Merge remote-tracking branch 'origin/jit-layernorm' into debug-bert

src/targets/gpu/compile_gen.cpp

@@ -25,6 +25,13 @@
 #include <migraphx/shape.hpp>
 #include <migraphx/permutation.hpp>
 #include <migraphx/stringutils.hpp>
+#include <migraphx/module.hpp>
+#include <migraphx/dead_code_elimination.hpp>
+#include <migraphx/eliminate_common_subexpression.hpp>
+#include <migraphx/cpp_generator.hpp>
+#include <migraphx/pass_manager.hpp>
+#include <migraphx/instruction.hpp>
+#include <migraphx/ranges.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -75,25 +82,26 @@ std::string vectorize::str() const
 preload preload::broadcasts(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);
+    std::vector<bool> result(inputs.size());
+    std::vector<std::size_t> preloaded;
+    for(auto i : range(inputs.size()))
+    {
+        if(inputs[i].strides()[axis] == 0)
+            preloaded.push_back(i);
+    }
+    std::sort(preloaded.begin(), preloaded.end(), by(std::less<>{}, [&](auto i) {
+                  return inputs[i].bytes();
+              }));
+
+    std::size_t bytes = 0;
+    for(auto i : preloaded)
+    {
+        auto input = inputs[i];
+        bytes += input.bytes();
+        if(bytes > max_lds_bytes)
+            break;
+        result[i] = true;
+    }
     return {result};
 }
 
@@ -125,6 +133,45 @@ std::string make_transformer_args(std::vector<std::string> transformers)
     return join_strings(std::move(transformers), ", ");
 }
 
+std::string generate_pointwise(const module& pm, const std::string& name)
+{
+    module m = pm;
+    run_passes(m, {eliminate_common_subexpression{}, dead_code_elimination{}});
+    cpp_generator g;
+    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()) + ">"; });
+    g.create_function(
+        g.generate_module(m).set_attributes({"__device__"}).set_generic_types(m).set_name(name));
+    return g.str();
+}
+
+static std::vector<std::string> get_op_names(const module& m)
+{
+    std::vector<std::string> result;
+    for(auto& ins : m)
+    {
+        if(starts_with(ins.name(), "@"))
+            continue;
+        result.push_back(ins.name());
+    }
+    return result;
+}
+
+std::string generate_name_from_ops(const module& m)
+{
+    auto op_names = get_op_names(m);
+    return join_strings(op_names, "_");
+}
+
 } // namespace gen
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

src/targets/gpu/fuse_ops.cpp

@@ -827,13 +827,14 @@ void apply_conv_bias(context& ctx, module& m, const match::matcher_result& r)
     m.replace_instruction(ins, cb, input_ins, weights_ins, old_ws_ins, bias_ins, alloc_ins);
 }
 
-inline auto precompile_name(std::string s) // NOLINT
+template <class... Strings>
+inline auto precompile_name(Strings... names) // 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);
+        return (contains({names...}, op.name()));
     });
 }
 
@@ -1041,6 +1042,31 @@ struct find_contiguous_pointwise
     }
 };
 
+struct find_layernorm_pointwise
+{
+    auto matcher() const
+    {
+        return precompile_name("pointwise")(match::arg(0)(
+            precompile_name("gpu::prelayernorm", "gpu::preadd_layernorm").bind("layernorm")));
+    }
+
+    void apply(module& m, const match::matcher_result& r) const
+    {
+        auto ins       = r.result;
+        auto layernorm = r.instructions["layernorm"];
+        auto* pm       = ins->module_inputs().front();
+
+        if(not layernorm->module_inputs().empty())
+            return;
+
+        auto inputs = layernorm->inputs();
+        inputs.pop_back();
+        inputs.insert(inputs.end(), ins->inputs().begin() + 1, ins->inputs().end());
+
+        m.replace_instruction(ins, layernorm->get_operator(), inputs, {pm});
+    }
+};
+
 void fuse_ops::apply(module& m) const
 {
     match::find_matches(m, find_contiguous_pointwise{}, find_gelu{}, find_gelu_new{fast_math});
@@ -1063,6 +1089,7 @@ void fuse_ops::apply(module& m) const
     match::find_matches(m,
                         find_triadd_layernorm{},
                         find_gemm_add{},
+                        find_layernorm_pointwise{},
                         find_gemm_pointwise{},
                         find_commutative_broadcast{});
     match::find_matches(m, find_contiguous{});

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

@@ -25,6 +25,7 @@
 #define MIGRAPHX_GUARD_GPU_COMPILE_GEN_HPP
 
 #include <migraphx/config.hpp>
+#include <migraphx/module_ref.hpp>
 #include <string>
 #include <unordered_map>
 #include <vector>
@@ -62,6 +63,10 @@ std::string make_transformer_args(Ts... xs)
     return make_transformer_args({xs.str()...});
 }
 
+std::string generate_pointwise(const module& pm, const std::string& name);
+
+std::string generate_name_from_ops(const module& m);
+
 } // namespace gen
 } // namespace gpu
 } // namespace MIGRAPHX_INLINE_NS

src/targets/gpu/jit/layernorm.cpp

+#include <migraphx/gpu/compiler.hpp>
+#include <migraphx/gpu/context.hpp>
+#include <migraphx/gpu/compile_hip_code_object.hpp>
+#include <migraphx/gpu/compile_hip.hpp>
+#include <migraphx/gpu/compile_gen.hpp>
+
+#include <migraphx/cpp_generator.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/reduce_dims.hpp>
+#include <migraphx/stringutils.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+
+using namespace migraphx::gpu::gen; // NOLINT
+
+static const char* const layernorm_kernel = R"__migraphx__(
+#include <migraphx/kernels/index.hpp>
+#include <migraphx/kernels/layernorm.hpp>
+#include <migraphx/kernels/vectorize.hpp>
+#include <migraphx/kernels/preload.hpp>
+#include <args.hpp>
+
+namespace migraphx {
+
+${preamble}
+
+extern "C" {
+__global__ void ${kernel}(${params}) 
+{
+    auto idx = make_index();
+    transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto... xs) {
+        ${layernorm}<${axis}>(${post}, xs...);
+    });
+}
+    
+}
+
+} // namespace migraphx
+
+)__migraphx__";
+
+struct layernorm_compiler : compiler<layernorm_compiler>
+{
+    std::vector<std::string> names() const
+    {
+        return {"layernorm", "gpu::prelayernorm", "gpu::preadd_layernorm"};
+    }
+
+    operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
+    {
+        // TODO: Use reduce_dims
+        auto axis  = inputs.front().lens().size() - 1;
+        auto faxis = find_fast_axis({inputs.front()});
+        vectorize vec{};
+        // Vectorize if the axis is a reduction axis
+        if(axis == faxis)
+        {
+            vec = vectorize::elements(faxis, inputs);
+        }
+        auto preloads   = preload::broadcasts(axis, inputs);
+        auto relements  = inputs[0].lens()[axis] / vec.size;
+        auto nelements  = (inputs.back().elements() / inputs[0].lens()[axis]);
+        auto block_size = compute_block_size(relements, 256);
+        hip_compile_options options;
+        options.set_launch_params(
+            v, compute_global_for(ctx, nelements * block_size, 256), block_size);
+        options.output      = inputs.back();
+        options.inputs      = inputs;
+        options.kernel_name = v.get("kernel", "layernorm_kernel");
+
+        auto src = interpolate_string(layernorm_kernel,
+                                      {{"kernel", options.kernel_name},
+                                       {"params", enum_params(inputs.size(), "void * private_p")},
+                                       {"args", enum_params(inputs.size(), "private_p")},
+                                       {"transformers", make_transformer_args(preloads, vec)},
+                                       {"post", v.get("post", std::string{"op::id{}"})},
+                                       {"preamble", v.get("preamble", std::string{})},
+                                       {"layernorm", v.get("layernorm", std::string{"layernorm"})},
+                                       {"axis", to_string(axis)}});
+
+        return compile_hip_code_object(src, options);
+    }
+
+    compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
+    {
+        auto v         = op.to_value();
+        v["layernorm"] = "layernorm";
+        v["kernel"]    = "layernorm_kernel";
+        if(op.name() == "gpu::preadd_layernorm")
+        {
+            v["layernorm"] = "add_layernorm";
+            v["kernel"]    = "add_layernorm_kernel";
+        }
+        if(not ins->module_inputs().empty())
+        {
+            auto* pm      = ins->module_inputs().front();
+            v["preamble"] = generate_pointwise(*pm, "post_layernorm");
+            v["post"]     = "MIGRAPHX_LIFT(post_layernorm)";
+            v["kernel"] =
+                v["layernorm"].to<std::string>() + "_" + generate_name_from_ops(*pm) + "_kernel";
+        }
+        return replace(compile_op(ctx, to_shapes(ins->inputs()), v));
+    }
+};
+
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/jit/pointwise.cpp

@@ -65,18 +65,6 @@ __global__ void ${kernel}(${params})
 
 )__migraphx__";
 
-static std::vector<std::string> get_op_names(const module& m)
-{
-    std::vector<std::string> result;
-    for(auto& ins : m)
-    {
-        if(starts_with(ins.name(), "@"))
-            continue;
-        result.push_back(ins.name());
-    }
-    return result;
-}
-
 struct pointwise_compiler : compiler<pointwise_compiler>
 {
     std::vector<std::string> names() const { return {"pointwise", "contiguous"}; }
@@ -126,34 +114,14 @@ struct pointwise_compiler : compiler<pointwise_compiler>
         else
         {
             assert(not ins->module_inputs().empty());
-            auto* pm = ins->module_inputs().front();
-            run_passes(*pm, {eliminate_common_subexpression{}, dead_code_elimination{}});
-            cpp_generator g;
-            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 ${0})");
-            g.add_point_op("mod", "migraphx::mod(${0}, ${1})");
-            g.add_point_op("fmod", "migraphx::fmod(${0}, ${1})");
-            // Add explict conversions
-            g.fresult([](const shape& s) {
-                return "migraphx::convert<" + shape::cpp_type(s.type()) + ">";
-            });
-            auto name = g.create_function(
-                g.generate_module(*pm).set_attributes({"__device__"}).set_generic_types(*pm));
-            std::string lambda = "MIGRAPHX_LIFT(" + name + ")";
-            auto op_names      = get_op_names(*pm);
-            op_names.push_back("kernel");
-            auto op_name_string = join_strings(op_names, "_");
-            return replace(compile_op(
-                ctx,
-                to_shapes(ins->inputs()),
-                {{"lambda", lambda}, {"preamble", g.str()}, {"kernel", op_name_string}}));
+            auto* pm           = ins->module_inputs().front();
+            auto pf            = generate_pointwise(*pm, "inner_pointwise");
+            std::string lambda = "MIGRAPHX_LIFT(inner_pointwise)";
+            auto kernel_name   = generate_name_from_ops(*pm) + "_kernel";
+            return replace(
+                compile_op(ctx,
+                           to_shapes(ins->inputs()),
+                           {{"lambda", lambda}, {"preamble", pf}, {"kernel", kernel_name}}));
         }
     }
 };

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

@@ -31,8 +31,9 @@
     ->decltype(__VA_ARGS__) { return __VA_ARGS__; }
 
 // NOLINTNEXTLINE
-#define MIGRAPHX_LIFT(...) \
-    [](auto&&... xs) MIGRAPHX_RETURNS((__VA_ARGS__)(static_cast<decltype(xs)>(xs)...))
+#define MIGRAPHX_LIFT(...)                           \
+    [](auto&&... private_lisft_xs) MIGRAPHX_RETURNS( \
+        (__VA_ARGS__)(static_cast<decltype(private_lisft_xs)>(private_lisft_xs)...))
 
 namespace migraphx {
 

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

+#ifndef MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP
+#define MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP
+#include <migraphx/kernels/reduce.hpp>
+#include <migraphx/kernels/ops.hpp>
+#include <migraphx/kernels/print.hpp>
+
+namespace migraphx {
+
+template <index_int Axis,
+          class F,
+          class BinOp,
+          class Output,
+          class Input1,
+          class Input2,
+          class... Inputs>
+__device__ void generic_binary_layernorm(
+    F compute, BinOp op, Output output, Input1 input1, Input2 input2, Inputs... inputs)
+{
+    using reduce_output = reduce::with_axis<Input1, Axis>;
+    constexpr auto relements =
+        get_shape_c<Input1>{}.elements() / get_shape_c<reduce_output>{}.elements();
+    MIGRAPHX_ASSERT(relements > 0);
+    reduce::block::run<reduce_output>([&](auto, auto r) {
+        using value_type = typename Input1::type;
+        auto mean        = [&](auto f) {
+            return r.reduce(op::sum{}, 0, [&](auto x1, auto x2) {
+                return f(x1, x2) / value_type{relements};
+            })(input1, input2);
+        };
+        // mean(x)
+        auto mean_x = mean(op);
+        // mean(m ^ 2)
+        auto mean_m2 = mean([&](auto x1, auto x2) {
+            auto m = op(x1, x2) - mean_x;
+            return m * m;
+        });
+
+        r.inner([&](auto& y, auto x1, auto x2, auto... xs) {
+            auto m = op(x1, x2) - mean_x;
+            // m * rsqrt(mean(m ^ 2) + 1e-12)
+            y = compute(m * rsqrt(mean_m2 + value_type{1e-12}), xs...);
+        })(output, input1, input2, inputs...);
+    });
+}
+
+template <index_int Axis, class F, class Output, class Input, class... Inputs>
+__device__ void layernorm(F compute, Output output, Input input, Inputs... inputs)
+{
+    generic_binary_layernorm<Axis>(
+        compute, [](auto x, auto) { return x; }, output, input, input, inputs...);
+}
+
+template <index_int Axis, class F, class Output, class Input1, class Input2, class... Inputs>
+__device__ void
+add_layernorm(F compute, Output output, Input1 input1, Input2 input2, Inputs... inputs)
+{
+    generic_binary_layernorm<Axis>(
+        compute, [](auto x1, auto x2) { return x1 + x2; }, output, input1, input2, inputs...);
+}
+
+} // namespace migraphx
+#endif // MIGRAPHX_GUARD_KERNELS_LAYERNORM_HPP

src/targets/gpu/prefuse_ops.cpp

@@ -24,12 +24,53 @@
 #include <migraphx/gpu/prefuse_ops.hpp>
 #include <migraphx/match/layernorm.hpp>
 #include <migraphx/make_op.hpp>
+#include <migraphx/register_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
-
 namespace {
+
+template <class Derived, std::size_t N>
+struct layernorm_base
+{
+    shape compute_shape(std::vector<shape> inputs, std::vector<module_ref> mods) const
+    {
+        std::size_t nargs = 1;
+        if(not mods.empty())
+        {
+            auto* pm = mods.front();
+            nargs    = pm->get_parameter_names().size();
+        }
+        check_shapes{inputs, static_cast<const Derived&>(*this)}.has(nargs + N);
+        auto s = inputs.at(0);
+        if(s.scalar())
+        {
+            return s;
+        }
+        else if(s.broadcasted())
+        {
+            return {s.type(), s.lens()};
+        }
+        else
+        {
+            return s.with_lens(s.lens());
+        }
+    }
+};
+
+struct layernorm : layernorm_base<layernorm, 0>
+{
+    std::string name() const { return "gpu::prelayernorm"; }
+};
+MIGRAPHX_REGISTER_OP(layernorm);
+
+struct add_layernorm : layernorm_base<add_layernorm, 1>
+{
+    std::string name() const { return "gpu::preadd_layernorm"; }
+};
+MIGRAPHX_REGISTER_OP(add_layernorm);
+
 struct find_layernorm
 {
     auto matcher() const { return match::layernorm(); }
@@ -39,59 +80,30 @@ struct find_layernorm
         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);
+        m.replace_instruction(ins, layernorm{}, x_ins);
     }
 };
 
-struct find_triaddlayernorm
+struct find_add_layernorm
 {
     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));
+        return match::layernorm()(match::var("x")(match::name("add").bind("add")));
     }
 
     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 ins     = r.result;
+        auto add_ins = r.instructions["add"];
 
-        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);
+        m.replace_instruction(ins, add_layernorm{}, add_ins->inputs());
     }
 };
 } // namespace
 
 void prefuse_ops::apply(module& m) const
 {
-    match::find_matches(m, find_triaddlayernorm{}, find_layernorm{});
+    match::find_matches(m, find_add_layernorm{}, find_layernorm{});
 }
 
 } // namespace gpu