dyn_dims;
+}
 
 std::vector<std::size_t> shape::min_lens() const
 {
@@ -679,12 +700,22 @@ const std::vector<shape>& shape::sub_shapes() const { return impl->m_shapes; }
 void migraphx_to_value(value& v, const shape& s)
 {
     value result;
-    result["type"]               = migraphx::to_value(s.type_string());
-    result["lens"]               = migraphx::to_value(s.lens());
-    result["strides"]            = migraphx::to_value(s.strides());
-    result["sub_shapes"]         = migraphx::to_value(s.sub_shapes());
-    result["dynamic_dimensions"] = migraphx::to_value(s.dyn_dims());
-    v                            = result;
+    result["type"]       = migraphx::to_value(s.type_string());
+    result["sub_shapes"] = migraphx::to_value(s.sub_shapes());
+    // avoid calling functions that will throw
+    if(s.dynamic())
+    {
+        result["lens"]               = {};
+        result["strides"]            = {};
+        result["dynamic_dimensions"] = migraphx::to_value(s.dyn_dims());
+    }
+    else
+    {
+        result["lens"]               = migraphx::to_value(s.lens());
+        result["strides"]            = migraphx::to_value(s.strides());
+        result["dynamic_dimensions"] = {};
+    }
+    v = result;
 }
 
 void migraphx_from_value(const value& v, shape& s)

src/simplify_reshapes.cpp

@@ -89,38 +89,23 @@ struct find_reshaper
 {
     auto matcher() const
     {
-        return match::name(reshaper_names())(
-            match::any_of[match::outputs()](match::name(reshaper_names())));
+        auto reshaper          = match::name(reshaper_names());
+        auto contiguous        = match::name("contiguous");
+        auto no_output_reshape = match::none_of[match::outputs()](reshaper);
+        auto input_reshape     = match::arg(0)(match::skip(contiguous)(reshaper));
+        auto input             = match::skip(reshaper, contiguous)(match::any().bind("x"));
+        return reshaper(no_output_reshape, input_reshape, input);
     }
 
     void apply(module& m, const match::matcher_result& mr) const
     {
-        auto ins = mr.result;
-        std::vector<instruction_ref> reshapes{ins};
-        while(is_reshaper(reshapes.back()))
-        {
-            assert(not reshapes.back()->inputs().empty());
-            assert(m.has_instruction(reshapes.back()->inputs().front()));
-            auto input = reshapes.back()->inputs().front();
-            reshapes.push_back(input);
-        }
+        auto ins   = mr.result;
+        auto input = mr.instructions["x"];
+        auto dims  = ins->get_shape().lens();
 
-        std::pair<instruction_ref, instruction_ref> r{m.end(), m.end()};
-        for(auto start : iterator_for(reshapes))
-        {
-            auto last = std::find_if(reshapes.rbegin(), reshapes.rend(), [&](auto&& i) {
-                return i->get_shape() == (*start)->get_shape() and i != (*start);
-            });
-            if(last != reshapes.rend())
-            {
-                r = std::make_pair(*start, *last);
-                break;
-            }
-        }
-        if(r.first != r.second)
-        {
-            m.replace_instruction(r.first, r.second);
-        }
+        if(not input->get_shape().standard())
+            input = m.insert_instruction(ins, make_op("contiguous"), input);
+        m.replace_instruction(ins, make_op("reshape", {{"dims", dims}}), input);
     }
 };
 
@@ -804,9 +789,9 @@ void simplify_reshapes::apply(module& m) const
         match::find_matches(m,
                             find_where_op{},
                             find_resize{},
-                            find_reshape_cont{},
                             find_nop_reshapes{},
                             find_reshaper{},
+                            find_reshape_cont{},
                             find_transpose{},
                             find_concat_transpose{},
                             find_concat_multibroadcasts{},

src/targets/cpu/deconvolution.cpp

@@ -23,14 +23,14 @@
  */
 #include <migraphx/config.hpp>
 #include <migraphx/cpu/dnnl.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace cpu {
 
 struct dnnl_deconvolution
-    : dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::deconvolution>
+    : dnnl_extend_op<dnnl_deconvolution, dnnl::deconvolution_forward, op::convolution_backwards>
 {
     std::vector<int> arg_map(int) const
     {

src/targets/cpu/lowering.cpp

@@ -27,7 +27,7 @@
 #include <migraphx/dfor.hpp>
 #include <migraphx/op/identity.hpp>
 #include <migraphx/op/convolution.hpp>
-#include <migraphx/op/deconvolution.hpp>
+#include <migraphx/op/convolution_backwards.hpp>
 #include <migraphx/op/quant_convolution.hpp>
 #include <migraphx/op/dot.hpp>
 #include <migraphx/op/quant_dot.hpp>
@@ -345,7 +345,7 @@ struct cpu_apply
         extend_op("contiguous", "dnnl::reorder");
         extend_op("convolution", "dnnl::convolution");
 #ifndef MIGRAPHX_ENABLE_ZENDNN
-        extend_op("deconvolution", "dnnl::deconvolution");
+        extend_op("convolution_backwards", "dnnl::convolution_backwards");
         extend_op("dot", "dnnl::dot");
 #endif
         extend_op("erf", "cpu::erf");

src/targets/gpu/CMakeLists.txt

@@ -176,7 +176,7 @@ register_op(migraphx_gpu
     OPERATORS gpu::rocblas_gemm<op::dot> gpu::rocblas_gemm<op::quant_dot>
     INCLUDES migraphx/gpu/context.hpp)
 register_op(migraphx_gpu HEADER migraphx/gpu/convolution.hpp
-    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::deconvolution> gpu::miopen_convolution<op::quant_convolution>
+    OPERATORS gpu::miopen_convolution<op::convolution> gpu::miopen_convolution<op::convolution_backwards> gpu::miopen_convolution<op::quant_convolution>
     INCLUDES migraphx/gpu/context.hpp)
 rocm_set_soversion(migraphx_gpu ${MIGRAPHX_SO_VERSION})
 rocm_clang_tidy_check(migraphx_gpu)
@@ -188,7 +188,9 @@ if(MIGRAPHX_ENABLE_MLIR)
     find_package(rocMLIR 1.0.0 CONFIG REQUIRED)
     message(STATUS "Build with rocMLIR::rockCompiler ${rocMLIR_VERSION}")
     target_compile_definitions(migraphx_gpu PRIVATE "-DMIGRAPHX_MLIR")
-    target_link_libraries(migraphx_gpu PUBLIC rocMLIR::rockCompiler)
+    # Make this private to avoid multiple inclusions of LLVM symbols.
+    # TODO: Fix rocMLIR's library to hide LLVM internals.
+    target_link_libraries(migraphx_gpu PRIVATE rocMLIR::rockCompiler)
 endif()
 
 if(MIGRAPHX_USE_HIPRTC)
@@ -234,7 +236,12 @@ check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_
 set(MIGRAPHX_USE_FIND_2_API "${HAS_FIND_2_API}" CACHE BOOL "")
 
 if(MIGRAPHX_USE_FIND_2_API)
-    target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
+    check_library_exists(MIOpen "miopenSetFindOptionPreallocatedTensor" "${MIOPEN_LOCATION}" HAS_PREALLOCATION_API)
+    if(HAS_PREALLOCATION_API)
+        target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API -DMIGRAPHX_PREALLOCATE_MIOPEN_BUFFERS)
+    else()
+        target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
+    endif()
     message(STATUS "MIGraphx is using Find-2.0 API of MIOpen")
 else()
     message(STATUS "MIGraphx is using legacy Find API in MIOpen")

src/targets/gpu/compile_hip_code_object.cpp

@@ -135,14 +135,13 @@ compute_global_for(context& ctx, std::size_t n, std::size_t over)
     std::size_t max_global = ctx.get_current_device().get_cu_count() *
                              ctx.get_current_device().get_max_workitems_per_cu();
     return [n, over, max_global](std::size_t local) {
-        std::size_t num_elements = n;
+        // hip require global workitems multiple of local workitems. It may degrade performance.
+        // [TODO]: consider adding "fno-hip-uniform-block" flag when it becomes available.
+        // https://reviews.llvm.org/D155213
+        std::size_t num_elements = ((n + local - 1) / local) * local;
         std::size_t groups       = (num_elements + local - 1) / local;
         std::size_t max_blocks   = max_global / local;
         std::size_t nglobal      = std::min(max_blocks * over, groups) * local;
-#ifdef MIGRAPHX_USE_HIPRTC
-        if(enabled(MIGRAPHX_ENABLE_HIPRTC_WORKAROUNDS{}))
-            num_elements = ((num_elements + local - 1) / local) * local;
-#endif
         return std::min(nglobal, num_elements);
     };
 }

src/targets/gpu/compile_miopen.cpp

@@ -79,7 +79,7 @@ void compile_miopen::apply(module& m) const
         std::size_t ws = 0;
         try
         {
-            // for the regular convolution and deconvolution, this try would always succeed
+            // for the regular convolution and convolution_backwards, this try would always succeed
             ws = compile(op, ins, int8_x4_format);
         }
         catch(migraphx::exception&)