Merge branch 'develop' into gpu-batch-gemm-bert

src/targets/gpu/analyze_streams.cpp

@@ -28,30 +28,30 @@ struct hip_stream_model
     bool is_wait(migraphx::instruction_ref ins) const { return ins->name() == "gpu::wait_event"; }
 };
 
-stream_model make_stream_model(const module& p)
+stream_model make_stream_model(const module& m)
 {
-    hip_stream_model m;
+    hip_stream_model hsm;
     std::size_t stream = 0;
-    for(auto ins : iterator_for(p))
+    for(auto ins : iterator_for(m))
     {
         if(ins->name() == "gpu::set_stream")
         {
             auto v         = ins->get_operator().to_value();
             stream         = v["stream"].to<std::size_t>();
-            m.max_stream = std::max(stream, m.max_stream);
+            hsm.max_stream = std::max(stream, hsm.max_stream);
         }
         if(ins->get_operator().is_context_free())
             continue;
         if(contains({"hip::hip_allocate_memory", "hip::hip_copy_literal", "@param"}, ins->name()))
             continue;
-        m.ins2stream[ins] = stream;
+        hsm.ins2stream[ins] = stream;
     }
-    return m;
+    return hsm;
 }
 
-std::vector<stream_race> analyze_streams(const module& p)
+std::vector<stream_race> analyze_streams(const module& m)
 {
-    return migraphx::analyze_streams(p, make_stream_model(p));
+    return migraphx::analyze_streams(m, make_stream_model(m));
 }
 
 } // namespace gpu

src/targets/gpu/compile_hip.cpp

@@ -22,6 +22,7 @@ namespace gpu {
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DEBUG);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_OPTIMIZE);
 MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DUMP_ASM);
+MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_GPU_DUMP_SRC);
 
 #if MIGRAPHX_USE_HIPRTC
 
@@ -133,6 +134,7 @@ struct hiprtc_program
         std::vector<char> buffer(n);
         MIGRAPHX_HIPRTC(hiprtcGetProgramLog(prog.get(), buffer.data()));
         assert(buffer.back() == 0);
+        // cppcheck-suppress returnDanglingLifetime
         return {buffer.begin(), buffer.end() - 1};
     }
 
@@ -246,6 +248,16 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
             MIGRAPHX_THROW("Missing hsaco");
         };
 
+    if(enabled(MIGRAPHX_GPU_DUMP_SRC{}))
+    {
+        for(const auto& src : srcs)
+        {
+            if(src.path.extension() != ".cpp")
+                continue;
+            std::cout << std::string(src.content.first, src.len()) << std::endl;
+        }
+    }
+
     if(enabled(MIGRAPHX_GPU_DUMP_ASM{}))
     {
 

src/targets/gpu/eliminate_workspace.cpp

@@ -11,11 +11,11 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-void eliminate_workspace::apply(module& p) const
+void eliminate_workspace::apply(module& m) const
 {
     std::size_t n = 0;
     std::vector<instruction_ref> allocs;
-    for(auto ins : iterator_for(p))
+    for(auto ins : iterator_for(m))
     {
         if(ins->outputs().size() != 1)
             continue;
@@ -30,11 +30,11 @@ void eliminate_workspace::apply(module& p) const
     }
     if(n > 0)
     {
-        auto ws = p.add_parameter("workspace", shape{shape::int8_type, {n}});
+        auto ws = m.add_parameter("workspace", shape{shape::int8_type, {n}});
         for(auto&& a : allocs)
         {
-            p.replace_instruction(a, ws);
-            p.remove_instruction(a);
+            m.replace_instruction(a, ws);
+            m.remove_instruction(a);
         }
     }
 }

src/targets/gpu/fuse_ops.cpp

@@ -316,7 +316,7 @@ struct find_layernorm
 {
     auto matcher() const { return match::layernorm(&gpu_name); }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins   = r.result;
         auto x_ins = r.instructions["x"];
@@ -331,7 +331,7 @@ struct find_layernorm
         if(relements > 1024 or (relements % 4 != 0 and relements > 256))
             return;
 
-        p.replace_instruction(ins, hip_layernorm{}, x_ins, args.back());
+        m.replace_instruction(ins, hip_layernorm{}, x_ins, args.back());
     }
 };
 
@@ -343,11 +343,11 @@ struct find_triadd_layernorm
             match::used_once(), match::all_of[match::inputs()](match::standard_shape()))));
     }
 
-    void apply(module& p, const match::matcher_result& r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins    = r.result;
         auto triadd = ins->inputs().front();
-        p.replace_instruction(ins, hip_triadd_layernorm{}, triadd->inputs());
+        m.replace_instruction(ins, hip_triadd_layernorm{}, triadd->inputs());
     }
 };
 
@@ -355,13 +355,13 @@ struct find_gelu
 {
     auto matcher() const { return match::gelu_erf(&gpu_name); }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins   = r.result;
         auto x_ins = r.instructions["x"];
         auto args  = ins->inputs();
 
-        p.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
+        m.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
     }
 };
 
@@ -372,7 +372,7 @@ struct find_add_gelu
         return match::name("gpu::gelu")(match::arg(0)(match::name("gpu::add").bind("add")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto add_ins = r.instructions["add"];
         auto ins     = r.result;
@@ -381,7 +381,7 @@ struct find_add_gelu
         move_broadcasted_back(args);
 
         args.back() = ins->inputs().back();
-        p.replace_instruction(ins, hip_add_gelu{}, args);
+        m.replace_instruction(ins, hip_add_gelu{}, args);
     }
 };
 
@@ -391,16 +391,16 @@ struct find_gelu_new
 
     auto matcher() const { return match::gelu_tanh(&gpu_name); }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins   = r.result;
         auto x_ins = r.instructions["x"];
         auto args  = ins->inputs();
 
         if(fast_math)
-            p.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
+            m.replace_instruction(ins, hip_gelu{}, x_ins, args.back());
         else
-            p.replace_instruction(ins, hip_gelu_new{}, x_ins, args.back());
+            m.replace_instruction(ins, hip_gelu_new{}, x_ins, args.back());
     }
 };
 
@@ -411,7 +411,7 @@ struct find_add_gelu_new
         return match::name("gpu::gelu_new")(match::arg(0)(match::name("gpu::add").bind("add")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto add_ins = r.instructions["add"];
         auto ins     = r.result;
@@ -420,7 +420,7 @@ struct find_add_gelu_new
         move_broadcasted_back(args);
 
         args.back() = ins->inputs().back();
-        p.replace_instruction(ins, hip_add_gelu_new{}, args);
+        m.replace_instruction(ins, hip_add_gelu_new{}, args);
     }
 };
 
@@ -435,7 +435,7 @@ struct find_add_clip
                               .bind("add")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto add_ins  = r.instructions["add"];
         auto ins      = r.result;
@@ -448,9 +448,9 @@ struct find_add_clip
         add_args.pop_back();
         add_args.insert(add_args.end(), std::next(ins_args.begin()), ins_args.end());
         if(add_ins->name() == "gpu::add")
-            p.replace_instruction(ins, hip_add_clip{}, add_args);
+            m.replace_instruction(ins, hip_add_clip{}, add_args);
         else if(add_ins->name() == "gpu::triadd")
-            p.replace_instruction(ins, hip_triadd_clip{}, add_args);
+            m.replace_instruction(ins, hip_triadd_clip{}, add_args);
     }
 };
 
@@ -470,7 +470,7 @@ struct find_add_unary
                 .bind("add")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto add_ins = r.instructions["add"];
         auto ins     = r.result;
@@ -481,9 +481,9 @@ struct find_add_unary
         // Use the allocation from the relu operator
         args.back() = ins->inputs().back();
         if(add_ins->name() == "gpu::add")
-            p.replace_instruction(ins, binary_add_op, args);
+            m.replace_instruction(ins, binary_add_op, args);
         else if(add_ins->name() == "gpu::triadd")
-            p.replace_instruction(ins, ternary_add_op, args);
+            m.replace_instruction(ins, ternary_add_op, args);
     }
 };
 
@@ -498,7 +498,7 @@ struct find_triadd
                 .bind("input")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto add_ins   = r.instructions["add"];
         auto input_ins = r.instructions["input"];
@@ -513,7 +513,7 @@ struct find_triadd
         move_broadcasted_back(args);
 
         args.back() = ins->inputs().back();
-        p.replace_instruction(ins, hip_triadd{}, args);
+        m.replace_instruction(ins, hip_triadd{}, args);
     }
 };
 
@@ -525,7 +525,7 @@ struct find_mul_add
             match::name("gpu::mul")(match::used_once()).bind("mul"), match::any().bind("b")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto mul_ins = r.instructions["mul"];
         auto b_ins   = r.instructions["b"];
@@ -538,7 +538,7 @@ struct find_mul_add
         args.insert(std::prev(args.end()), b_ins);
 
         args.back() = ins->inputs().back();
-        p.replace_instruction(ins, hip_mul_add{}, args);
+        m.replace_instruction(ins, hip_mul_add{}, args);
     }
 };
 
@@ -550,7 +550,7 @@ struct find_mul_add_relu
             match::arg(0)(match::name("gpu::mul_add")(match::used_once()).bind("mul_add")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto mul_add_ins = r.instructions["mul_add"];
         auto ins         = r.result;
@@ -558,7 +558,7 @@ struct find_mul_add_relu
 
         // Use the allocation from the relu operator
         args.back() = ins->inputs().back();
-        p.replace_instruction(ins, hip_mul_add_relu{}, args);
+        m.replace_instruction(ins, hip_mul_add_relu{}, args);
     }
 };
 
@@ -783,7 +783,7 @@ auto conv_bias(Ms... ms)
 }
 
 template <class Op>
-void apply_conv_bias(context& ctx, module& p, match::matcher_result r)
+void apply_conv_bias(context& ctx, module& m, const match::matcher_result& r)
 {
     auto conv_ins    = r.instructions["conv"];
     auto bias_ins    = r.instructions["bias"];
@@ -798,7 +798,7 @@ void apply_conv_bias(context& ctx, module& p, match::matcher_result r)
     // TODO: Insert ws allocation
     auto ws = cb.get_workspace(ctx);
     (void)ws;
-    p.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
+    m.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
 }
 
 inline auto precompile_name(std::string s) // NOLINT
@@ -829,9 +829,9 @@ struct find_conv_bias
             match::output(match::name(std::unordered_set<std::string>{"gpu::relu"}))));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
-        apply_conv_bias<miopen_conv_bias>(*ctx, p, std::move(r));
+        apply_conv_bias<miopen_conv_bias>(*ctx, m, r);
     }
 };
 
@@ -840,9 +840,9 @@ struct find_conv_bias_relu
     context* ctx = nullptr;
     auto matcher() const { return match::name("gpu::relu")(match::arg(0)(conv_bias())); }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
-        apply_conv_bias<miopen_conv_bias_relu>(*ctx, p, std::move(r));
+        apply_conv_bias<miopen_conv_bias_relu>(*ctx, m, r);
     }
 };
 
@@ -857,7 +857,7 @@ struct find_conv_pointwise
                                     fusable_conv(match::used_once()).bind("conv")));
     }
 
-    void apply(module& m, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto conv_ins    = r.instructions["conv"];
         auto bias_ins    = r.instructions["bias"];
@@ -896,7 +896,7 @@ struct find_gemm_add
                                     match::name("gpu::gemm")(match::nargs(3)).bind("gemm")));
     }
 
-    void apply(module& p, match::matcher_result r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins      = r.result;
         auto gemm_ins = r.instructions["gemm"];
@@ -919,15 +919,15 @@ struct find_gemm_add
         auto copy_ins = c_ins;
 
         // Insert copy
-        if(ins == p.end() or c_ins->outputs().size() > 1 or c_ins->inputs().empty())
+        if(ins == m.end() or c_ins->outputs().size() > 1 or c_ins->inputs().empty())
         {
-            copy_ins = p.insert_instruction(ins, hip_copy{}, c_ins, ins->inputs().back());
+            copy_ins = m.insert_instruction(ins, hip_copy{}, c_ins, ins->inputs().back());
         }
         inputs.push_back(copy_ins);
         inputs.push_back(copy_ins);
 
         gemm.beta = 1;
-        p.replace_instruction(ins, gemm, inputs);
+        m.replace_instruction(ins, gemm, inputs);
     }
 };
 
@@ -938,22 +938,22 @@ struct find_commutative_broadcast
         return match::name("gpu::add", "gpu::mul")(match::arg(1)(match::broadcast_shape()));
     }
 
-    void apply(module& p, const match::matcher_result& r) const
+    void apply(module& m, const match::matcher_result& r) const
     {
         auto ins  = r.result;
         auto args = ins->inputs();
         move_broadcasted_back(args);
 
-        p.replace_instruction(ins, ins->get_operator(), args);
+        m.replace_instruction(ins, ins->get_operator(), args);
     }
 };
 
-void fuse_ops::apply(module& p) const
+void fuse_ops::apply(module& m) const
 {
-    match::find_matches(p, find_gelu{}, find_gelu_new{fast_math});
-    run_passes(p, {dead_code_elimination{}});
-    match::find_matches(p, find_triadd{});
-    match::find_matches(p,
+    match::find_matches(m, find_gelu{}, find_gelu_new{fast_math});
+    run_passes(m, {dead_code_elimination{}});
+    match::find_matches(m, find_triadd{});
+    match::find_matches(m,
                         find_layernorm{},
                         find_conv_pointwise{ctx},
                         find_conv_bias_relu{ctx},
@@ -966,8 +966,8 @@ void fuse_ops::apply(module& p) const
                         find_add_unary{"gpu::sigmoid", hip_add_sigmoid{}, hip_triadd_sigmoid{}},
                         find_add_unary{"gpu::tanh", hip_add_tanh{}, hip_triadd_tanh{}},
                         find_add_clip{});
-    run_passes(p, {dead_code_elimination{}});
-    match::find_matches(p, find_triadd_layernorm{}, find_gemm_add{}, find_commutative_broadcast{});
+    run_passes(m, {dead_code_elimination{}});
+    match::find_matches(m, find_triadd_layernorm{}, find_gemm_add{}, find_commutative_broadcast{});
 }
 
 } // namespace gpu

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

@@ -11,7 +11,7 @@ struct module;
 
 namespace gpu {
 
-std::vector<stream_race> analyze_streams(const module& p);
+std::vector<stream_race> analyze_streams(const module& m);
 
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

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

@@ -14,7 +14,7 @@ namespace gpu {
 struct eliminate_workspace
 {
     std::string name() const { return "eliminate_workspace"; }
-    void apply(module& p) const;
+    void apply(module& m) const;
 };
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

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

@@ -16,7 +16,7 @@ struct fuse_ops
     context* ctx   = nullptr;
     bool fast_math = true;
     std::string name() const { return "gpu::fuse_ops"; }
-    void apply(module& p) const;
+    void apply(module& m) const;
 };
 
 } // namespace gpu

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

+#ifndef MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP
+#define MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP
+
+#include <migraphx/config.hpp>
+#include <migraphx/gpu/context.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+
+struct module;
+
+namespace gpu {
+
+struct prefuse_ops
+{
+    std::string name() const { return "gpu::prefuse_ops"; }
+    void apply(module& m) const;
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif // MIGRAPHX_GUARD_GPU_PREFUSE_OPS_HPP

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

@@ -17,9 +17,9 @@ struct schedule_model
 {
     std::size_t streams = 0;
     std::size_t concurrency() const;
-    void sched(module& p, instruction_ref ins, std::size_t n) const;
-    void wait(module& p, instruction_ref ins, std::size_t wait_id) const;
-    void record(module& p, instruction_ref ins, std::size_t wait_id) const;
+    void sched(module& m, instruction_ref ins, std::size_t n) const;
+    void wait(module& m, instruction_ref ins, std::size_t wait_id) const;
+    void record(module& m, instruction_ref ins, std::size_t wait_id) const;
     std::size_t weight(const operation& op) const;
 };
 

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

@@ -15,7 +15,7 @@ namespace gpu {
 struct sync_device
 {
     std::string name() const { return "sync_device"; }
-    void apply(module& p) const;
+    void apply(module& m) const;
 };
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

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

@@ -14,7 +14,7 @@ struct write_literals
     context* ctx = nullptr;
     std::string name() const { return "gpu::write_literals"; }
 
-    void apply(module& p) const;
+    void apply(module& m) const;
 };
 
 } // namespace gpu

src/targets/gpu/jit/pointwise.cpp

@@ -6,6 +6,7 @@
 #include <migraphx/cpp_generator.hpp>
 #include <migraphx/ranges.hpp>
 #include <migraphx/reduce_dims.hpp>
+#include <migraphx/permutation.hpp>
 #include <migraphx/stringutils.hpp>
 #include <migraphx/dead_code_elimination.hpp>
 #include <migraphx/eliminate_common_subexpression.hpp>
@@ -28,7 +29,8 @@ ${preamble}
 extern "C" {
 __global__ void kernel(${params}) 
 {
-    pointwise(${lambda}, ${args});
+    auto idx = make_index();
+    pointwise(idx, auto_preload<${preloads}>(idx), vectorize<${vec_size}, ${axis}>())(${lambda}, ${args});
 }
     
 }
@@ -41,40 +43,105 @@ struct pointwise_compiler : compiler<pointwise_compiler>
 {
     std::vector<std::string> names() const { return {"pointwise"}; }
 
-    static std::size_t oversubscribe(const std::vector<shape>& inputs)
+    static std::size_t oversubscribe_if(bool b)
     {
-        if(std::any_of(inputs.begin(), inputs.end(), [](const auto& s) { return s.broadcasted(); }))
-            return 1;
-        else
+        if(b)
             return 256;
+        else
+            return 1;
+    }
+    static std::size_t find_fast_axis(const std::vector<shape>& inputs)
+    {
+        auto permutation = find_permutation(inputs);
+        auto it          = std::max_element(permutation.begin(), permutation.end());
+        return it - permutation.begin();
+    }
+    static std::vector<bool> preload(std::size_t axis, const std::vector<shape>& inputs)
+    {
+        const std::size_t max_lds_bytes = 4096;
+        std::vector<bool> result;
+        std::transform(inputs.begin(),
+                       inputs.end(),
+                       std::back_inserter(result),
+                       [&](const shape& input) { return input.strides()[axis] == 0; });
+        auto bytes = std::inner_product(inputs.begin(),
+                                        inputs.end(),
+                                        result.begin(),
+                                        std::size_t{0},
+                                        std::plus<>{},
+                                        [](const shape& s, bool b) -> std::size_t {
+                                            if(b)
+                                                return s.bytes();
+                                            return 0;
+                                        });
+        if(bytes < max_lds_bytes)
+            return result;
+        // TODO: Try to partially preload items
+        std::fill(result.begin(), result.end(), false);
+        return result;
+    }
+    static std::string preload_str(const std::vector<bool>& bs)
+    {
+        std::vector<std::string> bool_strs;
+        std::transform(bs.begin(), std::prev(bs.end()), std::back_inserter(bool_strs), [](bool b) {
+            if(b)
+                return "true";
+            return "false";
+        });
+        return "false, " + join_strings(bool_strs, ", ");
     }
-    static std::size_t vectorize_elements(const std::vector<shape>& inputs)
+    static std::vector<std::size_t> vector_sizes(const std::vector<shape>& inputs)
     {
-        std::size_t n = inputs.front().elements();
+        // If all inputs is half then only use half2
         if(std::all_of(inputs.begin(), inputs.end(), [](const auto& s) {
-               return s.packed() or s.broadcasted();
+               return s.type() == shape::half_type;
            }))
-        {
-            if((n % 4) == 0)
-                return n / 4;
-            else if((n % 2) == 0)
-                return n / 2;
+            return {2};
+        return {4, 2};
     }
-        return n;
+    static auto vectorize_elements(std::size_t axis, const std::vector<shape>& inputs)
+    {
+        auto sizes = vector_sizes(inputs);
+        std::vector<std::size_t> max_vec_size;
+        std::transform(inputs.begin(),
+                       inputs.end(),
+                       std::back_inserter(max_vec_size),
+                       [&](const auto& input) -> std::size_t {
+                           auto stride = input.strides()[axis];
+                           auto len    = input.lens()[axis];
+                           if(stride != 0 and stride != 1)
+                               return 1;
+                           auto it = std::find_if(
+                               sizes.begin(), sizes.end(), [&](auto i) { return (len % i) == 0; });
+                           if(it != sizes.end())
+                               return *it;
+                           return 1;
+                       });
+        return *std::min_element(max_vec_size.begin(), max_vec_size.end());
     }
     operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
     {
         hip_compile_options options;
-        options.set_launch_params(
-            v, compute_global_for(ctx, vectorize_elements(inputs), oversubscribe(inputs)));
         options.inputs         = inputs;
         options.output         = inputs.back();
         options.virtual_inputs = reduce_dims(inputs);
         options.params         = "-Wno-float-equal";
+        auto axis              = find_fast_axis(options.virtual_inputs);
+        auto vec_size          = vectorize_elements(axis, options.virtual_inputs);
+        auto preloads          = preload(axis, options.virtual_inputs);
+        auto is_preloading =
+            std::accumulate(preloads.begin(), preloads.end(), false, std::logical_or<>{});
+        options.set_launch_params(v,
+                                  compute_global_for(ctx,
+                                                     options.output.elements() / vec_size,
+                                                     oversubscribe_if(not is_preloading)));
         auto src = interpolate_string(pointwise_kernel,
                                       {{"params", enum_params(inputs.size(), "void * private_p")},
                                        {"args", enum_params(inputs.size(), "private_p")},
                                        {"lambda", v.at("lambda").to<std::string>()},
+                                       {"vec_size", std::to_string(vec_size)},
+                                       {"axis", std::to_string(axis)},
+                                       {"preloads", preload_str(preloads)},
                                        {"preamble", v.get("preamble", std::string{})}});
         return compile_hip_code_object(src, options);
     }

src/targets/gpu/jit/scatternd.cpp

@@ -52,9 +52,8 @@ struct scatternd_compiler : compiler<scatternd_compiler>
     {
         hip_compile_options options;
         options.set_launch_params(v, compute_global_for(ctx, inputs.at(1).elements()));
-        auto out_s             = inputs.back();
         options.inputs         = inputs;
-        options.output         = out_s;
+        options.output         = inputs.back();
         options.kernel_name    = "scatternd_kernel";
         options.virtual_inputs = inputs;
         auto reduction         = "assign_" + v.get("reduction", std::string{"none"});

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

@@ -3,6 +3,14 @@
 
 #include <migraphx/kernels/array.hpp>
 
+// NOLINTNEXTLINE
+#define MIGRAPHX_RETURNS(...) \
+    ->decltype(__VA_ARGS__) { return __VA_ARGS__; }
+
+// NOLINTNEXTLINE
+#define MIGRAPHX_LIFT(...) \
+    [](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
+
 namespace migraphx {
 
 struct swallow
@@ -161,6 +169,18 @@ constexpr auto pack(Ts... xs)
     return [=](auto f) { return f(xs...); };
 }
 
+template <class G, class F>
+constexpr auto join(G g, F f)
+{
+    return f([=](auto... xs) { return g(xs...); });
+}
+
+template <class G, class F, class... Fs>
+constexpr auto join(G g, F f, Fs... fs)
+{
+    return f([=](auto... xs) { return join([=](auto... ys) { return g(xs..., ys...); }, fs...); });
+}
+
 template <class Compare, class P1, class P2>
 constexpr auto pack_compare(Compare compare, P1 p1, P2 p2)
 {
@@ -191,39 +211,45 @@ constexpr auto arg(IntegralConstant ic)
     return arg_c<ic>();
 }
 
-inline constexpr auto rotate_last()
+template <class F>
+constexpr auto make_transform(F f)
 {
-    return [](auto... xs) {
-        return [=](auto&& f) {
-            return sequence_c<sizeof...(xs)>([&](auto... is) {
-                constexpr auto size = sizeof...(is);
-                return f(arg_c<(is + size - 1) % size>()(xs...)...);
-            });
-        };
-    };
+    return [=](auto... xs) { return [=](auto g) { return f(g, xs...); }; };
 }
 
+// An arg transformation takes the arguments and then a function to take the new arguments:
+//     transform(xs...)([](auto... ys) { ... })
+// The transform_args function takes a list of transformations and continually applies them
 template <class F>
 constexpr auto transform_args(F f)
 {
-    return [=](auto... xs) {
-        return [=](auto g) { return f(xs...)([&](auto... ys) { return g(ys...); }); };
-    };
+    return f;
 }
 
 template <class F, class... Fs>
 constexpr auto transform_args(F f, Fs... fs)
 {
-    return [=](auto... xs) { return transform_args(f)(xs...)(transform_args(fs...)); };
+    return make_transform([=](auto g, auto... xs) {
+        return f(xs...)([=](auto... ys) { return transform_args(fs...)(ys...)(g); });
+    });
 }
 
-// NOLINTNEXTLINE
-#define MIGRAPHX_RETURNS(...) \
-    ->decltype(__VA_ARGS__) { return __VA_ARGS__; }
+// identity transform
+inline constexpr auto transform_args()
+{
+    return make_transform([](auto f, auto... xs) { return f(xs...); });
+}
 
-// NOLINTNEXTLINE
-#define MIGRAPHX_LIFT(...) \
-    [](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
+// Rotate the first argument to the last argument
+inline constexpr auto rotate_last()
+{
+    return make_transform([](auto f, auto... xs) {
+        return sequence_c<sizeof...(xs)>([&](auto... is) {
+            constexpr auto size = sizeof...(is);
+            return f(arg_c<(is + size - 1) % size>()(xs...)...);
+        });
+    });
+}
 
 } // namespace migraphx
 #endif // MIGRAPHX_GUARD_KERNELS_FUNCTIONAL_HPP

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

@@ -38,20 +38,17 @@ constexpr implicit_conversion_op<T> implicit_conversion(T 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) { out[i] = implicit_conversion(f(ps[i]...)); });
-    });
+                      [&](auto i) { out[i] = implicit_conversion(f(xs[i]...)); });
 }
 
-template <class F, class... Ts>
-__device__ void pointwise(F f, Ts*... ps)
+template <class... Transforms>
+__device__ auto pointwise(index idx, Transforms... transforms)
 {
-    auto t = transform_args(make_tensors(), rotate_last(), auto_vectorize());
-    t(ps...)([&](auto... xs) {
-        auto idx = make_index();
-        pointwise_tensor(idx, f, xs...);
-    });
+    return [=](auto f, auto*... ps) {
+        auto t = transform_args(make_tensors(), rotate_last(), transforms...);
+        t(ps...)([&](auto... xs) { pointwise_tensor(idx, f, xs...); });
+    };
 }
 
 } // namespace migraphx

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

@@ -3,6 +3,8 @@
 
 #include <migraphx/kernels/index.hpp>
 #include <migraphx/kernels/functional.hpp>
+#include <migraphx/kernels/tensor_view.hpp>
+#include <migraphx/kernels/vec.hpp>
 
 namespace migraphx {
 
@@ -73,7 +75,7 @@ __device__ auto preload_copy(index idx, F f, __shared__ T* buffer, Ts... xs)
             {
                 if constexpr(decltype(tensor_vec_size(x)){} == 0)
                 {
-                    auto v = vectorize(x);
+                    auto v = auto_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]; });
@@ -126,5 +128,47 @@ __device__ auto preload(index idx, Ts... xs)
     };
 }
 
+inline __device__ auto auto_preload(index idx)
+{
+    return make_transform([=](auto f, auto out, auto... xs) {
+        preload<typename decltype(out)::type>(idx, xs...)([&](auto... ys) { f(out, ys...); });
+    });
+}
+
+template <bool B, class T>
+__device__ auto preload_copy(index idx, T x)
+{
+    return [=](auto f) {
+        if constexpr(B)
+        {
+            using type          = typename T::type;
+            constexpr auto size = get_shape_c<T>{}.element_space();
+            __shared__ type buffer[size];
+            // TODO: Always vecotrize when size > 4, and then use a second loop for remainder
+            constexpr auto n = find_vectorize_size([&](auto i) { return (size % i) == 0; });
+            auto input       = as_vec<n>(remove_bool(x.data()));
+            auto b           = as_vec<n>(remove_bool(buffer));
+            idx.local_stride(size / n, [&](auto i) { b[i] = input[i]; });
+            return f(x.with(buffer));
+        }
+        else
+        {
+            return f(x);
+        }
+    };
+}
+
+template <bool... Bs>
+__device__ auto auto_preload(index idx)
+{
+    return make_transform([=](auto f, auto... xs) {
+        auto invoke = [=](auto... ys) {
+            __syncthreads();
+            f(ys...);
+        };
+        join(invoke, preload_copy<Bs>(idx, xs)...);
+    });
+}
+
 } // namespace migraphx
 #endif // MIGRAPHX_GUARD_KERNELS_PRELOAD_HPP

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

@@ -118,15 +118,13 @@ constexpr roalign_settings<Ts...> make_roalign_settings(Ts... xs)
 }
 
 template <class T, class U, class V, class W, class Settings>
-__device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, const W& y_t, Settings s)
+__device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, W& y_t, Settings s)
 {
     auto index      = make_index();
     const auto x    = x_t.begin();
     const auto rois = rois_t.begin();
     const auto ind  = ind_t.begin();
 
-    auto out_ptr = y_t.begin();
-
     // input shape
     auto x_lens      = x_t.get_shape().lens;
     auto channel_num = x_lens[1];
@@ -176,7 +174,7 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, const W&
         const auto offset_x = x + ((batch_ind * channel_num + c) * in_dims[0] * in_dims[1]);
         if constexpr(s.is_avg_pooling)
         {
-            out_ptr[i] = calc_pooling(offset_x,
+            y_t[i] = calc_pooling(offset_x,
                                   roi_starts,
                                   bin_size,
                                   {ph, pw},
@@ -187,7 +185,7 @@ __device__ void roialign(const T& x_t, const U& rois_t, const V& ind_t, const W&
         }
         else
         {
-            out_ptr[i] = calc_pooling(offset_x,
+            y_t[i] = calc_pooling(offset_x,
                                   roi_starts,
                                   bin_size,
                                   {ph, pw},

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

@@ -60,10 +60,19 @@ constexpr auto common_vec_size()
     })(vec_size<Ts>()...);
 }
 
+// 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>
 __device__ __host__ auto as_vec(T* x)
 {
-    if constexpr(N == 0)
+    if constexpr(N < 2)
         return x;
     else
         return reinterpret_cast<vec<T, N>*>(x);

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

@@ -50,19 +50,10 @@ constexpr auto shape_step(Shape s, Axis)
     });
 }
 
-// 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)
+    if constexpr(N < 2)
         return x;
     else
         return make_tensor_view(as_vec<N>(remove_bool(x.data())),
@@ -72,7 +63,7 @@ __device__ __host__ auto as_vec(T x, Axis axis)
 template <index_int N, class T, class Axis>
 constexpr auto tensor_step(T x, Axis axis)
 {
-    if constexpr(N == 0)
+    if constexpr(N < 2)
     {
         return x;
     }
@@ -157,11 +148,11 @@ constexpr auto find_vectorize_size(P pred)
     else if constexpr(decltype(pred(_c<2>)){})
         return _c<2>;
     else
-        return _c<0>;
+        return _c<1>;
 }
 
 template <class T>
-__host__ __device__ auto vectorize(T x)
+__host__ __device__ auto auto_vectorize(T x)
 {
     if constexpr(tensor_vec_size<T>() == 0)
     {
@@ -194,7 +185,7 @@ inline __device__ __host__ auto auto_vectorize_impl(F f, Ts... xs)
                 {
                     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);
+                    MIGRAPHX_ASSERT(n == 1 or s.lens[axis] % n == 0);
                     if constexpr(s.strides[axis] == 0)
                         return tensor_step<n>(x, axis);
                     else
@@ -215,7 +206,32 @@ inline __device__ __host__ auto auto_vectorize_impl(F f, Ts... xs)
 
 inline __device__ __host__ auto auto_vectorize()
 {
-    return [](auto... xs) { return [=](auto f) { auto_vectorize_impl(f, xs...); }; };
+    return make_transform([](auto f, auto... xs) { auto_vectorize_impl(f, xs...); });
+}
+
+template <index_int N, index_int Axis, class T>
+__device__ __host__ auto vectorize_tensor(T x)
+{
+    constexpr auto shape = get_shape_c<T>{};
+    if constexpr(shape.strides[Axis] == 0)
+        return tensor_step<N>(x, _c<Axis>);
+    else
+        return as_vec<N>(x, _c<Axis>);
+}
+
+template <index_int N, index_int Axis>
+__device__ __host__ auto vectorize()
+{
+    return make_transform([](auto f, auto... xs) {
+        if constexpr(N < 2)
+        {
+            f(xs...);
+        }
+        else
+        {
+            f(vectorize_tensor<N, Axis>(xs)...);
+        }
+    });
 }
 
 } // namespace migraphx

src/targets/gpu/prefuse_ops.cpp

+#include <migraphx/gpu/prefuse_ops.hpp>
+#include <migraphx/match/layernorm.hpp>
+#include <migraphx/make_op.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+namespace {
+struct find_layernorm
+{
+    auto matcher() const { return match::layernorm(); }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins   = r.result;
+        auto x_ins = r.instructions["x"];
+
+        if(not x_ins->get_shape().standard())
+            x_ins = m.insert_instruction(ins, make_op("contiguous"), x_ins);
+
+        auto relements = x_ins->get_shape().lens().back();
+
+        if(relements > 1024 or (relements % 4 != 0 and relements > 256))
+            return;
+
+        auto a = m.insert_instruction(
+            ins, make_op("hip::allocate", {{"shape", to_value(x_ins->get_shape())}}));
+        m.replace_instruction(ins, make_op("gpu::layernorm"), x_ins, a);
+    }
+};
+
+struct find_triaddlayernorm
+{
+    auto matcher() const
+    {
+        auto add1 =
+            match::name("add")(match::none_of(match::is_constant()),
+                               match::args(match::any().bind("z1"), match::any().bind("z2")));
+        auto add2 = match::name("add")(match::either_arg(0, 1)(add1, match::any().bind("z3")));
+        return match::layernorm()(match::var("x")(add2));
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins   = r.result;
+        auto x_ins = r.instructions["z1"];
+        auto y_ins = r.instructions["z2"];
+        auto z_ins = r.instructions["z3"];
+
+        for(auto* pins : {&x_ins, &y_ins, &z_ins})
+        {
+            if(not(*pins)->get_shape().standard())
+                *pins = m.insert_instruction(ins, make_op("contiguous"), *pins);
+        }
+
+        auto relements = x_ins->get_shape().lens().back();
+
+        if(relements > 1024 or (relements % 4 != 0 and relements > 256))
+            return;
+
+        auto a = m.insert_instruction(
+            ins, make_op("hip::allocate", {{"shape", to_value(x_ins->get_shape())}}));
+        m.replace_instruction(ins, make_op("gpu::triadd_layernorm"), x_ins, y_ins, z_ins, a);
+    }
+};
+} // namespace
+
+void prefuse_ops::apply(module& m) const
+{
+    match::find_matches(m, find_triaddlayernorm{}, find_layernorm{});
+}
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx