merge from develop branch and resolve merge conflicts

src/targets/gpu/argmax.cpp

@@ -9,7 +9,7 @@ namespace gpu {
 
 shape hip_argmax::compute_shape(const std::vector<shape>& inputs) const
 {
-    check_shapes{inputs, *this}.has(2).standard();
+    check_shapes{inputs, *this}.has(2);
     return op.normalize_compute_shape({inputs.at(0)});
 }
 

src/targets/gpu/argmin.cpp

@@ -9,7 +9,7 @@ namespace gpu {
 
 shape hip_argmin::compute_shape(const std::vector<shape>& inputs) const
 {
-    check_shapes{inputs, *this}.has(2).standard();
+    check_shapes{inputs, *this}.has(2);
     return op.normalize_compute_shape({inputs.at(0)});
 }
 

src/targets/gpu/compile_hip_code_object.cpp

@@ -108,12 +108,13 @@ operation compile_hip_code_object(const std::string& content, hip_compile_option
     srcs.push_back(src_file{fs::path{"main.cpp"},
                             std::make_pair(content.data(), content.data() + content.size())});
     auto args_hpp =
-        generate_args_hpp(options.reduced_inputs.empty() ? options.inputs : options.reduced_inputs);
+        generate_args_hpp(options.virtual_inputs.empty() ? options.inputs : options.virtual_inputs);
     srcs.push_back(src_file{fs::path{"args.hpp"},
                             std::make_pair(args_hpp.data(), args_hpp.data() + args_hpp.size())});
     options.params += " -DMIGRAPHX_NGLOBAL=" + std::to_string(options.global);
     options.params += " -DMIGRAPHX_NLOCAL=" + std::to_string(options.local);
     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());
     if(cos.size() != 1)

src/targets/gpu/compile_ops.cpp

@@ -12,6 +12,8 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_COMPILE_PARALLEL);
+
 struct precompile_op
 {
     operation op = op::identity{};
@@ -70,6 +72,14 @@ struct compiled_result
     instruction_ref ins;
 };
 
+template <class F>
+void par_compile(std::size_t n, F f)
+{
+    if(n == 0)
+        return;
+    par_for(n, n / value_of(MIGRAPHX_GPU_COMPILE_PARALLEL{}, n), f);
+}
+
 void compile_ops::apply(module& m) const
 {
     auto compilers = make_compilers(pointwise_compiler{});
@@ -85,7 +95,7 @@ void compile_ops::apply(module& m) const
         compiles.emplace_back([=]() -> compiled_result { return {c(*ctx, ins, preop), ins}; });
     }
     std::vector<compiled_result> results(compiles.size());
-    par_for(compiles.size(), 1, [&](auto i) { results[i] = compiles[i](); });
+    par_compile(compiles.size(), [&](auto i) { results[i] = compiles[i](); });
     for(const auto& cr : results)
     {
         m.replace_instruction(cr.ins, cr.op, cr.ins->inputs());

src/targets/gpu/compile_pointwise.cpp

@@ -20,7 +20,7 @@ static const char* const pointwise_kernel = R"__migraphx__(
 #include <migraphx/kernels/pointwise.hpp>
 #include <args.hpp>
 
-using namespace migraphx;
+namespace migraphx {
 
 ${preamble}
 
@@ -32,6 +32,8 @@ __global__ void kernel(${params})
     
 }
 
+} // namespace migraphx
+
 int main() {}
 
 )__migraphx__";
@@ -46,7 +48,7 @@ operation compile_pointwise(context&,
     options.local          = 1024;
     options.inputs         = inputs;
     options.output         = inputs.back();
-    options.reduced_inputs = reduce_dims(inputs);
+    options.virtual_inputs = reduce_dims(inputs);
     options.params         = "-Wno-float-equal";
     auto src               = interpolate_string(pointwise_kernel,
                                   {{"params", enum_params(inputs.size(), "void * private_p")},
@@ -60,8 +62,20 @@ operation compile_pointwise(context& ctx, const std::vector<shape>& inputs, modu
 {
     run_passes(m, {eliminate_common_subexpression{}, dead_code_elimination{}});
     cpp_generator g;
-    auto name = g.create_function(g.generate_module(m).set_attributes({"__device__"}));
-    return compile_pointwise((ctx), inputs, "&" + name, g.str());
+    g.fmap([](const std::string& fname) { return "migraphx::" + fname; });
+    g.add_point_op("where", "${function:where}(${0}, ${1}, ${2})");
+    g.add_point_op("prelu", "${function:where}(${0} < 0, ${0} * ${1}, ${0})");
+    g.add_point_op("sign", "${function:where}(${0} > 0, 1, ${function:where}(${0} < 0, -1, 0))");
+    g.add_point_op("equal", "migraphx::abs(${0} == ${1})");
+    g.add_point_op("less", "migraphx::abs(${0} < ${1})");
+    g.add_point_op("greater", "migraphx::abs(${0} > ${1})");
+    g.add_point_op("not", "migraphx::abs(not ${0})");
+    // Add explict conversions
+    g.fresult(
+        [](const shape& s) { return "migraphx::convert<" + shape::cpp_type(s.type()) + ">"; });
+    auto name =
+        g.create_function(g.generate_module(m).set_attributes({"__device__"}).set_generic_types(m));
+    return compile_pointwise((ctx), inputs, "MIGRAPHX_LIFT(" + name + ")", g.str());
 }
 
 } // namespace gpu

src/targets/gpu/compile_roialign.cpp

@@ -50,7 +50,7 @@ operation compile_roialign(context&, const std::vector<shape>& io_shapes, const
     options.inputs         = io_shapes;
     options.output         = out_s;
     options.kernel_name    = "roialign_kernel";
-    options.reduced_inputs = io_shapes;
+    options.virtual_inputs = io_shapes;
 
     // sampling_ratio
     assert(val.contains("sampling_ratio"));

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

@@ -75,8 +75,9 @@ MIGRAPHX_DEVICE_CONSTEXPR auto gs_invoke(F&& f, index_int i, index) -> decltype(
 
 inline auto gs_launch(hipStream_t stream, index_int n, index_int local = 1024)
 {
-    index_int groups  = (n + local - 1) / local;
-    index_int nglobal = std::min<index_int>(256, groups) * local;
+    index_int groups = (n + local - 1) / local;
+    // max possible number of blocks is set to 1B (1,073,741,824)
+    index_int nglobal = std::min<index_int>(1073741824, groups) * local;
 
     return [=](auto f) {
         launch(stream, nglobal, local)([=](auto idx) __device__ {

src/targets/gpu/device/softmax.cpp

@@ -20,34 +20,58 @@ void softmax(hipStream_t stream, const argument& result, const argument& arg, in
     migraphx::shape batch_shape{result.get_shape().type(), batch_lens};
 
     hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
-        const index_int max_block_size = 256;
+        const index_int max_block_size = 128;
         const index_int block_size     = compute_block_size(batch_item_num, max_block_size);
-        gs_launch(stream,
-                  batch_shape.elements() * block_size,
-                  block_size)([=](auto i, auto idx) __device__ {
-            auto data_idx = batch.multi(i / block_size);
-            using type    = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
-            type init     = lowest();
-
-            auto batch_max = block_reduce<max_block_size>(
-                idx, max{}, init, batch_item_num, [&](auto j) __device__ {
-                    data_idx[axis] = j;
-                    return input[data_idx];
-                });
+        using type = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
+        type init  = lowest();
+
+        if(axis == batch_lens.size() - 1)
+        {
+            gs_launch(stream, batch_shape.elements() * block_size, block_size)(
+                [=](auto i, auto idx) __device__ {
+                    auto start_loc = i / block_size * batch_item_num;
+                    auto batch_max = block_reduce<max_block_size>(
+                        idx, max{}, init, batch_item_num, [&](auto j) __device__ {
+                            return input[start_loc + j];
+                        });
+
+                    auto batch_sum = block_reduce<max_block_size>(
+                        idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
+                            auto val = input[start_loc + j] - batch_max;
+                            return ::exp(to_hip_type(val));
+                        });
 
-            auto batch_sum =
-                block_reduce<max_block_size>(idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
-                    data_idx[axis] = j;
-                    auto val       = input[data_idx] - batch_max;
-                    return ::exp(to_hip_type(val));
+                    idx.local_stride(batch_item_num, [&](auto j) __device__ {
+                        auto val              = input[start_loc + j] - batch_max;
+                        output[start_loc + j] = ::exp(to_hip_type(val)) / batch_sum;
+                    });
                 });
+        }
+        else
+        {
+            gs_launch(stream, batch_shape.elements() * block_size, block_size)(
+                [=](auto i, auto idx) __device__ {
+                    auto data_idx  = batch.multi(i / block_size);
+                    auto batch_max = block_reduce<max_block_size>(
+                        idx, max{}, init, batch_item_num, [&](auto j) __device__ {
+                            data_idx[axis] = j;
+                            return input[data_idx];
+                        });
 
-            idx.local_stride(batch_item_num, [&](auto j) __device__ {
-                data_idx[axis]   = j;
-                auto val         = input[data_idx] - batch_max;
-                output[data_idx] = ::exp(to_hip_type(val)) / batch_sum;
-            });
-        });
+                    auto batch_sum = block_reduce<max_block_size>(
+                        idx, sum{}, 0, batch_item_num, [&](auto j) __device__ {
+                            data_idx[axis] = j;
+                            auto val       = input[data_idx] - batch_max;
+                            return ::exp(to_hip_type(val));
+                        });
+
+                    idx.local_stride(batch_item_num, [&](auto j) __device__ {
+                        data_idx[axis]   = j;
+                        auto val         = input[data_idx] - batch_max;
+                        output[data_idx] = ::exp(to_hip_type(val)) / batch_sum;
+                    });
+                });
+        }
     });
 }
 

src/targets/gpu/fuse_ops.cpp

@@ -62,6 +62,8 @@ struct fusion
         keep_alive(std::move(t));
     }
 
+    bool empty() const { return fp == nullptr; }
+
     op_t operator[](std::size_t i) const
     {
         assert(fp);
@@ -125,12 +127,11 @@ struct fusion
         return shape{shape::int8_type, {ws_size}};
     }
 
-    void compile(context& ctx)
+    bool compile(context& ctx)
     {
         assert(fp);
-        auto status = miopenCompileFusionPlan(ctx.get_stream().get_miopen(), fp.get());
-        if(status != miopenStatusSuccess)
-            MIGRAPHX_THROW("Compiling fusion plan failed");
+        return miopenCompileFusionPlan(ctx.get_stream().get_miopen(), fp.get()) ==
+               miopenStatusSuccess;
     }
 
     argument execute(context& ctx,
@@ -169,7 +170,7 @@ MIGRAPHX_PRED_MATCHER(bias_shape, instruction_ref ins)
 
 MIGRAPHX_PRED_MATCHER(fusable_conv, instruction_ref ins)
 {
-    const auto device_name = split_string(get_device_name(), ':').front();
+    const auto device_name = trim(split_string(get_device_name(), ':').front());
     if(not contains(get_supported_archs(), device_name))
         return false;
     if(enabled(MIGRAPHX_DISABLE_MIOPEN_FUSION{}))
@@ -561,6 +562,122 @@ struct find_mul_add_relu
     }
 };
 
+struct miopen_fusion
+{
+    struct fuse_op_data
+    {
+        operation op;
+        float alpha = 1;
+        float beta  = 0;
+    };
+    struct fuse_op : fuse_op_data, reflect_equality<fuse_op>, reflect_stream<fuse_op>
+    {
+        template <class Self, class F>
+        static auto reflect(Self& self, F f)
+        {
+            return pack(f(self.op, "op"), f(self.alpha, "alpha"), f(self.beta, "beta"));
+        }
+    };
+    std::vector<fuse_op> ops = {};
+    fusion f                 = {};
+    std::function<void(context&, const fusion&, const std::vector<argument>&)> execute;
+    template <class Self, class F>
+    static auto reflect(Self& self, F f)
+    {
+        return pack(f(self.ops, "ops"));
+    }
+
+    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+    {
+        return shapes.size() - 1;
+    }
+
+    value compile(context& ctx, const shape&, std::vector<shape> inputs)
+    {
+        // Compensate for allocation
+        inputs.pop_back();
+        std::size_t i = 0;
+        f             = fusion(inputs[i]);
+        i++;
+        std::vector<std::function<void(const fused_operator_args&, const std::vector<argument>&)>>
+            invokers;
+        for(auto&& fop : ops)
+        {
+            if(i > inputs.size())
+            {
+                f = {};
+                return {};
+            }
+            if(fop.op.name() == "convolution")
+            {
+                auto* mop = f.create_conv(any_cast<op::convolution>(fop.op), inputs[i]);
+                invokers.push_back(
+                    [=](const fused_operator_args& fargs, const std::vector<argument>& args) {
+                        miopenSetOpArgsConvForward(
+                            fargs.get(), mop, &fop.alpha, &fop.beta, args[i].implicit());
+                    });
+                i++;
+            }
+            else if(fop.op.name() == "add")
+            {
+                auto* mop = f.create_bias(inputs[i]);
+                invokers.push_back(
+                    [=](const fused_operator_args& fargs, const std::vector<argument>& args) {
+                        miopenSetOpArgsBiasForward(
+                            fargs.get(), mop, &fop.alpha, &fop.beta, args[i].implicit());
+                    });
+                i++;
+            }
+            else if(fop.op.name() == "relu")
+            {
+                auto* mop = f.create_relu();
+                invokers.push_back([=](const fused_operator_args& fargs,
+                                       const std::vector<argument>&) {
+                    miopenSetOpArgsActivForward(fargs.get(), mop, &fop.alpha, &fop.beta, 0, 0, 0);
+                });
+            }
+            else
+            {
+                f = {};
+                return {};
+            }
+        }
+        if(not f.compile(ctx))
+        {
+            f = {};
+            return {};
+        }
+        execute = [invokers](context& c, const fusion& ff, const std::vector<argument>& args) {
+            auto fargs = make_fused_args();
+            for(auto&& invoker : invokers)
+                invoker(fargs, args);
+            ff.execute(c, fargs, args.front(), args.back());
+        };
+        return {{"workspace", f.get_workspace(ctx).bytes()}};
+    }
+    void finalize(context& ctx, const shape& output_shape, const std::vector<shape>& inputs)
+    {
+        if(not f.empty())
+            return;
+        auto v = compile(ctx, output_shape, inputs);
+        if(not v.is_object())
+            MIGRAPHX_THROW("Failed to compile fusion plan");
+    }
+    std::string name() const { return "gpu::miopen_fusion"; }
+    shape compute_shape(const std::vector<shape>& inputs) const
+    {
+        if(ops.empty())
+            return {};
+        // TODO: Check number of arguments
+        return ops.front().op.compute_shape({inputs[0], inputs[1]});
+    }
+    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const
+    {
+        execute(ctx, f, args);
+        return args.back();
+    }
+};
+
 struct miopen_conv_bias
 {
     op::convolution op;
@@ -596,7 +713,8 @@ struct miopen_conv_bias
         f    = fusion(inputs[0]);
         conv = f.create_conv(op, inputs[1]);
         bias = f.create_bias(inputs[3]);
-        f.compile(ctx);
+        if(not f.compile(ctx))
+            MIGRAPHX_THROW("Failed to compile fusion plan");
     }
 
     shape get_workspace(context& ctx) { return f.get_workspace(ctx); }
@@ -683,6 +801,25 @@ void apply_conv_bias(context& ctx, module& p, match::matcher_result r)
     p.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
 }
 
+inline auto precompile_name(std::string s) // NOLINT
+{
+    return match::make_basic_pred_matcher([=](instruction_ref ins) {
+        if(ins->name() != "gpu::precompile_op")
+            return false;
+        auto op = from_value<operation>(ins->get_operator().to_value().at("op"));
+        return (op.name() == s);
+    });
+}
+
+template <class... Ms>
+auto conv_bias_pointwise(Ms... ms)
+{
+    return precompile_name("pointwise")(
+        match::either_arg(0, 1)(bias_shape(match::used_once()).bind("bias"),
+                                fusable_conv(match::used_once()).bind("conv")),
+        ms...);
+}
+
 struct find_conv_bias
 {
     context* ctx = nullptr;
@@ -709,6 +846,46 @@ struct find_conv_bias_relu
     }
 };
 
+struct find_conv_pointwise
+{
+    context* ctx = nullptr;
+    auto matcher() const
+    {
+        return precompile_name("pointwise")(
+            match::nargs(3),
+            match::either_arg(0, 1)(bias_shape(match::used_once()).bind("bias"),
+                                    fusable_conv(match::used_once()).bind("conv")));
+    }
+
+    void apply(module& m, match::matcher_result r) const
+    {
+        auto conv_ins    = r.instructions["conv"];
+        auto bias_ins    = r.instructions["bias"];
+        auto ins         = r.result;
+        auto input_ins   = conv_ins->inputs().at(0);
+        auto weights_ins = conv_ins->inputs().at(1);
+        auto conv_op     = any_cast<miopen_convolution>(conv_ins->get_operator()).op;
+        auto alloc_ins   = ins->inputs().back();
+
+        module_ref pm = ins->module_inputs().front();
+
+        miopen_fusion op{};
+        op.ops.push_back({{conv_op}});
+        for(auto&& i : *pm)
+        {
+            if(i.name()[0] == '@')
+                continue;
+            auto inputs = to_shapes(i.inputs());
+            op.ops.push_back({{i.get_operator()}});
+        }
+        std::vector<instruction_ref> inputs = {input_ins, weights_ins, bias_ins, alloc_ins};
+        auto v                              = op.compile(*ctx, ins->get_shape(), to_shapes(inputs));
+        if(not v.is_object())
+            return;
+        m.replace_instruction(ins, op, inputs);
+    }
+};
+
 struct find_gemm_add
 {
     auto matcher() const
@@ -778,6 +955,7 @@ void fuse_ops::apply(module& p) const
     match::find_matches(p, find_triadd{});
     match::find_matches(p,
                         find_layernorm{},
+                        find_conv_pointwise{ctx},
                         find_conv_bias_relu{ctx},
                         find_conv_bias{ctx},
                         find_add_gelu{},

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

@@ -16,7 +16,7 @@ struct hip_compile_options
     shape output;
     std::string kernel_name           = "kernel";
     std::string params                = "";
-    std::vector<shape> reduced_inputs = {};
+    std::vector<shape> virtual_inputs = {};
 };
 
 operation compile_hip_code_object(const std::string& content, hip_compile_options options);

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

@@ -76,8 +76,9 @@ void arg_op(Op op, hipStream_t stream, const argument& result, const argument& a
     size_t batch_item_num = batch_lens[axis];
     batch_lens[axis]      = 1;
     migraphx::shape batch_shape{arg_shape.type(), batch_lens};
+    migraphx::shape std_arg_shape{arg_shape.type(), arg_shape.lens()};
 
-    hip_visit_all(arg, arg_shape, batch_shape)([&](auto input, auto arg_s, auto batch_s) {
+    hip_visit_all(arg, std_arg_shape, batch_shape)([&](auto input, auto arg_s, auto batch_s) {
         auto* output = device_cast(result.get<int64_t>().data());
         using type   = device_type<std::remove_cv_t<typename decltype(input)::value_type>>;
         // use one block for items in one batch.

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

@@ -5,6 +5,9 @@
 
 namespace migraphx {
 
+#define MIGRAPHX_STRINGIZE_1(...) #__VA_ARGS__
+#define MIGRAPHX_STRINGIZE(...) MIGRAPHX_STRINGIZE_1(__VA_ARGS__)
+
 // Workaround hip's broken abort on device code
 #ifdef __HIP_DEVICE_COMPILE__
 // NOLINTNEXTLINE
@@ -14,19 +17,67 @@ namespace migraphx {
 #define MIGRAPHX_HIP_NORETURN [[noreturn]]
 #endif
 
+namespace debug {
+struct swallow
+{
+    template <class... Ts>
+    constexpr swallow(Ts&&...)
+    {
+    }
+};
+
+template <size_t N>
+struct print_buffer
+{
+    char buffer[N + 1] = {0};
+    char* pos          = buffer;
+
+    constexpr void append(char c)
+    {
+        if(c == 0)
+            return;
+        if(pos < buffer + N)
+        {
+            *pos = c;
+            pos++;
+        }
+    }
+
+    template <size_t M>
+    constexpr void append(const char (&array)[M])
+    {
+        for(int i = 0; i < M; i++)
+            append(array[i]);
+    }
+};
+
+template <class... Ts>
+__host__ __device__ void print(const Ts&... xs)
+{
+    const auto size = (sizeof(xs) + ...);
+    print_buffer<size> buffer;
+    swallow{(buffer.append(xs), 0)...};
+    printf("%s", buffer.buffer);
+}
+
+} // namespace debug
+
 // noreturn cannot be used on this function because abort in hip is broken
+template <class T1, class T2, class T3, class T4>
 MIGRAPHX_HIP_NORETURN inline __host__ __device__ void
-assert_fail(const char* assertion, const char* file, unsigned int line, const char* function)
+assert_fail(const T1& assertion, const T2& file, const T3& line, const T4& function)
 {
-    printf("%s:%u: %s: assertion '%s' failed.\n", file, line, function, assertion);
+    // printf is broken on hip with more than one argument, so use a simple print functions instead
+    debug::print(file, ":", line, ": ", function, ": assertion '", assertion, "' failed.\n");
+    // printf("%s:%s: %s: assertion '%s' failed.\n", file, line, function, assertion);
     abort();
 }
 
 #ifdef MIGRAPHX_DEBUG
-#define MIGRAPHX_ASSERT(cond)            \
-    ((cond) ? void(0) : [](auto... xs) { \
-        assert_fail(xs...);              \
-    }(#cond, __FILE__, __LINE__, __PRETTY_FUNCTION__))
+#define MIGRAPHX_ASSERT(cond)                               \
+    ((cond) ? void(0) : [](auto&&... private_migraphx_xs) { \
+        assert_fail(private_migraphx_xs...);                \
+    }(#cond, __FILE__, MIGRAPHX_STRINGIZE(__LINE__), __PRETTY_FUNCTION__))
 #else
 #define MIGRAPHX_ASSERT(cond)
 #endif

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

@@ -16,6 +16,19 @@ struct swallow
 template <index_int>
 using ignore = swallow;
 
+template <class... Fs>
+struct overloaded : Fs...
+{
+    using Fs::operator()...;
+    overloaded(Fs... fs) : Fs(fs)... {}
+};
+
+template <class... Fs>
+overloaded<Fs...> overload(Fs... fs)
+{
+    return {fs...};
+}
+
 namespace detail {
 
 template <class R>
@@ -124,12 +137,48 @@ constexpr void each_args(F)
 {
 }
 
+template <class F, class T>
+constexpr auto fold_impl(F&&, T&& x)
+{
+    return static_cast<T&&>(x);
+}
+
+template <class F, class T, class U, class... Ts>
+constexpr auto fold_impl(F&& f, T&& x, U&& y, Ts&&... xs)
+{
+    return fold_impl(f, f(static_cast<T&&>(x), static_cast<U&&>(y)), static_cast<Ts&&>(xs)...);
+}
+
+template <class F>
+constexpr auto fold(F f)
+{
+    return [=](auto&&... xs) { return fold_impl(f, static_cast<decltype(xs)&&>(xs)...); };
+}
+
 template <class... Ts>
-auto pack(Ts... xs)
+constexpr auto pack(Ts... xs)
 {
     return [=](auto f) { return f(xs...); };
 }
 
+template <class Compare, class P1, class P2>
+constexpr auto pack_compare(Compare compare, P1 p1, P2 p2)
+{
+    return p1([&](auto... xs) {
+        return p2([&](auto... ys) {
+            auto c = [&](auto x, auto y) -> int {
+                if(compare(x, y))
+                    return 1;
+                else if(compare(y, x))
+                    return -1;
+                else
+                    return 0;
+            };
+            return fold([](auto x, auto y) { return x ? x : y; })(c(xs, ys)..., 0);
+        });
+    });
+}
+
 template <index_int N>
 constexpr auto arg_c()
 {
@@ -168,9 +217,13 @@ constexpr auto transform_args(F f, Fs... fs)
     return [=](auto... xs) { return transform_args(f)(xs...)(transform_args(fs...)); };
 }
 
+// NOLINTNEXTLINE
+#define MIGRAPHX_RETURNS(...) \
+    ->decltype(__VA_ARGS__) { return __VA_ARGS__; }
+
 // NOLINTNEXTLINE
 #define MIGRAPHX_LIFT(...) \
-    ([](auto&&... xs) { return (__VA_ARGS__)(static_cast<decltype(xs)>(xs)...); })
+    [](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
 
 } // namespace migraphx
 #endif // MIGRAPHX_GUARD_KERNELS_FUNCTIONAL_HPP

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

@@ -13,6 +13,7 @@ struct integral_constant
     using type               = integral_constant;
     constexpr operator value_type() const noexcept { return value; }
     constexpr value_type operator()() const noexcept { return value; }
+    static constexpr type to() { return {}; }
 };
 
 // NOLINTNEXTLINE

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

+#ifndef MIGRAPHX_GUARD_KERNELS_MATH_HPP
+#define MIGRAPHX_GUARD_KERNELS_MATH_HPP
+
+#include <migraphx/kernels/types.hpp>
+#include <migraphx/kernels/vec.hpp>
+#include <migraphx/kernels/functional.hpp>
+#include <migraphx/kernels/type_traits.hpp>
+#include <hip/hip_fp16.h>
+#include <hip/math_functions.h>
+
+namespace migraphx {
+
+namespace math {
+constexpr float as_float(migraphx::half x) { return x; }
+template <class T>
+constexpr T as_float(T x)
+{
+    return x;
+}
+} // namespace math
+
+// NOLINTNEXTLINE
+#define MIGRAPHX_DEVICE_MATH(name, fname)                              \
+    template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
+    auto __device__ name(Ts... xs) MIGRAPHX_RETURNS(fname(xs...))
+
+// NOLINTNEXTLINE
+#define MIGRAPHX_DEVICE_MATH_VEC(name)                                       \
+    template <class... Ts, MIGRAPHX_REQUIRES(is_any_vec<Ts...>())>           \
+    auto __device__ name(Ts... xs)                                           \
+    {                                                                        \
+        return vec_transform(xs...)([](auto... ys) { return name(ys...); }); \
+    }
+
+// NOLINTNEXTLINE
+#define MIGRAPHX_DEVICE_MATH_FOR(type, name, fname)                    \
+    template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
+    auto __device__ name(type x, Ts... xs)->type                       \
+    {                                                                  \
+        return fname(x, xs...);                                        \
+    }
+
+// NOLINTNEXTLINE
+#define MIGRAPHX_DEVICE_MATH_HALF(name, fname)                         \
+    template <class... Ts, MIGRAPHX_REQUIRES(not is_any_vec<Ts...>())> \
+    auto __device__ name(migraphx::half x, Ts... xs)                   \
+        MIGRAPHX_RETURNS(fname(math::as_float(x), math::as_float(xs)...))
+
+MIGRAPHX_DEVICE_MATH(abs, ::abs)
+MIGRAPHX_DEVICE_MATH(acos, ::acos)
+MIGRAPHX_DEVICE_MATH(acosh, ::acosh)
+MIGRAPHX_DEVICE_MATH(asin, ::asin)
+MIGRAPHX_DEVICE_MATH(asinh, ::asinh)
+MIGRAPHX_DEVICE_MATH(atan, ::atan)
+MIGRAPHX_DEVICE_MATH(atanh, ::atanh)
+MIGRAPHX_DEVICE_MATH(ceil, ::ceil)
+MIGRAPHX_DEVICE_MATH(cos, ::cos)
+MIGRAPHX_DEVICE_MATH(cosh, ::cosh)
+MIGRAPHX_DEVICE_MATH(erf, ::erf)
+MIGRAPHX_DEVICE_MATH(exp, ::exp)
+MIGRAPHX_DEVICE_MATH(floor, ::floor)
+MIGRAPHX_DEVICE_MATH(log, ::log)
+MIGRAPHX_DEVICE_MATH(pow, ::pow)
+MIGRAPHX_DEVICE_MATH(round, ::round)
+MIGRAPHX_DEVICE_MATH(rsqrt, ::rsqrt)
+MIGRAPHX_DEVICE_MATH(sin, ::sin)
+MIGRAPHX_DEVICE_MATH(sinh, ::sinh)
+MIGRAPHX_DEVICE_MATH(sqrt, ::sqrt)
+MIGRAPHX_DEVICE_MATH(tan, ::tan)
+MIGRAPHX_DEVICE_MATH(tanh, ::tanh)
+
+// Float overloads
+MIGRAPHX_DEVICE_MATH_FOR(float, acos, ::acosf)
+MIGRAPHX_DEVICE_MATH_FOR(float, acosh, ::acoshf)
+MIGRAPHX_DEVICE_MATH_FOR(float, asin, ::asinf)
+MIGRAPHX_DEVICE_MATH_FOR(float, asinh, ::asinhf)
+MIGRAPHX_DEVICE_MATH_FOR(float, atan, ::atanf)
+MIGRAPHX_DEVICE_MATH_FOR(float, atanh, ::atanhf)
+MIGRAPHX_DEVICE_MATH_FOR(float, cos, ::cosf)
+MIGRAPHX_DEVICE_MATH_FOR(float, cosh, ::coshf)
+MIGRAPHX_DEVICE_MATH_FOR(float, rsqrt, ::rsqrtf)
+MIGRAPHX_DEVICE_MATH_FOR(float, sin, ::sinf)
+MIGRAPHX_DEVICE_MATH_FOR(float, sinh, ::sinhf)
+MIGRAPHX_DEVICE_MATH_FOR(float, tan, ::tanf)
+MIGRAPHX_DEVICE_MATH_FOR(float, tanh, ::tanhf)
+
+// Builtin half functions
+MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, abs, ::__habs)
+MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, exp, ::hexp)
+MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, log, ::hlog)
+MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, rsqrt, ::hrsqrt)
+MIGRAPHX_DEVICE_MATH_FOR(migraphx::half, sqrt, ::hsqrt)
+
+// Use float to compute half overload
+MIGRAPHX_DEVICE_MATH_HALF(acos, ::acos)
+MIGRAPHX_DEVICE_MATH_HALF(acosh, ::acosh)
+MIGRAPHX_DEVICE_MATH_HALF(asin, ::asin)
+MIGRAPHX_DEVICE_MATH_HALF(asinh, ::asinh)
+MIGRAPHX_DEVICE_MATH_HALF(atan, ::atan)
+MIGRAPHX_DEVICE_MATH_HALF(atanh, ::atanh)
+MIGRAPHX_DEVICE_MATH_HALF(ceil, ::ceil)
+MIGRAPHX_DEVICE_MATH_HALF(cos, ::cos)
+MIGRAPHX_DEVICE_MATH_HALF(cosh, ::cosh)
+MIGRAPHX_DEVICE_MATH_HALF(erf, ::erf)
+MIGRAPHX_DEVICE_MATH_HALF(floor, ::floor)
+MIGRAPHX_DEVICE_MATH_HALF(pow, ::pow)
+MIGRAPHX_DEVICE_MATH_HALF(round, ::round)
+MIGRAPHX_DEVICE_MATH_HALF(sin, ::sin)
+MIGRAPHX_DEVICE_MATH_HALF(sinh, ::sinh)
+MIGRAPHX_DEVICE_MATH_HALF(tan, ::tan)
+MIGRAPHX_DEVICE_MATH_HALF(tanh, ::tanh)
+
+template <class T, class U>
+constexpr auto where(bool cond, const T& a, const U& b)
+{
+    return cond ? a : b;
+}
+
+MIGRAPHX_DEVICE_MATH_VEC(abs)
+MIGRAPHX_DEVICE_MATH_VEC(acos)
+MIGRAPHX_DEVICE_MATH_VEC(acosh)
+MIGRAPHX_DEVICE_MATH_VEC(asin)
+MIGRAPHX_DEVICE_MATH_VEC(asinh)
+MIGRAPHX_DEVICE_MATH_VEC(atan)
+MIGRAPHX_DEVICE_MATH_VEC(atanh)
+MIGRAPHX_DEVICE_MATH_VEC(ceil)
+MIGRAPHX_DEVICE_MATH_VEC(cos)
+MIGRAPHX_DEVICE_MATH_VEC(cosh)
+MIGRAPHX_DEVICE_MATH_VEC(erf)
+MIGRAPHX_DEVICE_MATH_VEC(exp)
+MIGRAPHX_DEVICE_MATH_VEC(floor)
+MIGRAPHX_DEVICE_MATH_VEC(log)
+MIGRAPHX_DEVICE_MATH_VEC(pow)
+MIGRAPHX_DEVICE_MATH_VEC(round)
+MIGRAPHX_DEVICE_MATH_VEC(rsqrt)
+MIGRAPHX_DEVICE_MATH_VEC(sin)
+MIGRAPHX_DEVICE_MATH_VEC(sinh)
+MIGRAPHX_DEVICE_MATH_VEC(sqrt)
+MIGRAPHX_DEVICE_MATH_VEC(tan)
+MIGRAPHX_DEVICE_MATH_VEC(tanh)
+MIGRAPHX_DEVICE_MATH_VEC(where)
+
+template <class T, class U>
+constexpr auto max(const T& a, const U& b)
+{
+    return where(a < b, b, a);
+}
+
+template <class T, class U>
+constexpr auto min(const T& a, const U& b)
+{
+    return where(a > b, b, a);
+}
+
+template <class T, class U>
+constexpr auto convert(U v)
+{
+    return vec_transform(v)([](auto x) -> T { return x; });
+}
+
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_KERNELS_MATH_HPP

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

@@ -3,19 +3,45 @@
 
 #include <migraphx/kernels/index.hpp>
 #include <migraphx/kernels/functional.hpp>
+#include <migraphx/kernels/math.hpp>
 #include <migraphx/kernels/preload.hpp>
 #include <migraphx/kernels/vectorize.hpp>
 #include <migraphx/kernels/args.hpp>
 
 namespace migraphx {
 
+template <class T>
+struct implicit_conversion_op
+{
+    T x;
+
+    template <index_int N, class U>
+    constexpr operator vec<U, N>() const
+    {
+        static_assert(vec_size<T>() == N, "Vector mismatch size");
+        return __builtin_convertvector(x, vec<U, N>);
+    }
+
+    template <class U>
+    constexpr operator U() const
+    {
+        return x;
+    }
+};
+
+template <class T>
+constexpr implicit_conversion_op<T> implicit_conversion(T x)
+{
+    return {x};
+}
+
 template <class F, class T, class... Ts>
 __device__ void pointwise_tensor(index idx, F f, T out, Ts... xs)
 {
     preload<typename T::type>(idx, xs...)([&](auto... ps) {
         idx.global_stride(out.get_shape().elements(), [&](auto i) {
             auto multi_idx = out.get_shape().multi(i);
-            out[multi_idx] = f(ps[multi_idx]...);
+            out[multi_idx] = implicit_conversion(f(ps[multi_idx]...));
         });
     });
 }
@@ -23,7 +49,7 @@ __device__ void pointwise_tensor(index idx, F f, T out, Ts... xs)
 template <class F, class... Ts>
 __device__ void pointwise(F f, Ts*... ps)
 {
-    auto t = transform_args(make_tensors(), rotate_last());
+    auto t = transform_args(make_tensors(), rotate_last(), auto_vectorize());
     t(ps...)([&](auto... xs) {
         auto idx = make_index();
         pointwise_tensor(idx, f, xs...);

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

@@ -29,7 +29,7 @@ constexpr auto traverse_preload(Shapes... ss)
 }
 
 template <class T, class... Shapes>
-constexpr index_int compute_preload_size(Shapes...)
+constexpr index_int compute_preload_size_c(Shapes...)
 {
     index_int size = 0;
     traverse_preload<T>(Shapes{}...)(
@@ -37,6 +37,12 @@ constexpr index_int compute_preload_size(Shapes...)
     return size;
 }
 
+template <class T, class... Shapes>
+constexpr auto compute_preload_size(Shapes...)
+{
+    return _c<compute_preload_size_c<T>(Shapes{}...)>;
+}
+
 template <class F, class T, class... Ts>
 __device__ auto preload_copy(index idx, F f, __shared__ T* buffer, Ts... xs)
 {
@@ -48,11 +54,21 @@ __device__ auto preload_copy(index idx, F f, __shared__ T* buffer, Ts... xs)
         [&](auto x, auto offset, auto copy) {
             if constexpr(copy)
             {
-                auto v = vectorize(x);
-                auto b = as_vec(tensor_vec_size(v), buffer + offset);
-                idx.local_stride(v.get_shape().element_space(),
-                                 [&](auto i) { b[i] = v.data()[i]; });
-                return x.with(buffer + offset);
+                if constexpr(decltype(tensor_vec_size(x)){} == 0)
+                {
+                    auto v = vectorize(x);
+                    auto b = as_vec(tensor_vec_size(v), buffer + offset);
+                    idx.local_stride(v.get_shape().element_space(),
+                                     [&](auto i) { b[i] = v.data()[i]; });
+                    return x.with(buffer + offset);
+                }
+                else
+                {
+                    auto b = as_vec(tensor_vec_size(x), buffer + offset);
+                    idx.local_stride(x.get_shape().element_space(),
+                                     [&](auto i) { b[i] = x.data()[i]; });
+                    return x.with(b);
+                }
             }
             else
             {
@@ -78,7 +94,7 @@ template <class T, class... Ts>
 __device__ auto preload(index idx, Ts... xs)
 {
     using type               = typename remove_vec<T>::type;
-    constexpr auto size      = compute_preload_size<type>(xs.get_shape()...);
+    constexpr auto size      = decltype(compute_preload_size<type>(xs.get_shape()...)){};
     const index_int max_size = 512 * sizeof(type);
     return [=](auto f) {
         if constexpr(size > 0 and size < max_size)

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

@@ -3,6 +3,7 @@
 
 #include <migraphx/kernels/types.hpp>
 #include <migraphx/kernels/integral_constant.hpp>
+#include <migraphx/kernels/functional.hpp>
 
 namespace migraphx {
 
@@ -24,6 +25,38 @@ constexpr auto vec_size()
     return decltype(vec_size(T{})){};
 }
 
+template <class... Ts>
+constexpr auto is_any_vec()
+{
+    if constexpr(sizeof...(Ts) == 0)
+        return false_type{};
+    else
+        return bool_constant<((vec_size<Ts>() + ...) > 0)>{};
+}
+
+template <class T, class I>
+constexpr auto vec_at(T x, I i)
+{
+    if constexpr(vec_size<T>() == 0)
+        return x;
+    else
+    {
+        MIGRAPHX_ASSERT(i < vec_size<T>());
+        return x[i];
+    }
+}
+
+template <class... Ts>
+constexpr auto common_vec_size()
+{
+    return fold([](auto x, auto y) {
+        if constexpr(x > y)
+            return x;
+        else
+            return y;
+    })(vec_size<Ts>()...);
+}
+
 template <index_int N, class T>
 __device__ __host__ auto as_vec(T* x)
 {
@@ -33,5 +66,28 @@ __device__ __host__ auto as_vec(T* x)
         return reinterpret_cast<vec<T, N>*>(x);
 }
 
+template <class T, index_int N>
+using safe_vec = vec<std::conditional_t<std::is_same<T, bool>{}, uint8_t, T>, N>;
+
+template <class... Ts>
+constexpr auto vec_transform(Ts... xs)
+{
+    return [=](auto f) {
+        if constexpr(is_any_vec<Ts...>())
+        {
+            using type                  = decltype(f(vec_at(xs, 0)...));
+            constexpr auto size         = common_vec_size<Ts...>();
+            safe_vec<type, size> result = {0};
+            for(int i = 0; i < size; i++)
+                result[i] = f(vec_at(xs, i)...);
+            return result;
+        }
+        else
+        {
+            return f(xs...);
+        }
+    };
+}
+
 } // namespace migraphx
 #endif // MIGRAPHX_GUARD_KERNELS_VEC_HPP

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

@@ -7,40 +7,70 @@
 namespace migraphx {
 
 template <class T>
-constexpr auto tensor_vec_size(T)
+constexpr auto tensor_vec_size()
 {
     return vec_size<typename T::type>();
 }
 
-template <index_int N, class Shape>
-constexpr auto as_vec_shape(Shape s)
+template <class T>
+constexpr auto tensor_vec_size(T)
 {
-    auto lens    = transform(s.lens, s.strides, [](auto len, auto stride) {
-        if(stride == 1)
-            return len / N;
-        else
-            return len;
-    });
-    auto strides = transform(s.strides, [](auto stride) {
-        if(stride == 1)
-            return stride;
-        return stride / N;
+    return tensor_vec_size<T>();
+}
+
+template <index_int N, class Shape, class Axis>
+constexpr auto shape_step(Shape s, Axis)
+{
+    static_assert(N > 0, "Vector size must be non-zero");
+    return sequence(s.lens.size(), [&](auto... is) {
+        auto lens    = transform(s.lens, index_ints<is...>{}, [&](auto i, auto j) {
+            constexpr auto axis = Axis::to();
+            MIGRAPHX_ASSERT(i != 0);
+            MIGRAPHX_ASSERT(j != axis or i % N == 0);
+            if(j == axis)
+                return i / N;
+            else
+                return i;
+        });
+        auto strides = transform(s.strides, index_ints<is...>{}, [&](auto i, auto j) {
+            constexpr auto axis = Axis::to();
+            // If stride of the axis is zero then we dont need to adjust the other strides
+            if(Shape{}.strides[axis] == 0)
+                return i;
+            MIGRAPHX_ASSERT(j == axis or i % N == 0);
+            if(j == axis)
+                return i;
+            else
+                return i / N;
+        });
+        MIGRAPHX_ASSERT(make_shape(lens, strides).elements() * N == s.elements());
+        MIGRAPHX_ASSERT(strides[Axis{}] == 0 or
+                        make_shape(lens, strides).element_space() * N == s.element_space());
+        return make_shape(lens, strides);
     });
-    MIGRAPHX_ASSERT(make_shape(lens, strides).element_space() * N == s.element_space());
-    return make_shape(lens, strides);
 }
 
-template <index_int N, class T>
-__device__ __host__ auto as_vec(T x)
+// Bools can not be used as a vector type so convert it to uint8
+template <class T>
+__device__ __host__ T* remove_bool(T* x)
+{
+    return x;
+}
+
+inline __device__ __host__ uint8_t* remove_bool(bool* x) { return reinterpret_cast<uint8_t*>(x); }
+
+template <index_int N, class T, class Axis>
+__device__ __host__ auto as_vec(T x, Axis axis)
 {
     if constexpr(N == 0)
         return x;
     else
-        return make_tensor_view(as_vec<N>(x.data()), as_vec_shape<N>(x.get_shape()));
+        return make_tensor_view(as_vec<N>(remove_bool(x.data())),
+                                shape_step<N>(x.get_shape(), axis));
 }
 
 template <index_int N, class T, class Axis>
-constexpr auto tensor_step(T x, Axis)
+constexpr auto tensor_step(T x, Axis axis)
 {
     if constexpr(N == 0)
     {
@@ -49,17 +79,8 @@ constexpr auto tensor_step(T x, Axis)
     else
     {
         constexpr auto s = decltype(x.get_shape()){};
-        MIGRAPHX_ASSERT(s.strides[Axis{}] == 0);
-        return sequence(x.get_shape().lens.size(), [&](auto... is) {
-            auto lens = transform(s.lens, index_ints<is...>{}, [&](auto i, auto j) {
-                constexpr auto axis = Axis{};
-                if(j == axis)
-                    return i / N;
-                else
-                    return i;
-            });
-            return make_tensor_view(x.data(), make_shape(lens, s.strides));
-        });
+        MIGRAPHX_ASSERT(s.strides[axis] == 0);
+        return make_tensor_view(x.data(), shape_step<N>(s, axis));
     }
 }
 
@@ -69,42 +90,71 @@ __device__ __host__ auto as_vec(IntegralConstant ic, T&& x)
     return as_vec<ic>(x);
 }
 
-template <class... Shapes>
-constexpr index_int find_vector_axis(Shapes... ss)
+template <class Shape>
+constexpr index_int find_vector_axis_c(Shape s)
 {
+    // Find the fastest axis that is not broadcasted
     index_int axis = 0;
-    bool b         = false;
+    for(index_int i = 1; i < s.lens.size(); i++)
+    {
+        if(s.strides[i] == 0)
+            continue;
+        if(s.strides[axis] == 0 or
+           pack_compare(less{}, pack(s.strides[i], s.lens[i]), pack(s.strides[axis], s.lens[axis])))
+            axis = i;
+    }
+    return axis;
+}
+
+template <class... Shapes>
+constexpr index_int find_vector_axis_c(Shapes... ss)
+{
+    const bool all_broadcasted = (ss.broadcasted() and ...);
+    index_int axis             = 0;
+    bool b                     = false;
     by([&](auto s) {
-        if(s.broadcasted() or b)
+        if(b)
             return;
-        auto it = find(s.strides.begin(), s.strides.end(), 1);
-        if(it == s.strides.end())
+        // Skip broadcasted shapes if there are shapes not broadcasted
+        if(not all_broadcasted and s.broadcasted())
             return;
-        axis = it - s.strides.begin();
-        b    = true;
+        axis = find_vector_axis_c(s);
+        if(s.strides[axis] == 1)
+            b = true;
     })(ss...);
+    if(not b)
+        return -1;
     return axis;
 }
 
+template <class... Shapes>
+constexpr auto find_vector_axis(Shapes...)
+{
+    return _c<find_vector_axis_c(Shapes{}...)>;
+}
+
 template <index_int N, class Axis, class... Shapes>
-constexpr auto is_vectorizable(Axis axis, Shapes... ss)
+constexpr auto is_vectorizable_c(Axis axis, Shapes... ss)
 {
-    return (((ss.lens[axis] % N) == 0 and (ss.strides[axis] == 1 or ss.strides[axis] == 0)) and
+    return ((axis < ss.lens.size() and ss.lens[axis] % N == 0 and
+             // Only vectorize broadcasted types with stride 0, since this causes issues in the
+             // preloader
+             ((not ss.broadcasted() and ss.strides[axis] == 1) or ss.strides[axis] == 0)) and
             ...);
 }
 
-template <index_int N, class... Shapes>
-constexpr bool is_vectorizable(Shapes... ss)
+template <index_int N, class Axis, class... Shapes>
+constexpr auto is_vectorizable(Axis, Shapes...)
 {
-    return (is_vectorizable<N>(ss, find_vector_axis(ss)) and ...);
+    return _c<is_vectorizable_c<N>(Axis::to(), Shapes{}...)>;
 }
 
 template <class P>
 constexpr auto find_vectorize_size(P pred)
 {
-    if constexpr(pred(_c<4>))
+    if constexpr(decltype(pred(_c<4>)){})
         return _c<4>;
-    else if constexpr(pred(_c<2>))
+    else if constexpr(decltype(pred(_c<2>)){})
         return _c<2>;
     else
         return _c<0>;
@@ -113,11 +163,12 @@ constexpr auto find_vectorize_size(P pred)
 template <class T>
 __host__ __device__ auto vectorize(T x)
 {
-    if constexpr(vec_size<T>() == 0)
+    if constexpr(tensor_vec_size<T>() == 0)
     {
+        constexpr auto axis = find_vector_axis(x.get_shape());
         constexpr auto n =
-            find_vectorize_size([&](auto i) { return _c<is_vectorizable<i>(x.get_shape())>; });
-        return as_vec<n>(x);
+            find_vectorize_size([&](auto i) { return is_vectorizable<i>(axis, x.get_shape()); });
+        return as_vec<n>(x, axis);
     }
     else
     {
@@ -125,34 +176,46 @@ __host__ __device__ auto vectorize(T x)
     }
 }
 
+template <class F, class... Ts>
+inline __device__ __host__ auto auto_vectorize_impl(F f, Ts... xs)
+{
+    // TODO: Just check there a single axis of 1
+    constexpr bool packed_or_broadcasted =
+        ((xs.get_shape().packed() or xs.get_shape().broadcasted()) and ...);
+    if constexpr(packed_or_broadcasted)
+    {
+        constexpr auto axis = decltype(find_vector_axis(xs.get_shape()...)){};
+        constexpr auto n    = find_vectorize_size(
+            [&](auto i) { return is_vectorizable<i>(axis, xs.get_shape()...); });
+        by(
+            [&](auto x) {
+                constexpr auto s = decltype(x.get_shape()){};
+                if constexpr(axis < s.strides.size())
+                {
+                    MIGRAPHX_ASSERT(s.strides[axis] == 0 or s.strides[axis] == 1);
+                    MIGRAPHX_ASSERT(s.lens[axis] > 0);
+                    MIGRAPHX_ASSERT(n == 0 or s.lens[axis] % n == 0);
+                    if constexpr(s.strides[axis] == 0)
+                        return tensor_step<n>(x, axis);
+                    else
+                        return as_vec<n>(x, axis);
+                }
+                else
+                {
+                    return x;
+                }
+            },
+            f)(xs...);
+    }
+    else
+    {
+        f(xs...);
+    }
+}
+
 inline __device__ __host__ auto auto_vectorize()
 {
-    return [](auto... xs) {
-        return [=](auto f) {
-            // TODO: Just check there a single axis of 1
-            constexpr bool packed_or_broadcasted =
-                ((xs.get_shape().packed() or xs.get_shape().broadcasted()) and ...);
-            if constexpr(packed_or_broadcasted)
-            {
-                constexpr auto axis = find_vector_axis(xs.get_shape()...);
-                constexpr auto n    = find_vectorize_size(
-                    [&](auto i) { return _c<is_vectorizable<i>(axis, xs.get_shape()...)>; });
-                by(
-                    [&](auto x) {
-                        constexpr auto s = x.get_shape();
-                        if constexpr(s.strides[axis] == 0)
-                            return tensor_step<n>(x, axis);
-                        else
-                            return as_vec<n>(x);
-                    },
-                    f)(xs...);
-            }
-            else
-            {
-                f(xs...);
-            }
-        };
-    };
+    return [](auto... xs) { return [=](auto f) { auto_vectorize_impl(f, xs...); }; };
 }
 
 } // namespace migraphx

test/auto_contiguous_test.cpp

@@ -101,4 +101,38 @@ TEST_CASE(after_param_broadcast)
     EXPECT(not m.get_output_shapes().back().broadcasted());
 }
 
+TEST_CASE(two_transpose_gather)
+{
+    migraphx::module m1;
+    {
+        auto data = m1.add_parameter("2x2", {migraphx::shape::float_type, {2, 3, 4, 5}});
+        auto ind  = m1.add_parameter("ind", {migraphx::shape::float_type, {2, 3}});
+        auto td   = m1.add_instruction(
+            migraphx::make_op("transpose", {{"permutation", {0, 2, 3, 1}}}), data);
+        auto sd = m1.add_instruction(migraphx::make_op("softmax", {{"axis", 2}}), td);
+        auto bd =
+            m1.add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 3, 1, 2}}}), sd);
+        auto r = m1.add_instruction(migraphx::make_op("gather", {{"axis", 2}}), bd, ind);
+        m1.add_return({r});
+    }
+    run_pass(m1);
+
+    migraphx::module m2;
+    {
+        auto data = m2.add_parameter("2x2", {migraphx::shape::float_type, {2, 3, 4, 5}});
+        auto ind  = m2.add_parameter("ind", {migraphx::shape::float_type, {2, 3}});
+        auto td   = m2.add_instruction(
+            migraphx::make_op("transpose", {{"permutation", {0, 2, 3, 1}}}), data);
+        auto ctd = m2.add_instruction(migraphx::make_op("contiguous"), td);
+        auto sd  = m2.add_instruction(migraphx::make_op("softmax", {{"axis", 2}}), ctd);
+        auto bd =
+            m2.add_instruction(migraphx::make_op("transpose", {{"permutation", {0, 3, 1, 2}}}), sd);
+        auto cbd = m2.add_instruction(migraphx::make_op("contiguous"), bd);
+        auto r   = m2.add_instruction(migraphx::make_op("gather", {{"axis", 2}}), cbd, ind);
+        m2.add_return({r});
+    }
+
+    EXPECT(m1 == m2);
+}
+
 int main(int argc, const char* argv[]) { test::run(argc, argv); }