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

src/onnx/parse_split.cpp

@@ -26,6 +26,9 @@
 #include <migraphx/ranges.hpp>
 #include <migraphx/make_op.hpp>
 #include <migraphx/tune_axis.hpp>
+#include <migraphx/onnx/checks.hpp>
+
+#include <migraphx/stringutils.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -55,12 +58,12 @@ struct parse_split : op_parser<parse_split>
         {
             literal s = parser.parse_value(info.attributes.at("split"));
             s.visit([&](auto v) { vec_splits.assign(v.begin(), v.end()); });
-
-            if(std::accumulate(vec_splits.begin(), vec_splits.end(), int64_t(0)) !=
-               static_cast<int64_t>(lens[tuned_axis]))
-            {
-                MIGRAPHX_THROW("PARSE_SPLIT: sum of split attribute unequal to dim size of axis!");
-            }
+        }
+        else if(args.size() == 2)
+        {
+            auto s = args[1]->eval();
+            check_arg_empty(s, "Split: dynamic shape is not supported");
+            s.visit([&](auto v) { vec_splits.assign(v.begin(), v.end()); });
         }
         // no split attribute, input is equally divided
         else
@@ -74,6 +77,15 @@ struct parse_split : op_parser<parse_split>
             vec_splits.resize(info.num_outputs, dl);
         }
 
+        if(std::accumulate(vec_splits.begin(), vec_splits.end(), int64_t(0)) !=
+           static_cast<int64_t>(lens[tuned_axis]))
+        {
+            MIGRAPHX_THROW(
+                "PARSE_SPLIT: sum of split attribute unequal to dim size of axis! tuned axis:" +
+                std::to_string(lens[tuned_axis]) + " Output " + to_string_range(vec_splits) +
+                " Rank " + std::to_string(n_rank) + " Len outs " + to_string_range(lens));
+        }
+
         std::vector<instruction_ref> ret_ins;
         int64_t start = 0;
         for(auto sl : vec_splits)

src/onnx/parse_transpose.cpp

@@ -47,7 +47,7 @@ struct parse_transpose : op_parser<parse_transpose>
         }
 
         // if perm is empty, use the default value
-        auto n_dim = args.front()->get_shape().lens().size();
+        auto n_dim = args.front()->get_shape().ndim();
         if(perm.empty())
         {
             perm.resize(n_dim);

src/pass_manager.cpp

@@ -94,11 +94,19 @@ struct module_pm : module_pass_manager
     virtual void run_pass(const pass& p) override
     {
         assert(mod);
+
+        timer ts{};
+        using seconds = std::chrono::duration<double>;
+
         trace("Module: ", mod->name(), ", Pass: ", p.name());
+        const double t1 = ts.record<seconds>();
         assert(mod->validate() == mod->end());
         p.apply(*this);
         trace(*mod);
         validate_pass(*mod, p, *t);
+
+        const double t2 = ts.record<seconds>();
+        trace("Pass: ", p.name(), " completed in (s): ", (t2 - t1));
     }
 };
 

src/targets/cpu/binary.cpp

@@ -51,7 +51,18 @@ struct dnnl_binary : dnnl_op<dnnl_binary, dnnl::binary>
         auto r  = s0;
         if(s0 != s1 or not s0.packed())
         {
-            r = shape{s0.type(), s0.lens()};
+            if(s0.packed() != s1.packed())
+            {
+                r = s0.packed() ? s0 : s1;
+            }
+            else if(s0.broadcasted() != s1.broadcasted())
+            {
+                r = s0.broadcasted() ? s1.with_lens(s0.lens()) : s0.with_lens(s0.lens());
+            }
+            else
+            {
+                r = {s0.type(), s0.lens()};
+            }
         }
         // Call to get_primitive to make sure an algo is available
         this->get_primitive(this->to_memory_desc(r, inputs));

src/targets/cpu/convolution.cpp

@@ -43,9 +43,9 @@ struct dnnl_convolution
         return {MIGRAPHX_DNNL_PREFIX(ARG_SRC), MIGRAPHX_DNNL_PREFIX(ARG_WEIGHTS)};
     }
 
-    shape adjust_shape(const shape& x, int i) const
+    shape adjust_shape(const shape& x, int i, const shape& output) const
     {
-        auto s = base_adjust_shape(x);
+        auto s = base_adjust_shape(x, output);
         if(i == 1 and op.group > 1)
         {
             // TODO: Add support for transposed weights

src/targets/cpu/deconvolution.cpp

@@ -37,9 +37,9 @@ struct dnnl_deconvolution
         return {MIGRAPHX_DNNL_PREFIX(ARG_SRC), MIGRAPHX_DNNL_PREFIX(ARG_WEIGHTS)};
     }
 
-    shape adjust_shape(const shape& x, int i) const
+    shape adjust_shape(const shape& x, int i, const shape& output) const
     {
-        auto s = base_adjust_shape(x);
+        auto s = base_adjust_shape(x, output);
         if(i == 1)
         {
             // The input and output channels are flipped for dnnl

src/targets/cpu/include/migraphx/cpu/dnnl.hpp

@@ -167,7 +167,7 @@ struct dnnl_op : auto_register_op<Derived>
         std::iota(result.begin(), result.end(), MIGRAPHX_DNNL_PREFIX(ARG_SRC_0));
         return result;
     }
-    shape base_adjust_shape(const shape& s) const
+    shape base_adjust_shape(const shape& s, const shape& output) const
     {
         if(s.broadcasted())
         {
@@ -183,7 +183,8 @@ struct dnnl_op : auto_register_op<Derived>
                                else
                                    return len;
                            });
-            return shape{s.type(), lens};
+            // Use the permutation of the output
+            return output.with_lens(s.type(), lens);
         }
         return s;
     }
@@ -204,7 +205,10 @@ struct dnnl_op : auto_register_op<Derived>
             i++;
         }
     }
-    shape adjust_shape(const shape& s, int) const { return base_adjust_shape(s); }
+    shape adjust_shape(const shape& s, int, const shape& output) const
+    {
+        return base_adjust_shape(s, output);
+    }
     std::vector<int> create_arg_map(std::size_t input_size) const
     {
         const auto& self     = static_cast<const Derived&>(*this);
@@ -224,12 +228,12 @@ struct dnnl_op : auto_register_op<Derived>
         const auto& self = static_cast<const Derived&>(*this);
         std::unordered_map<int, dnnl::memory::desc> result;
         result[MIGRAPHX_DNNL_PREFIX(ARG_DST)] =
-            to_dnnl_memory_desc(self.adjust_shape(output_shape, inputs.size()));
+            to_dnnl_memory_desc(self.adjust_shape(output_shape, inputs.size(), output_shape));
         auto m = create_arg_map(inputs.size());
         assert(m.size() >= inputs.size());
         for(int i = 0; i < inputs.size(); i++)
         {
-            result[m[i]] = to_dnnl_memory_desc(self.adjust_shape(inputs[i], i));
+            result[m[i]] = to_dnnl_memory_desc(self.adjust_shape(inputs[i], i, output_shape));
         }
         return result;
     }

src/targets/cpu/reorder.cpp

@@ -32,7 +32,7 @@ struct dnnl_reorder : dnnl_op<dnnl_reorder, dnnl::reorder>
 {
     std::string name() const { return "dnnl::reorder"; }
 
-    shape adjust_shape(const shape& x, int) const { return x; }
+    shape adjust_shape(const shape& x, int, const shape&) const { return x; }
 
     shape compute_shape(const std::vector<shape>& inputs) const
     {

src/targets/cpu/target.cpp

@@ -33,6 +33,7 @@
 #include <migraphx/eliminate_data_type.hpp>
 #include <migraphx/eliminate_identity.hpp>
 #include <migraphx/eliminate_pad.hpp>
+#include <migraphx/layout_nhwc.hpp>
 #include <migraphx/memory_coloring.hpp>
 #include <migraphx/propagate_constant.hpp>
 #include <migraphx/register_target.hpp>
@@ -82,6 +83,9 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
             dead_code_elimination{},
             simplify_algebra{},
             simplify_reshapes{},
+            layout_nhwc{},
+            dead_code_elimination{},
+            simplify_reshapes{},
             simplify_algebra{},
             auto_contiguous{},
             simplify_reshapes{},

src/targets/gpu/CMakeLists.txt

@@ -83,6 +83,7 @@ add_library(migraphx_gpu
     compile_gen.cpp
     compile_hip.cpp
     compile_hip_code_object.cpp
+    compile_miopen.cpp
     compiler.cpp
     device_name.cpp
     fuse_mlir.cpp
@@ -232,11 +233,14 @@ get_target_property(MIOPEN_LOCATION MIOpen LOCATION)
 check_library_exists(MIOpen "miopenHiddenSetConvolutionFindMode" "${MIOPEN_LOCATION}" HAS_FIND_MODE_API)
 check_library_exists(MIOpen "miopenFindSolutions" "${MIOPEN_LOCATION}" HAS_FIND_2_API)
 
-if(HAS_FIND_2_API) 
+# TODO: Set default to HAS_FIND_2_API
+set(MIGRAPHX_USE_FIND_2_API OFF CACHE BOOL "")
+
+if(MIGRAPHX_USE_FIND_2_API) 
     target_compile_definitions(migraphx_gpu PUBLIC -DMIGRAPHX_HAS_FIND_2_API)
     message(STATUS "MIGraphx is using Find-2.0 API of MIOpen")
 else()
-    message(STATUS "MIOpen does not have Find-2.0 API")
+    message(STATUS "MIGraphx is using legacy Find API in MIOpen")
 endif()
 
 if(HAS_FIND_MODE_API)

src/targets/gpu/compile_miopen.cpp

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 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/compile_miopen.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/module.hpp>
+#include <migraphx/iterator_for.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/register_op.hpp>
+#include <migraphx/op/identity.hpp>
+#include <migraphx/gpu/rocblas.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+struct miopen_op
+{
+    operation op = op::identity{};
+
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.op, "op"));
+    }
+
+    std::string name() const { return "gpu::miopen_op"; }
+
+    shape compute_shape(std::vector<shape> inputs) const
+    {
+        inputs.push_back(inputs.back());
+        return op.compute_shape(inputs);
+    }
+
+    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+    {
+        return shapes.size() - 1;
+    }
+};
+MIGRAPHX_REGISTER_OP(miopen_op);
+
+std::size_t compile_miopen::compile(operation& op, instruction_ref ins, bool format) const
+{
+    op.from_value({{"int8_x4_format", format}});
+    auto v = op.compile(*ctx, ins->get_shape(), to_shapes(ins->inputs()));
+    return v.get<std::size_t>("workspace", 0);
+}
+
+void compile_miopen::apply(module& m) const
+{
+    assert(ctx);
+    const bool int8_x4_format = get_int8_x4_format(any_cast<migraphx::gpu::context>(*ctx));
+    for(auto ins : iterator_for(m))
+    {
+        if(ins->name() != "gpu::miopen_op")
+            continue;
+        auto op        = any_cast<miopen_op>(ins->get_operator()).op;
+        std::size_t ws = 0;
+        try
+        {
+            // for the regular convolution and deconvolution, this try would always succeed
+            ws = compile(op, ins, int8_x4_format);
+        }
+        catch(migraphx::exception&)
+        {
+            // In case no solver supports the default format, retry using the other format.
+            ws = compile(op, ins, not int8_x4_format);
+        }
+        auto inputs = ins->inputs();
+        auto alloc  = m.insert_instruction(
+            ins, make_op("allocate", {{"shape", to_value(shape{shape::int8_type, {ws}})}}));
+        inputs.insert(std::prev(inputs.end()), alloc);
+
+        m.replace_instruction(ins, op, inputs);
+    }
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/compile_ops.cpp

@@ -39,19 +39,26 @@ MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_COMPILE_PARALLEL);
 
 struct precompile_op
 {
-    operation op = op::identity{};
+    operation op                = op::identity{};
+    std::size_t additional_args = 1;
+    bool ignore_modules         = false;
 
     template <class Self, class F>
     static auto reflect(Self& self, F f)
     {
-        return pack(f(self.op, "op"));
+        return pack(f(self.op, "op"),
+                    f(self.additional_args, "additional_args"),
+                    f(self.ignore_modules, "ignore_modules"));
     }
 
     std::string name() const { return "gpu::precompile_op"; }
 
     shape compute_shape(std::vector<shape> inputs, const std::vector<module_ref>& mods) const
     {
-        inputs.pop_back();
+        // Pop off additional args
+        inputs.resize(inputs.size() - additional_args);
+        if(ignore_modules)
+            return op.compute_shape(inputs);
         return op.compute_shape(inputs, mods);
     }
 

src/targets/gpu/fuse_ops.cpp

@@ -772,11 +772,9 @@ struct find_layernorm_pointwise
     {
         auto ins       = r.result;
         auto layernorm = r.instructions["layernorm"];
-        auto* pm       = ins->module_inputs().front();
-
         if(not layernorm->module_inputs().empty())
             return;
-
+        auto* pm    = ins->module_inputs().front();
         auto inputs = layernorm->inputs();
         inputs.pop_back();
         inputs.insert(inputs.end(), ins->inputs().begin() + 1, ins->inputs().end());
@@ -785,6 +783,37 @@ struct find_layernorm_pointwise
     }
 };
 
+struct find_concat_pointwise
+{
+    auto matcher() const
+    {
+        return precompile_name("pointwise")(
+            match::arg(0)(precompile_name("concat").bind("concat")));
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins    = r.result;
+        auto concat = r.instructions["concat"];
+        if(not concat->module_inputs().empty())
+            return;
+
+        // TODO: Handle type conversions
+        if(ins->get_shape().type() != concat->get_shape().type())
+            return;
+
+        auto* pm    = ins->module_inputs().front();
+        auto inputs = concat->inputs();
+        inputs.pop_back();
+        inputs.insert(inputs.end(), ins->inputs().begin() + 1, ins->inputs().end());
+
+        auto op = concat->get_operator();
+        op.from_value({{"additional_args", ins->inputs().size() - 1}, {"ignore_modules", true}});
+
+        m.replace_instruction(ins, op, inputs, {pm});
+    }
+};
+
 void fuse_ops::apply(module& m) const
 {
     match::find_matches(m, find_contiguous_pointwise{});
@@ -793,6 +822,7 @@ void fuse_ops::apply(module& m) const
     run_passes(m, {dead_code_elimination{}});
     match::find_matches(m,
                         find_layernorm_pointwise{},
+                        find_concat_pointwise{},
                         find_gemm_pointwise{},
                         find_contiguous_tranpose_gemm{},
                         find_commutative_broadcast{});

src/targets/gpu/gemm_impl.cpp

@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#include <rocblas.h>
+#include <rocblas/rocblas.h>
 #include <migraphx/gpu/gemm_impl.hpp>
 #include <migraphx/reduce_dims.hpp>
 #include <migraphx/permutation.hpp>

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

+/*
+ * The MIT License (MIT)
+ *
+ * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#ifndef MIGRAPHX_GUARD_GPU_COMPILE_MIOPEN_HPP
+#define MIGRAPHX_GUARD_GPU_COMPILE_MIOPEN_HPP
+
+#include <migraphx/config.hpp>
+#include <migraphx/instruction_ref.hpp>
+#include <string>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct module;
+struct context;
+struct operation;
+
+namespace gpu {
+
+struct compile_miopen
+{
+    context* ctx = nullptr;
+    std::string name() const { return "gpu::compile_miopen"; }
+    void apply(module& m) const;
+    std::size_t compile(operation& op, instruction_ref ins, bool format) const;
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_GPU_COMPILE_MIOPEN_HPP

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

@@ -83,9 +83,10 @@ struct miopen_convolution
 
     inline shape compute_shape(const std::vector<shape>& inputs) const
     {
-        check_shapes{inputs, op}.has(4).standard();
+        check_shapes{inputs, op}.has(4);
         std::vector<shape> conv_inputs(inputs.begin(), inputs.begin() + 2);
-        check_shapes{conv_inputs, op}.max_ndims(5);
+        check_shapes{conv_inputs, *this}.max_ndims(5).packed_layouts(
+            {{0, 1, 2}, {0, 1, 2, 3}, {0, 2, 3, 1}, {0, 1, 2, 3, 4}});
         return migraphx::compute_shape<Op>(op, conv_inputs);
     }
 
@@ -144,12 +145,9 @@ struct miopen_convolution
 #endif
     }
 
-    inline void set_conv_descriptor()
+    void set_conv_descriptor()
     {
-        if(cd == nullptr)
-        {
-            cd = (op.name() == "deconvolution") ? make_deconv(op) : make_conv(op);
-        }
+        cd = (op.name() == "deconvolution") ? make_deconv(op) : make_conv(op);
     }
 
     value compile(migraphx::context& ctx, const shape& output, const std::vector<shape>& input)
@@ -239,7 +237,6 @@ struct miopen_convolution
         if(status != miopenStatusSuccess)
             MIGRAPHX_THROW("MIOpen " + op.name() + " : find convolution failed");
         algo = perf.fwd_algo;
-
         size_t solution_count;
 
         status = miopenConvolutionForwardGetSolutionCount(ctx.get_stream().get_miopen(),

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

@@ -25,7 +25,7 @@
 #define MIGRAPHX_GUARD_MIGRAPHLIB_ROCBLAS_HPP
 #include <migraphx/manage_ptr.hpp>
 #include <migraphx/config.hpp>
-#include <rocblas.h>
+#include <rocblas/rocblas.h>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {

src/targets/gpu/jit/concat.cpp

@@ -38,16 +38,19 @@ using namespace migraphx::gpu::gen; // NOLINT
 static const char* const concat_kernel = R"__migraphx__(
 #include <migraphx/kernels/concat.hpp>
 #include <migraphx/kernels/vectorize.hpp>
+#include <migraphx/kernels/ops.hpp>
 #include <args.hpp>
 
 namespace migraphx {
 
+${preamble}
+
 extern "C" {
 
 __global__ void ${kernel}(${params}) 
 {
-    transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto y, auto... xs) {
-        concat<${axis}>(y, xs...);
+    transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto y, ${concat_params}, auto... xs) {
+        concat<${axis}>(${concat_args})(${post}, y, xs...);
     });
 }
 
@@ -68,28 +71,42 @@ struct concat_compiler : compiler<concat_compiler>
 
     operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
     {
-        // TODO: Use reduce_dims
+        auto num_of_concat_inputs = v.get("concat_inputs", inputs.size() - 1);
         hip_compile_options options;
         options.inputs      = inputs;
         options.output      = inputs.back();
         options.params      = "-Wno-float-equal";
+        options.kernel_name = v.get("kernel", "concat_kernel");
         auto axis           = find_fast_axis(options.inputs);
         auto vec            = vectorize::elements(ctx, axis, options.inputs);
-        options.kernel_name = v.get("kernel", "concat_kernel");
         options.set_launch_params(
             v, compute_global_for(ctx, get_concat_elements(options.inputs) / vec.size, 256));
-        auto src = interpolate_string(concat_kernel,
-                                      {{"kernel", options.kernel_name},
-                                       {"params", enum_params(inputs.size(), "void * private_p")},
-                                       {"args", enum_params(inputs.size(), "private_p")},
-                                       {"transformers", make_transformer_args(vec)},
-                                       {"axis", v.at("axis").to<std::string>()}});
+        auto src = interpolate_string(
+            concat_kernel,
+            {{"kernel", options.kernel_name},
+             {"params", enum_params(inputs.size(), "void * private_p")},
+             {"args", enum_params(inputs.size(), "private_p")},
+             {"concat_params", enum_params(num_of_concat_inputs, "auto concat_x")},
+             {"concat_args", enum_params(num_of_concat_inputs, "concat_x")},
+             {"post", v.get("post", std::string{"op::id{}"})},
+             {"transformers", make_transformer_args(vec)},
+             {"preamble", v.get("preamble", std::string{})},
+             {"axis", v.at("axis").to<std::string>()}});
         return compile_hip_code_object(src, options);
     }
 
     compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
     {
-        return replace(compile_op(ctx, to_shapes(ins->inputs()), op.to_value()));
+        auto v = op.to_value();
+        if(not ins->module_inputs().empty())
+        {
+            auto* pm           = ins->module_inputs().front();
+            v["concat_inputs"] = ins->inputs().size() - pm->get_parameter_names().size();
+            v["preamble"]      = generate_pointwise(*pm, "post_concat");
+            v["post"]          = "MIGRAPHX_LIFT(post_concat)";
+            v["kernel"]        = "concat_" + generate_name_from_ops(*pm) + "_kernel";
+        }
+        return replace(compile_op(ctx, to_shapes(ins->inputs()), v));
     }
 };
 

src/targets/gpu/jit/pointwise.cpp

@@ -58,7 +58,7 @@ __global__ void ${kernel}(${params})
 
 struct pointwise_compiler : compiler<pointwise_compiler>
 {
-    std::vector<std::string> names() const { return {"pointwise", "contiguous"}; }
+    std::vector<std::string> names() const { return {"pointwise", "contiguous", "layout"}; }
 
     static std::size_t oversubscribe_if(bool b)
     {
@@ -91,12 +91,12 @@ struct pointwise_compiler : compiler<pointwise_compiler>
 
     compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
     {
-        if(op.name() == "contiguous")
+        if(contains({"layout", "contiguous"}, op.name()))
         {
             return replace(compile_op(
                 ctx,
                 to_shapes(ins->inputs()),
-                {{"lambda", "[](auto x) { return x; }"}, {"kernel", "contiguous_kernel"}}));
+                {{"lambda", "[](auto x) { return x; }"}, {"kernel", op.name() + "_kernel"}}));
         }
         else
         {

src/targets/gpu/kernels/include/migraphx/kernels/concat.hpp

@@ -41,7 +41,15 @@ constexpr auto concat_slice(Output out, Input, Start)
         return Start{} * output_shape.strides[Axis];
     });
     constexpr auto s       = make_shape(lens, strides);
-    return make_tensor_view(&out[offset], s);
+    MIGRAPHX_ASSERT(offset < out.get_shape().element_space());
+    MIGRAPHX_ASSERT((s.element_space() + offset) <= out.get_shape().element_space());
+    return make_tensor_view(out.data() + offset, s);
+}
+
+template <index_int Axis, class Input, class Start, class... Ts>
+constexpr auto concat_slices(Input input, Start start, Ts... xs)
+{
+    return [=](auto f) { f(concat_slice<Axis>(xs, input, start)...); };
 }
 
 template <index_int Axis, class Input>
@@ -51,15 +59,19 @@ constexpr auto concat_ends(Input)
     return _c<lens[Axis]>;
 }
 
-template <index_int Axis, class Output, class... Inputs>
-__device__ void concat(Output output, Inputs... inputs)
+template <index_int Axis, class... Inputs>
+__device__ auto concat(Inputs... inputs)
 {
-    auto idx = make_index();
-    fold([&](auto start, auto input) {
-        auto y = concat_slice<Axis>(output, input, start);
-        idx.global_stride(input.get_shape().elements(), [&](auto i) { y[i] = input[i]; });
-        return start + concat_ends<Axis>(input);
-    })(_c<0>, inputs...);
+    return [=](auto f, auto... ts) {
+        auto idx = make_index();
+        fold([&](auto start, auto input) {
+            concat_slices<Axis>(input, start, ts...)([&](auto y, auto... xs) {
+                idx.global_stride(input.get_shape().elements(),
+                                  [&](auto i) { y[i] = f(input[i], xs[i]...); });
+            });
+            return start + concat_ends<Axis>(input);
+        })(_c<0>, inputs...);
+    };
 }
 
 } // namespace migraphx