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Commit fe493c28 authored by Alan Turner's avatar Alan Turner
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Merge remote-tracking branch 'origin/develop' into ck-gsg

parents ba0b3794 cce35871
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Showing with 1198 additions and 577 deletions
src/targets/gpu/include/migraphx/gpu/prefix_scan_sum.hpp
View file @ fe493c28
......@@ -33,7 +33,6 @@
#include <migraphx/shape.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <migraphx/type_name.hpp>
#include <utility>
#include <iostream>
......
src/targets/gpu/include/migraphx/gpu/reduce_op.hpp
View file @ fe493c28
......@@ -31,7 +31,6 @@
#include <migraphx/shape.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/config.hpp>
#include <migraphx/type_name.hpp>
#include <utility>
#include <iostream>
......
src/targets/gpu/jit/concat.cpp
View file @ fe493c28
......@@ -78,7 +78,9 @@ struct concat_compiler : compiler<concat_compiler>
options.params = "-Wno-float-equal";
options.kernel_name = v.get("kernel", "concat_kernel");
auto axis = find_fast_axis(options.inputs);
auto vec = vectorize::elements(ctx, axis, options.inputs);
vectorize vec{};
if(axis != v.at("axis").to<std::size_t>())
vec = vectorize::elements(ctx, axis, options.inputs);
options.set_launch_params(
v, compute_global_for(ctx, get_concat_elements(options.inputs) / vec.size, 256));
auto src = interpolate_string(
......
src/targets/gpu/jit/mlir.cpp
View file @ fe493c28
......@@ -32,7 +32,7 @@ namespace gpu {
struct mlir_compiler : compiler<mlir_compiler>
{
std::vector<std::string> names() const { return {"gpu::mlir_conv"}; }
std::vector<std::string> names() const { return {"gpu::mlir_op"}; }
operation compile_op(context&, const std::vector<shape>&, const value&) const { return {}; }
......
src/targets/gpu/jit/reduce.cpp
View file @ fe493c28
......@@ -60,15 +60,6 @@ __global__ void reduce_kernel(void* input_p, void* output_p)
)__migraphx__";
static std::size_t get_reduce_elements(const std::vector<shape>& inputs)
{
return inputs.front().elements() / inputs.back().elements();
}
static std::size_t get_reduce_elements(const std::vector<instruction_ref>& inputs)
{
return get_reduce_elements(to_shapes(inputs));
}
static std::vector<std::size_t> get_reduce_lens(const std::vector<std::size_t>& input_lens,
const std::vector<std::size_t>& output_lens)
{
......@@ -86,9 +77,28 @@ static std::vector<std::size_t> get_reduce_lens(const std::vector<std::size_t>&
return reduce_lens;
}
static std::string get_reduce_algo(const std::vector<shape>& inputs)
template <class T>
static shape get_reduced_shape(const shape& s, const std::vector<T>& axes)
{
auto lens = s.lens();
std::fill(lens.begin(), lens.end(), 1);
for(const auto& axis : axes)
lens[axis] = s.lens()[axis];
return shape{s.type(), lens};
}
template <class T>
static shape get_output_shape(const shape& s, const std::vector<T>& axes)
{
auto lens = s.lens();
for(const auto& axis : axes)
lens[axis] = 1;
return shape{s.type(), lens};
}
template <class ReduceLens>
static std::string get_reduce_algo(const std::vector<shape>& inputs, ReduceLens rlens)
{
auto rlens = get_reduce_lens(inputs.front().lens(), inputs.back().lens());
const auto init = std::numeric_limits<std::size_t>::max();
// The minimum stride
auto min_stride = std::inner_product(
......@@ -103,11 +113,27 @@ static std::string get_reduce_algo(const std::vector<shape>& inputs)
return "block";
}
struct reduce_compiler : compiler<reduce_compiler>
static std::string get_reduce_algo(const std::vector<shape>& inputs)
{
auto rlens = get_reduce_lens(inputs.front().lens(), inputs.back().lens());
return get_reduce_algo(inputs, rlens);
}
struct simple_reduce_compiler : compiler<simple_reduce_compiler>
{
std::vector<std::string> names() const
{
return {"reduce", "reduce_sum", "reduce_mean", "reduce_max", "reduce_min", "reduce_prod"};
return {"simple_reduce",
"reduce_sum",
"reduce_mean",
"reduce_max",
"reduce_min",
"reduce_prod"};
}
static std::size_t get_reduce_elements(const std::vector<shape>& inputs)
{
return inputs.front().elements() / inputs.back().elements();
}
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
......@@ -157,44 +183,108 @@ struct reduce_compiler : compiler<reduce_compiler>
compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
{
value v = value::object{};
if(op.name() == "reduce_sum")
{
v["reduction"] = "op::sum{}";
}
else if(op.name() == "reduce_mean")
{
auto reduce_elements = get_reduce_elements(ins->inputs());
auto reduce_type = ins->inputs().front()->get_shape().type();
v["reduction"] = "op::sum{}";
std::string mean = "op::mean<" + std::to_string(reduce_elements) + ">{}";
// Use float accumulator when reduction size is too large for half
if(reduce_type == shape::half_type and reduce_elements > 16384)
v["read"] = "compose(" + mean + ", op::convert_to<float>{})";
else if(contains({shape::float_type, shape::half_type, shape::double_type},
reduce_type))
v["read"] = mean;
else
v["write"] = mean;
}
else if(op.name() == "reduce_max")
{
v["reduction"] = "op::max{}";
v["init"] = "lowest{}";
}
else if(op.name() == "reduce_min")
reduce_op r{};
r.set(ins, op);
v["reduction"] = r.reduction;
v["read"] = r.read;
v["write"] = r.write;
v["init"] = r.init;
return replace(compile_op(ctx, to_shapes(ins->inputs()), v));
}
};
static const char* const fused_reduce_kernel = R"__migraphx__(
#include <migraphx/kernels/index.hpp>
#include <migraphx/kernels/reduce.hpp>
#include <migraphx/kernels/pointwise.hpp>
#include <migraphx/kernels/vectorize.hpp>
#include <args.hpp>
namespace migraphx {
${preamble}
extern "C" {
MIGRAPHX_GLOBAL void ${kernel}(${params})
{
transform_args(make_tensors(), rotate_last(), ${transformers})(${args})([](auto y, auto... xs) {
fused_reduce<reduce::${algo}, ${reduced}>(y, partial(${lambda})(xs...));
});
}
}
} // namespace migraphx
)__migraphx__";
struct fused_reduce_compiler : compiler<fused_reduce_compiler>
{
std::vector<std::string> names() const { return {"fused_reduce"}; }
operation compile_op(context& ctx, const std::vector<shape>& inputs, const value& v) const
{
auto axes = v.at("axes").to_vector<std::size_t>();
auto virtual_inputs = inputs;
virtual_inputs.push_back(get_reduced_shape(inputs.front(), axes));
virtual_inputs.push_back(get_output_shape(inputs.front(), axes));
virtual_inputs = reduce_dims(virtual_inputs);
auto reduce_output_shape = virtual_inputs.back();
virtual_inputs.pop_back();
auto reduction_shape = virtual_inputs.back();
virtual_inputs.pop_back();
hip_compile_options options;
options.inputs = inputs;
options.output = inputs.back();
options.virtual_inputs = virtual_inputs;
auto faxis = find_fast_axis({options.virtual_inputs.front()});
vectorize vec{};
auto nelements = reduce_output_shape.elements();
auto algo = v.get("algo", get_reduce_algo(options.virtual_inputs, reduction_shape.lens()));
if(algo == "block")
{
v["reduction"] = "op::min{}";
v["init"] = "highest{}";
// Vectorize if the axis is a reduction axis
if(reduce_output_shape.lens()[faxis] == 1)
vec = vectorize::elements(ctx, faxis, options.virtual_inputs);
auto relements = reduction_shape.elements() / vec.size;
auto block_size = compute_block_size(relements, 256);
if(relements >= block_size * 256)
algo = "block_large";
options.set_launch_params(
v, compute_global_for(ctx, nelements * block_size, 256), block_size);
}
else if(op.name() == "reduce_prod")
else if(algo == "lane")
{
v["reduction"] = "op::product{}";
v["init"] = "1";
options.set_launch_params(v, compute_global_for(ctx, nelements, 256));
}
else
{
MIGRAPHX_THROW("Unsupported reduce");
MIGRAPHX_THROW("Unknown reduce algo: " + algo);
}
options.kernel_name = v.get("kernel", "reduce_kernel");
auto src = interpolate_string(
fused_reduce_kernel,
{{"kernel", options.kernel_name},
{"params", enum_params(inputs.size(), "void * private_p")},
{"args", enum_params(inputs.size(), "private_p")},
{"algo", algo},
{"reduced", "decltype(" + generate_make_shape(reduce_output_shape) + ")"},
{"lambda", v.at("lambda").to<std::string>()},
{"transformers", make_transformer_args(vec)},
{"preamble", v.get("preamble", std::string{})}});
options.params += "-Wno-float-equal";
return compile_hip_code_object(src, options);
}
compiler_replace compile(context& ctx, instruction_ref ins, const operation& op) const
{
assert(not ins->module_inputs().empty());
auto v = op.to_value();
auto* rm = ins->module_inputs().front();
v["preamble"] = generate_reduce(*rm, "fused_reduce_op");
v["lambda"] = "MIGRAPHX_LIFT(fused_reduce_op)";
v["kernel"] = generate_name_from_ops(*rm) + "_kernel";
return replace(compile_op(ctx, to_shapes(ins->inputs()), v));
}
};
......
src/targets/gpu/kernels/include/migraphx/kernels/functional.hpp
View file @ fe493c28
......@@ -204,6 +204,14 @@ constexpr auto compose(Fs... fs)
})(fs...);
}
template <class F>
constexpr auto partial(F f)
{
return [=](auto... xs) {
return [=](auto&&... ys) { return f(xs..., static_cast<decltype(ys)>(ys)...); };
};
}
template <class... Ts>
constexpr auto pack(Ts... xs)
{
......
src/targets/gpu/kernels/include/migraphx/kernels/index.hpp
View file @ fe493c28
......@@ -241,6 +241,12 @@ struct index
}
};
#ifdef MIGRAPHX_NLOCAL
#define MIGRAPHX_GLOBAL \
__global__ __attribute__((amdgpu_flat_work_group_size(MIGRAPHX_NLOCAL, MIGRAPHX_NLOCAL)))
#else
#define MIGRAPHX_GLOBAL __global__
#endif
inline __device__ __attribute__((const)) index make_index()
{
return index{blockIdx.x * blockDim.x + threadIdx.x, threadIdx.x, blockIdx.x}; // NOLINT
......
src/targets/gpu/kernels/include/migraphx/kernels/reduce.hpp
View file @ fe493c28
......@@ -174,6 +174,25 @@ struct inner_storage_tag
template <class T>
using is_inner_storage = is_base_of<inner_storage_tag, remove_cv_t<remove_reference_t<T>>>;
template <class Size, class F>
struct lazy_inner_storage : inner_storage_tag
{
using type = remove_reference_t<decltype(declval<F>()(0, _c<0>))>;
F f;
constexpr Size rsize() const { return {}; }
template <class U, class V>
constexpr auto operator()(U j, V d) const
{
return f(j, d);
}
};
template <class Size, class F>
constexpr lazy_inner_storage<Size, F> make_lazy_inner_storage(Size, F f)
{
return {{}, f};
}
template <class R, class F>
struct storage_access : F
{
......@@ -278,6 +297,14 @@ struct reducer_base
});
}
template <class F>
__device__ auto lazy_inner(F f) const
{
return this->inner_sliced([=](auto n, auto&&... xs) {
return make_lazy_inner_storage(n, [=](auto j, auto d) { return f(xs(j, d)...); });
});
}
template <class Op, class T, class Read>
__device__ auto reduce(Op op, T init, Read read) const
{
......@@ -396,25 +423,6 @@ struct block_large
index idx;
Slicer slice;
template <class Size, class F>
struct inner_storage : inner_storage_tag
{
using type = remove_reference_t<decltype(declval<F>()(0, _c<0>))>;
F f;
constexpr Size rsize() const { return {}; }
template <class U, class V>
constexpr auto operator()(U j, V d) const
{
return f(j, d);
}
};
template <class Size, class F>
static constexpr inner_storage<Size, F> make_inner_storage(Size, F f)
{
return {{}, {f}};
}
template <class Op, class T, class Read, class N, class... Ts>
__device__ auto reduce_impl(Op op, T init, Read read, N n, Ts&&... xs) const
{
......@@ -439,7 +447,7 @@ struct block_large
template <class R, class F, class N, class... Ts>
__device__ auto inner_impl(F f, N n, Ts&&... xs) const
{
return make_inner_storage(n, [=](auto j, auto d) { return f(xs(j, d)...); });
return make_lazy_inner_storage(n, [=](auto j, auto d) { return f(xs(j, d)...); });
}
};
......@@ -469,25 +477,6 @@ struct lane
index idx;
Slicer slice;
template <class Size, class F>
struct inner_storage : inner_storage_tag
{
using type = remove_reference_t<decltype(declval<F>()(0, _c<0>))>;
F f;
constexpr Size rsize() const { return {}; }
template <class U, class V>
constexpr auto operator()(U j, V d) const
{
return f(j, d);
}
};
template <class Size, class F>
static constexpr inner_storage<Size, F> make_inner_storage(Size, F f)
{
return {{}, {f}};
}
template <class Op, class T, class Read, class N, class U, class... Us>
__device__ auto reduce_impl(Op op, T init, Read read, N n, U&& x, Us&&... xs) const
{
......@@ -518,7 +507,7 @@ struct lane
template <class R, class F, class N, class... Ts>
__device__ auto inner_impl(F f, N n, Ts&&... xs) const
{
return make_inner_storage(n, [=](auto j, auto d) { return f(xs(j, d)...); });
return make_lazy_inner_storage(n, [=](auto j, auto d) { return f(xs(j, d)...); });
}
};
template <class Slicer>
......@@ -577,5 +566,21 @@ simple_reduce(Op op, T init, Input input, Output output, ReadInput read, WriteOu
});
}
template <class Algo, class Reduced, class Output, class F>
__device__ void fused_reduce(Output output, F f)
{
Algo::template run<Reduced>([&](auto out_idx, auto r) {
auto result = f(r);
if constexpr(reduce::is_inner_storage<decltype(result)>{})
{
r.inner([&](auto& y, auto x) { y = x; })(output, result);
}
else
{
r.outer([&] { output[out_idx] = implicit_conversion(result); });
}
});
}
} // namespace migraphx
#endif // MIGRAPHX_GUARD_KERNELS_REDUCE_HPP
src/targets/gpu/mlir.cpp
View file @ fe493c28
......@@ -30,6 +30,7 @@
#include <mlir-c/BuiltinTypes.h>
#include <mlir-c/Diagnostics.h>
#include <mlir-c/Dialect/MIGraphX.h>
#include <mlir-c/Dialect/Rock.h>
#include <mlir-c/IntegerSet.h>
#include <mlir-c/Pass.h>
#include <mutex>
......@@ -55,12 +56,16 @@
#include <migraphx/permutation.hpp>
#include <deque>
#include <variant>
#include <fstream>
#include <sstream>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_MLIR);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_DB);
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_MLIR_TUNING_CFG);
#ifdef MIGRAPHX_MLIR
template <class T, class F, F f> // NOLINT
......@@ -124,6 +129,8 @@ using mlir_op_printing_flags = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirOpPrintingFlags,
using mlir_region = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirRegion, mlirRegionDestroy);
using mlir_block = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirBlock, mlirBlockDestroy);
using mlir_pass_manager = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirPassManager, mlirPassManagerDestroy);
using mlir_tuning_table = MIGRAPHX_MANAGE_MLIR_HANDLE(MlirRockTuningTable,
mlirRockTuningTableDestroy);
std::string_view to_string_view(MlirStringRef s) { return {s.data, s.length}; }
......@@ -455,7 +462,7 @@ struct mlir_program
auto ops = create_operation_state("func.func");
ops.add_attributes({{"function_type", make_function_type(inputs, outputs)},
{"sym_name", std::string("main")},
{"sym_name", sym_name},
{"kernel", std::string("mixr")},
{"arch", target_arch}});
ops.add_region(std::move(region));
......@@ -498,11 +505,25 @@ struct mlir_program
return ins->get_shape();
}
static std::string get_symbol_name(const module& m)
{
for(auto ins : iterator_for(m))
{
if(ins->name() == "convolution" or ins->name() == "dot")
{
return "mlir_" + ins->name();
}
}
return "main";
}
void parse(const module& m)
{
sym_name = get_symbol_name(m);
auto mbody = mlirModuleGetBody(mmodule.get());
std::unordered_map<instruction_ref, MlirValue> ins_map;
auto fbody = insert(mbody, m, ins_map);
for(auto ins : iterator_for(m))
{
if(ins->name() == "@param")
......@@ -512,16 +533,13 @@ struct mlir_program
ops.add_attribute_value(get_operator_value(ins->get_operator()));
if(ins->name() != "@return")
ops.add_results({get_shape(ins)});
if(ins->name() == "convolution")
if(ins->name() == "convolution" or ins->name() == "dot")
{
pp =
problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()};
// check if HW supports xdlops
auto target_chip = trim(split_string(target_arch, ':').front());
bool xdlops = contains(get_xdlops_archs(), target_chip);
std::string tuned = get_tune_params(xdlops);
if(not tuned.empty())
ops.add_attributes({{"perf_config", tuned}});
auto target_chip = trim(split_string(target_arch, ':').front());
bool xdlops = contains(get_xdlops_archs(), target_chip);
if(xdlops)
ops.add_attributes({{"xdlopsV2", true}});
}
......@@ -542,15 +560,19 @@ struct mlir_program
code_object_op compile() MIGRAPHX_TIDY_CONST
{
mlir_pass_manager pm{mlirPassManagerCreate(ctx.get())};
mlir_pass_manager pm_front{mlirPassManagerCreate(ctx.get())};
mlir_pass_manager pm_back{mlirPassManagerCreate(ctx.get())};
// 1st pipeline to call
mlirMIGraphXAddHighLevelPipeline(pm.get());
mlirMIGraphXAddHighLevelPipeline(pm_front.get());
mlirPassManagerRun(pm_front.get(), mmodule.get());
// 2nd pipeline to call
mlirMIGraphXAddBackendPipeline(pm.get(), target_arch.c_str());
mlirPassManagerRun(pm.get(), mmodule.get());
get_module_tuned();
mlirMIGraphXAddBackendPipeline(pm_back.get(), target_arch.c_str());
mlirPassManagerRun(pm_back.get(), mmodule.get());
code_object_op op{};
op.symbol_name = "main";
op.symbol_name = sym_name;
op.code_object = get_binary();
std::tie(op.global, op.local) = get_launch_params();
return op;
......@@ -578,7 +600,74 @@ struct mlir_program
MIGRAPHX_THROW("Failed to compile mlir program");
}
std::string get_tune_params(bool xdlops) { return get_mlir_perf_for_conv(pp, xdlops); }
std::string get_tune_params(bool xdlops) const { return get_mlir_perf_for_conv(pp, xdlops); }
// This function appends to tuning cfg file that could be
// used with rocMLIR tuning scripts.
void dump_tuning_cfg(const char* prob_config) const
{
std::string tuning_cfg_path = string_value_of(MIGRAPHX_MLIR_TUNING_CFG{});
if(!tuning_cfg_path.empty())
{
std::vector<std::string> tokens = split_string(prob_config, '\t');
std::string prob = tokens[1];
if(starts_with(prob, "conv"))
{
tuning_cfg_path += ".conv";
}
else
{
tuning_cfg_path += ".gemm";
}
std::ofstream tuning_cfg(tuning_cfg_path, std::ios::app);
tuning_cfg << prob << std::endl;
}
}
static mlir_tuning_table create_tuning_table()
{
mlir_tuning_table tuning_table{mlirRockTuningTableCreate()};
std::string tuning_db_path = string_value_of(MIGRAPHX_MLIR_TUNING_DB{});
if(!tuning_db_path.empty())
{
std::ifstream tuning_db_tsv(tuning_db_path);
if(tuning_db_tsv)
{
std::string line;
while(std::getline(tuning_db_tsv, line))
{
std::vector<std::string> tokens = split_string(line, '\t');
std::string arch = tokens[0];
std::string prob = tokens[1];
std::string perf = tokens[2];
std::string key = arch.append("\t").append(prob);
mlirRockTuningUpdateTable(tuning_table.get(), key.c_str(), perf.c_str(), 1.0);
}
}
}
else
{
std::cerr
<< "WARNING: MLIR tuning db not found. Please set MIGRAPHX_MLIR_TUNING_DB for "
"optimal performance."
<< std::endl;
}
return tuning_table;
}
bool get_module_tuned() const
{
static mlir_tuning_table tuning_table = create_tuning_table();
if(!mlirRockTuningSetFromTable(tuning_table.get(), mmodule.get()))
{
const char* prob_config = mlirRockTuningGetKey(tuning_table.get(), mmodule.get());
std::stringstream key(prob_config);
std::cerr << "fails to set param on" << prob_config << std::endl;
dump_tuning_cfg(prob_config);
return false;
}
return true;
}
mlir_context ctx;
MlirLocation location;
......@@ -586,6 +675,7 @@ struct mlir_program
problem_params pp;
std::deque<std::string> strings{};
std::string target_arch;
std::string sym_name;
};
std::string dump_mlir(const module& m)
......
src/targets/gpu/target.cpp
View file @ fe493c28
......@@ -26,13 +26,13 @@
#include <migraphx/check_context.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/eliminate_allocation.hpp>
#include <migraphx/eliminate_common_subexpression.hpp>
#include <migraphx/eliminate_concat.hpp>
#include <migraphx/eliminate_contiguous.hpp>
#include <migraphx/eliminate_data_type.hpp>
#include <migraphx/eliminate_identity.hpp>
#include <migraphx/eliminate_pad.hpp>
#include <migraphx/fuse_pointwise.hpp>
#include <migraphx/fuse_reduce.hpp>
#include <migraphx/inline_module.hpp>
#include <migraphx/insert_pad.hpp>
#include <migraphx/layout_nhwc.hpp>
......@@ -40,7 +40,7 @@
#include <migraphx/normalize_ops.hpp>
#include <migraphx/optimize_module.hpp>
#include <migraphx/preallocate_param.hpp>
#include <migraphx/propagate_constant.hpp>
#include <migraphx/promote_literals.hpp>
#include <migraphx/register_target.hpp>
#include <migraphx/replace_allocate.hpp>
#include <migraphx/rewrite_gelu.hpp>
......@@ -48,9 +48,9 @@
#include <migraphx/rewrite_quantization.hpp>
#include <migraphx/rewrite_rnn.hpp>
#include <migraphx/schedule.hpp>
#include <migraphx/simplify_algebra.hpp>
#include <migraphx/simplify_qdq.hpp>
#include <migraphx/simplify_reshapes.hpp>
#include <migraphx/split_single_dyn_dim.hpp>
#include <migraphx/gpu/allocation_model.hpp>
#include <migraphx/gpu/compile_miopen.hpp>
#include <migraphx/gpu/compile_ops.hpp>
......@@ -75,6 +75,7 @@ namespace gpu {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_DISABLE_SCHEDULE_PASS)
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_DISABLE_POINTWISE_FUSION)
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_DISABLE_REDUCE_FUSION)
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_ENABLE_NHWC)
struct id_pass
{
......@@ -103,6 +104,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
// clang-format off
return
{
enable_pass(options.split_single_dyn_dim, split_single_dyn_dim{}),
enable_pass(options.split_single_dyn_dim, dead_code_elimination{}),
normalize_ops{},
dead_code_elimination{},
simplify_qdq{},
......@@ -132,6 +135,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
optimize_module{},
enable_pass(not enabled(MIGRAPHX_DISABLE_POINTWISE_FUSION{}), fuse_pointwise{}),
dead_code_elimination{},
enable_pass(not enabled(MIGRAPHX_DISABLE_REDUCE_FUSION{}), fuse_reduce{}),
dead_code_elimination{},
fuse_mlir{&ctx},
dead_code_elimination{},
fuse_ck{&ctx},
......@@ -153,6 +158,8 @@ std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_opti
dead_code_elimination{},
compile_ops{&ctx},
dead_code_elimination{},
promote_literals{},
dead_code_elimination{},
write_literals{&ctx},
schedule{gpu::schedule_model{ctx.get_current_device().nstreams()}, not enabled(MIGRAPHX_DISABLE_SCHEDULE_PASS{})},
memory_coloring{"hip::allocate"},
......
src/targets/ref/CMakeLists.txt
View file @ fe493c28
......@@ -31,10 +31,9 @@ set_target_properties(migraphx_ref PROPERTIES EXPORT_NAME ref)
rocm_set_soversion(migraphx_ref ${MIGRAPHX_SO_VERSION})
find_path(BLAZE_INCLUDE blaze/Blaze.h)
find_package(Threads)
rocm_clang_tidy_check(migraphx_ref)
target_link_libraries(migraphx_ref migraphx Threads::Threads)
target_link_libraries(migraphx_ref PUBLIC migraphx)
target_include_directories(migraphx_ref PRIVATE ${BLAZE_INCLUDE})
target_compile_definitions(migraphx_ref PRIVATE -DBLAZE_USE_CPP_THREADS)
......
test/CMakeLists.txt
View file @ fe493c28
......@@ -110,7 +110,7 @@ function(add_test_executable TEST_NAME)
add_test_command(${TEST_NAME} ${TEST_COMMAND})
add_dependencies(tests ${TEST_NAME})
add_dependencies(check ${TEST_NAME})
target_link_libraries(${TEST_NAME} migraphx migraphx_onnx)
target_link_libraries(${TEST_NAME} migraphx migraphx_onnx migraphx_ref)
target_include_directories(${TEST_NAME} PUBLIC include)
endfunction(add_test_executable)
......@@ -163,7 +163,7 @@ foreach(ONNX_TEST ${ONNX_TESTS})
set(TEST_NAME test_${BASE_NAME})
add_executable(${TEST_NAME} ${ONNX_TEST})
rocm_clang_tidy_check(${TEST_NAME})
target_link_libraries(${TEST_NAME} migraphx_onnx)
target_link_libraries(${TEST_NAME} migraphx_onnx migraphx_ref)
target_include_directories(${TEST_NAME} PUBLIC include)
add_test(NAME ${TEST_NAME} COMMAND $<TARGET_FILE:${TEST_NAME}> WORKING_DIRECTORY ${TEST_ONNX_DIR})
add_dependencies(tests ${TEST_NAME})
......@@ -218,3 +218,10 @@ test_headers(migraphx/ref ${CMAKE_SOURCE_DIR}/src/targets/ref/include/migraphx/r
if(MIGRAPHX_ENABLE_GPU)
test_headers(migraphx/gpu ${CMAKE_SOURCE_DIR}/src/targets/gpu/include/migraphx/gpu/*.hpp)
endif()
if(MIGRAPHX_ENABLE_CPU)
test_headers(migraphx/cpu ${CMAKE_SOURCE_DIR}/src/targets/cpu/include/migraphx/cpu/*.hpp)
endif()
if(MIGRAPHX_ENABLE_FPGA)
test_headers(migraphx/fpga ${CMAKE_SOURCE_DIR}/src/targets/fpga/include/migraphx/fpga/*.hpp)
endif()
test/api/CMakeLists.txt
View file @ fe493c28
......@@ -25,7 +25,7 @@ function(add_api_test TEST_NAME TEST_SRC TEST_DIR)
set(NAME test_api_${TEST_NAME})
add_executable(${NAME} EXCLUDE_FROM_ALL ${TEST_SRC})
rocm_clang_tidy_check(${NAME})
target_link_libraries(${NAME} migraphx_c migraphx)
target_link_libraries(${NAME} migraphx_c migraphx migraphx_all_targets)
target_include_directories(${NAME} PUBLIC ../include)
add_test(NAME ${NAME} COMMAND $<TARGET_FILE:${NAME}> WORKING_DIRECTORY ${TEST_DIR})
add_dependencies(tests ${NAME})
......@@ -59,7 +59,7 @@ if(MIGRAPHX_ENABLE_GPU)
list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
find_package(hip)
add_api_test(gpu test_gpu.cpp ${TEST_ONNX_DIR})
target_link_libraries(test_api_gpu hip::host)
target_link_libraries(test_api_gpu)
add_api_test(custom_op_gpu test_custom_op_gpu.cpp ${TEST_ONNX_DIR})
target_link_libraries(test_api_custom_op_gpu hip::host)
target_link_libraries(test_api_custom_op_gpu)
endif()
test/check_shapes_test.cpp
View file @ fe493c28
......@@ -36,7 +36,7 @@ bool create_shapes(bool dynamic_allowed)
try
{
shape a{shape::int64_type, {3}};
shape b{shape::float_type, {{3, 6, 0}, {4, 4, 0}}};
shape b{shape::float_type, {{3, 6}, {4, 4}}};
auto op = migraphx::make_op("add");
migraphx::check_shapes{{a, b}, op, dynamic_allowed}.has(2);
return true;
......
test/fuse_reduce.cpp 0 → 100644
View file @ fe493c28
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/fuse_reduce.hpp>
#include <migraphx/dead_code_elimination.hpp>
#include <migraphx/instruction.hpp>
#include <migraphx/pass_manager.hpp>
#include <migraphx/program.hpp>
#include <basic_ops.hpp>
#include <migraphx/make_op.hpp>
#include <test.hpp>
#include <pointwise.hpp>
void run_pass(migraphx::program& p)
{
migraphx::run_passes(p, {migraphx::fuse_reduce{}, migraphx::dead_code_elimination{}});
}
bool all_instructions_are_local(const migraphx::module& m)
{
return std::all_of(m.begin(), m.end(), [&](const auto& ins) {
return std::all_of(ins.inputs().begin(), ins.inputs().end(), [&](auto input) {
return m.has_instruction(input);
});
});
}
template <class F>
migraphx::instruction_ref add_reduce(migraphx::program& p,
const std::string& name,
std::vector<migraphx::instruction_ref> inputs,
const std::vector<int64_t>& axes,
F f)
{
auto* rm = p.create_module(name);
auto* mm = p.get_main_module();
rm->set_bypass();
std::vector<migraphx::instruction_ref> params;
std::transform(inputs.begin(), inputs.end(), std::back_inserter(params), [&](auto input) {
return rm->add_parameter(
"x" + std::to_string(params.size()),
migraphx::shape{input->get_shape().type(), input->get_shape().lens()});
});
auto r = f(rm, params, axes);
rm->add_return({r});
EXPECT(all_instructions_are_local(*rm));
return mm->add_instruction(migraphx::make_op("fused_reduce", {{"axes", axes}}), inputs, {rm});
}
inline auto single_reduce(const std::string& name)
{
return [=](auto* rm, const auto& inputs, const auto& axes) {
return rm->add_instruction(migraphx::make_op(name, {{"axes", axes}}), inputs);
};
}
TEST_CASE(single)
{
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto rsum1 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), x);
auto rsum2 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), y);
mm->add_return({rsum1, rsum2});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto rsum1 = add_reduce(p2, "main:reduce_sum0", {x}, {1}, single_reduce("reduce_sum"));
auto rsum2 = add_reduce(p2, "main:reduce_sum1", {y}, {1}, single_reduce("reduce_sum"));
mm->add_return({rsum1, rsum2});
}
EXPECT(p1 == p2);
}
TEST_CASE(pointwise_reduce)
{
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto add = add_pointwise(p1, "main:pointwise0", {x, y}, single_pointwise("add"));
auto rsum = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), add);
mm->add_return({rsum});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto rsum = add_reduce(
p2,
"main:pointwise0:main:reduce_sum0",
{x, y},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto add =
add_pointwise(p2, rm, "main:pointwise0", inputs, single_pointwise("add"));
return rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}), add);
});
mm->add_return({rsum});
}
EXPECT(p1 == p2);
}
TEST_CASE(reduce_pointwise)
{
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto rsum = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), x);
auto rsumb = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum);
auto add = add_pointwise(p1, "main:pointwise0", {rsumb, y}, single_pointwise("add"));
mm->add_return({add});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto y = mm->add_parameter("y", s);
auto add = add_reduce(
p2,
"main:reduce_sum0:main:pointwise0",
{x, y},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto rsum = rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}),
inputs[0]);
auto rsumb = rm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum);
return add_pointwise(
p2, rm, "main:pointwise0", {rsumb, inputs[1]}, single_pointwise("add"));
});
mm->add_return({add});
}
EXPECT(p1 == p2);
}
TEST_CASE(reduce_reduce)
{
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), x);
auto rsumb = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum);
auto rsumdiff = add_pointwise(p1, "main:pointwise0", {rsumb, x}, single_pointwise("sub"));
auto rsum2 =
mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), rsumdiff);
auto sqrt = add_pointwise(p1, "main:pointwise1", {rsum2}, single_pointwise("sqrt"));
mm->add_return({sqrt});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto sqrt = add_reduce(
p2,
"main:reduce_sum1:main:reduce_sum0:main:pointwise0:main:pointwise1",
{x},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto rsum = rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}),
inputs[0]);
auto rsumb = rm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum);
auto rsumdiff = add_pointwise(
p2, rm, "main:pointwise0", {rsumb, inputs[0]}, single_pointwise("sub"));
auto rsum2 = rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}),
rsumdiff);
return add_pointwise(p2, rm, "main:pointwise1", {rsum2}, single_pointwise("sqrt"));
});
mm->add_return({sqrt});
}
EXPECT(p1 == p2);
}
TEST_CASE(reduce_reduce_mismatch_axis)
{
migraphx::shape s{migraphx::shape::float_type, {4, 2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum1 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), x);
auto rsum2 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {2}}}), rsum1);
mm->add_return({rsum2});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum1 = add_reduce(p2, "main:reduce_sum0", {x}, {1}, single_reduce("reduce_sum"));
auto rsum2 = add_reduce(p2, "main:reduce_sum1", {rsum1}, {2}, single_reduce("reduce_sum"));
mm->add_return({rsum2});
}
EXPECT(p1 == p2);
}
TEST_CASE(pointwise_reduce_broadcast)
{
migraphx::shape s{migraphx::shape::float_type, {2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum1 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), x);
auto sqrt = add_pointwise(p1, "main:pointwise0", {rsum1}, single_pointwise("sqrt"));
auto sqrtb = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), sqrt);
auto add1 = add_pointwise(p1, "main:pointwise1", {sqrtb, x}, single_pointwise("add"));
auto rsum2 = mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {1}}}), add1);
auto add2 = add_pointwise(p1, "main:pointwise2", {rsum2, rsum1}, single_pointwise("add"));
mm->add_return({add2});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto add2 = add_reduce(
p2,
"main:pointwise0:main:pointwise1:main:reduce_sum1:main:pointwise2:main:reduce_sum0",
{x},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto rsum1 = rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}),
inputs[0]);
auto sqrt =
add_pointwise(p2, rm, "main:pointwise0", {rsum1}, single_pointwise("sqrt"));
auto sqrtb = rm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), sqrt);
auto add1 = add_pointwise(
p2, rm, "main:pointwise1", {sqrtb, inputs[0]}, single_pointwise("add"));
auto rsum2 =
rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}), add1);
return add_pointwise(
p2, rm, "main:pointwise2", {rsum2, rsum1}, single_pointwise("add"));
});
mm->add_return({add2});
}
EXPECT(p1 == p2);
}
TEST_CASE(reduce_reduce_broadcast)
{
migraphx::shape s{migraphx::shape::float_type, {4, 2, 3}};
migraphx::program p1;
{
auto* mm = p1.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum1 = add_reduce(p1, "test:reduce_sum0", {x}, {1}, single_reduce("reduce_sum"));
auto rsumb = mm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum1);
auto add = add_reduce(
p1,
"test:reduce_sum1",
{rsumb, x},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto add2 =
add_pointwise(p1, rm, "test:pointwise0", inputs, single_pointwise("add"));
return rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}), add2);
});
mm->add_return({add});
}
run_pass(p1);
migraphx::program p2;
{
auto* mm = p2.get_main_module();
auto x = mm->add_parameter("x", s);
auto rsum = add_reduce(
p2,
"test:reduce_sum1:test:reduce_sum0",
{x},
{1},
[&](auto* rm, const auto& inputs, const auto& axes) {
auto rsum1 = rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}),
inputs[0]);
auto rsumb = rm->add_instruction(
migraphx::make_op("multibroadcast", {{"out_lens", s.lens()}}), rsum1);
auto add = add_pointwise(
p2, rm, "test:pointwise0", {rsumb, inputs[0]}, single_pointwise("add"));
return rm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", axes}}), add);
});
mm->add_return({rsum});
}
EXPECT(p1 == p2);
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/gpu/hip.cpp
View file @ fe493c28
......@@ -27,7 +27,7 @@
#include <migraphx/gpu/hip.hpp>
#include <migraphx/gpu/target.hpp>
TEST_CASE(tuple_to_from_gpu)
TEST_CASE(tuple_from_gpu)
{
migraphx::shape s1{migraphx::shape::float_type, {2, 3}};
migraphx::shape s2{migraphx::shape::int32_type, {2, 4}};
......@@ -47,4 +47,23 @@ TEST_CASE(tuple_to_from_gpu)
EXPECT(result2 == p2_data);
}
TEST_CASE(tuple_to_gpu)
{
migraphx::shape s1{migraphx::shape::float_type, {2, 3}};
migraphx::shape s2{migraphx::shape::int32_type, {2, 4}};
std::vector<float> p1_data = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6};
std::vector<int> p2_data = {1, 2, 3, 4, 5, 6, 7, 8};
auto p1 = migraphx::argument{s1, p1_data.data()};
auto p2 = migraphx::argument{s2, p2_data.data()};
auto p_gpu = migraphx::gpu::to_gpu(migraphx::argument({p1, p2}));
auto p_host = migraphx::gpu::from_gpu(p_gpu);
std::vector<migraphx::argument> results = p_host.get_sub_objects();
std::vector<float> result1;
results[0].visit([&](auto output) { result1.assign(output.begin(), output.end()); });
std::vector<int> result2;
results[1].visit([&](auto output) { result2.assign(output.begin(), output.end()); });
EXPECT(result1 == p1_data);
EXPECT(result2 == p2_data);
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/gpu/mlir.cpp
View file @ fe493c28
......@@ -140,7 +140,7 @@ TEST_CASE(conv)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @main(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_convolution(%arg0: tensor<2x8x3x3xf32>, %arg1: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
%0 = migraphx.convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
return %0 : tensor<1x2x2x2xf32>
}
......@@ -163,7 +163,7 @@ TEST_CASE(conv_add_relu)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @main(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_convolution(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
%0 = migraphx.convolution(%arg2, %arg1) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xf32>, tensor<2x8x3x3xf32>) -> tensor<1x2x2x2xf32>
%1 = migraphx.add(%0, %arg0) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
%2 = migraphx.relu(%1) : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
......@@ -187,4 +187,30 @@ module {
EXPECT(verify_mlir(m));
}
TEST_CASE(dot_add)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
%0 = migraphx.dot(%arg0, %arg1) : tensor<1x5x4xf32>, tensor<1x4x3xf32> -> tensor<1x5x3xf32>
%1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
return %1 : tensor<1x5x3xf32>
}
}
)__migraphx__";
migraphx::module m;
auto arg0 = m.add_parameter("arg0", {migraphx::shape::float_type, {1, 5, 4}});
auto arg1 = m.add_parameter("arg1", {migraphx::shape::float_type, {1, 4, 3}});
auto arg2 = m.add_parameter("arg2", {migraphx::shape::float_type, {1, 5, 3}});
auto conv = m.add_instruction(migraphx::make_op("dot"), arg0, arg1);
auto add = m.add_instruction(migraphx::make_op("add"), conv, arg2);
m.add_return({add});
auto s = migraphx::gpu::dump_mlir(m);
// Skip test if MLIR is not enabled
if(s.empty())
return;
CHECK(encode(s) == encode(mlir_output));
EXPECT(verify_mlir(m));
}
int main(int argc, const char* argv[]) { test::run(argc, argv); }
test/include/pointwise.hpp
View file @ fe493c28
......@@ -30,12 +30,12 @@
template <class F>
migraphx::instruction_ref add_pointwise(migraphx::program& p,
migraphx::module_ref mm,
const std::string& name,
std::vector<migraphx::instruction_ref> inputs,
F f)
{
auto* pm = p.create_module(name);
auto* mm = p.get_main_module();
pm->set_bypass();
std::vector<migraphx::instruction_ref> params;
std::transform(inputs.begin(), inputs.end(), std::back_inserter(params), [&](auto input) {
......@@ -47,6 +47,15 @@ migraphx::instruction_ref add_pointwise(migraphx::program& p,
return mm->add_instruction(migraphx::make_op("pointwise"), inputs, {pm});
}
template <class F>
migraphx::instruction_ref add_pointwise(migraphx::program& p,
const std::string& name,
std::vector<migraphx::instruction_ref> inputs,
F f)
{
return add_pointwise(p, p.get_main_module(), name, inputs, f);
}
inline auto single_pointwise(const std::string& name)
{
return [=](auto* pm, const auto& inputs) {
......
test/onnx/onnx_test.cpp
View file @ fe493c28
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test/op_shape_test.cpp
View file @ fe493c28
This diff is collapsed.
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