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Unverified Commit 065d06af authored by Krzysztof Drewniak's avatar Krzysztof Drewniak Committed by GitHub
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[MLIR] Changes for tuning API v2, MLIR grid layout changes. (#1961) 

This PR constitutes the MIGraphX-side changes needed to not break the build in the presence of
ROCmSoftwarePlatform/rocMLIR#1136 , and updates what data is sent in to MLIR during the kernel generation and tuning process.
parent ff877d8f
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Dockerfile
View file @ 065d06af
......@@ -114,7 +114,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
# Use the /opt/cmake install because LLVM/MLIR need cmake >= 3.20
RUN env PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@1ad9d6df32acc6d29d58e8ed6710e36746d0a4d6 -DBUILD_FAT_LIBROCKCOMPILER=On
RUN env PATH=/opt/cmake/bin:$PATH cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@ea15b3597ce55b9088621818228595dd48fb6ec0 -DBUILD_FAT_LIBROCKCOMPILER=On
ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db
......
src/targets/gpu/include/migraphx/gpu/mlir.hpp
View file @ 065d06af
......@@ -37,7 +37,7 @@ struct module;
namespace gpu {
MIGRAPHX_GPU_EXPORT std::string dump_mlir(const module& m);
MIGRAPHX_GPU_EXPORT code_object_op compile_mlir(const context& ctx,
MIGRAPHX_GPU_EXPORT code_object_op compile_mlir(const context& migraphx_ctx,
module m,
const std::vector<instruction_ref>& inputs,
const value& solution);
......@@ -47,7 +47,8 @@ MIGRAPHX_GPU_EXPORT instruction_ref insert_mlir(module& m,
code_object_op co,
const std::vector<instruction_ref>& inputs);
MIGRAPHX_GPU_EXPORT tuning_config get_tuning_config_mlir(module m,
MIGRAPHX_GPU_EXPORT tuning_config get_tuning_config_mlir(const context& migraphx_ctx,
module m,
const std::vector<shape>& inputs);
} // namespace gpu
......
src/targets/gpu/jit/mlir.cpp
View file @ 065d06af
......@@ -52,14 +52,16 @@ struct mlir_compiler : compiler<mlir_compiler>
}};
}
optional<tuning_config>
get_tuning_config(context&, instruction_ref ins, const operation&, bool exhaustive) const
optional<tuning_config> get_tuning_config(const context& ctx,
instruction_ref ins,
const operation&,
bool exhaustive) const
{
if(not exhaustive)
return nullopt;
auto shapes = to_shapes(ins->inputs());
auto* smod = ins->module_inputs().front();
return get_tuning_config_mlir(*smod, shapes);
return get_tuning_config_mlir(ctx, *smod, shapes);
}
};
......
src/targets/gpu/mlir.cpp
View file @ 065d06af
......@@ -176,12 +176,6 @@ std::string mlir_print(F f, T x)
return ss.str();
}
bool has_xdlops(const std::string& target_arch)
{
const auto device_name = trim(split_string(target_arch, ':').front());
return (starts_with(device_name, "gfx9") and device_name >= "gfx908");
}
struct mlir_program
{
mlir_program()
......@@ -516,7 +510,8 @@ struct mlir_program
ops.add_attributes({{"function_type", make_function_type(inputs, outputs)},
{"sym_name", sym_name},
{"kernel", std::string("mixr")},
{"arch", target_arch}});
{"arch", target_arch},
{"num_cu", num_cu}});
ops.add_region(std::move(region));
insert(body, std::move(ops));
......@@ -599,9 +594,6 @@ struct mlir_program
{
pp =
problem_params{ins->get_operator(), to_shapes(ins->inputs()), ins->get_shape()};
// check if HW supports xdlops
if(has_xdlops(target_arch))
ops.add_attributes({{"xdlopsV2", true}});
}
std::vector<MlirValue> inputs;
......@@ -650,7 +642,12 @@ struct mlir_program
return op;
}
void find_target() { target_arch = get_device_name(); }
void set_gpu_properties(const context& migraphx_ctx)
{
auto& device = migraphx_ctx.get_current_device();
target_arch = device.get_device_name();
num_cu = device.get_cu_count();
}
std::pair<std::size_t, std::size_t> get_launch_params() const
{
......@@ -664,7 +661,7 @@ struct mlir_program
value::binary get_binary() const
{
int size = 0;
size_t size = 0;
mlirGetBinary(mmodule.get(), &size, nullptr);
value::binary result(size);
if(mlirGetBinary(mmodule.get(), &size, reinterpret_cast<char*>(result.data())))
......@@ -674,28 +671,39 @@ struct mlir_program
void set_tuning(const value& v)
{
auto str = v.to<std::string>();
// We need to make a copy of the buffer since mlirRockTuningSetFromStr may modify the string
std::vector<char> buffer(str.begin(), str.end());
buffer.push_back(0);
if(not mlirRockTuningSetFromStr(mmodule.get(), buffer.data()))
MIGRAPHX_THROW("Failed setting tuning key: " + str);
auto* str = v.if_string();
if(not str)
MIGRAPHX_THROW("mlir tuning solutions must be strings");
if(not mlirRockTuningSetFromStr(mmodule.get(), make_mlir_string_ref(*str)))
MIGRAPHX_THROW("Failed setting tuning key: " + *str);
}
tuning_config get_tuning_config() MIGRAPHX_TIDY_CONST
{
tuning_config tc;
run_high_level_pipeline();
mlir_tuning_space params{mlirRockTuningSpaceCreate(mmodule.get())};
for(auto i : range(mlirRockTuningGetNumParamsFull(params.get())))
mlir_tuning_space params{
mlirRockTuningSpaceCreate(mmodule.get(), RocmlirTuningParamSetKindFull)};
for(auto i : range(mlirRockTuningGetNumParams(params.get())))
{
mlir_tuning_param param{mlirRockTuningParamCreate()};
if(not mlirRockTuningParamGet(params.get(), i, param.get()))
MIGRAPHX_THROW("Incorrect mlir tuning parameter: " + std::to_string(i));
tc.solutions.push_back(std::string{mlirRockTuningGetParamStr(param.get())});
std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> perf_key;
size_t perf_key_bytes =
mlirRockTuningParamToString(param.get(), perf_key.data(), perf_key.size());
if(perf_key_bytes > perf_key.size())
MIGRAPHX_THROW("Tuning perf key was " + std::to_string(perf_key_bytes) +
" bytes and thus too long");
tc.solutions.emplace_back(perf_key.begin(), perf_key.begin() + perf_key_bytes);
}
mlir_tuning_table tuning_table{mlirRockTuningTableCreate()};
tc.problem = std::string{mlirRockTuningGetKey(tuning_table.get(), mmodule.get())};
std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> tuning_key;
size_t tuning_key_bytes =
mlirRockTuningGetKey(mmodule.get(), tuning_key.data(), tuning_key.size());
if(tuning_key_bytes > tuning_key.size())
MIGRAPHX_THROW("Tuning table key was " + std::to_string(tuning_key_bytes) +
" bytes and thus too long");
tc.problem = std::string(tuning_key.begin(), tuning_key.begin() + tuning_key_bytes);
return tc;
}
......@@ -703,7 +711,7 @@ struct mlir_program
// This function appends to tuning cfg file that could be
// used with rocMLIR tuning scripts.
void dump_tuning_cfg(const char* prob_config) const
void dump_tuning_cfg(const std::string& prob_config) const
{
std::string tuning_cfg_path = string_value_of(MIGRAPHX_MLIR_TUNING_CFG{});
if(not tuning_cfg_path.empty())
......@@ -723,51 +731,66 @@ struct mlir_program
}
}
static mlir_tuning_table create_tuning_table()
static std::pair<mlir_tuning_table, bool> load_tuning_table()
{
mlir_tuning_table tuning_table{mlirRockTuningTableCreate()};
bool found_table = false;
std::string tuning_db_path = string_value_of(MIGRAPHX_MLIR_TUNING_DB{});
if(not tuning_db_path.empty())
{
std::ifstream tuning_db_tsv(tuning_db_path);
if(tuning_db_tsv)
{
found_table = true;
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);
std::string numCU = tokens[1];
std::string prob = tokens[2];
std::string perf = tokens[3];
std::string key = arch.append("\t").append(numCU).append("\t").append(prob);
mlirRockTuningUpdateTable(tuning_table.get(),
make_mlir_string_ref(key),
make_mlir_string_ref(perf),
1.0);
}
}
}
else
{
found_table = false;
std::cerr
<< "WARNING: MLIR tuning db not found. Please set MIGRAPHX_MLIR_TUNING_DB for "
"optimal performance."
<< std::endl;
}
return tuning_table;
return std::make_pair(std::move(tuning_table), found_table);
}
bool get_module_tuned() const
{
static mlir_tuning_table tuning_table = create_tuning_table();
// The tuning table as currently implemented is currently not
// thread safe. This will be fixed in the future. For now,
// stick a mutex around all tuning table interaction.
static std::mutex lock;
std::lock_guard<std::mutex> guard(lock);
if(not mlirRockTuningSetFromTable(tuning_table.get(), mmodule.get()))
static std::pair<mlir_tuning_table, bool> tuning_table = load_tuning_table();
if(not mlirRockTuningSetFromTable(tuning_table.first.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);
std::array<char, ROCMLIR_TUNING_KEY_BUFSZ> prob_config;
size_t prob_config_bytes =
mlirRockTuningGetKey(mmodule.get(), prob_config.data(), prob_config.size());
if(prob_config_bytes >= prob_config.size())
{
std::cerr << "MLIR tuning key overflowed buffer, needed " << prob_config_bytes
<< " bytes" << std::endl;
return false;
}
std::string prob_config_str(prob_config.begin(),
prob_config.begin() + prob_config_bytes);
if(tuning_table.second)
{
std::cerr << "NOTE: MLIR tuning table did not include a key for " << prob_config_str
<< std::endl;
}
dump_tuning_cfg(prob_config_str);
return false;
}
return true;
......@@ -778,7 +801,8 @@ struct mlir_program
mlir_module mmodule;
problem_params pp;
std::deque<std::string> strings{};
std::string target_arch;
std::string target_arch = "";
std::size_t num_cu = 0;
std::string sym_name;
};
......@@ -835,7 +859,7 @@ void adjust_param_shapes(module& m, const std::vector<shape>& inputs)
}
}
code_object_op compile_mlir(const context&,
code_object_op compile_mlir(const context& migraphx_ctx,
module m,
const std::vector<instruction_ref>& inputs,
const value& solution)
......@@ -847,7 +871,7 @@ code_object_op compile_mlir(const context&,
std::cout << m << std::endl;
mlir_program mp;
mp.find_target();
mp.set_gpu_properties(migraphx_ctx);
mp.parse(m);
auto mod_op = mlirModuleGetOperation(mp.mmodule.get());
if(trace)
......@@ -874,12 +898,13 @@ instruction_ref insert_mlir(module& m,
return m.insert_instruction(ins, co, refs);
}
tuning_config get_tuning_config_mlir(module m, const std::vector<shape>& inputs)
tuning_config
get_tuning_config_mlir(const context& migraphx_ctx, module m, const std::vector<shape>& inputs)
{
adjust_param_shapes(m, inputs);
mlir_program mp;
mp.find_target();
mp.set_gpu_properties(migraphx_ctx);
mp.parse(m);
return mp.get_tuning_config();
}
......@@ -910,7 +935,10 @@ insert_mlir(module& m, instruction_ref, code_object_op co, const std::vector<ins
return m.end();
}
tuning_config get_tuning_config_mlir(module, const std::vector<shape>&) { return {}; }
tuning_config get_tuning_config_mlir(const context&, module, const std::vector<shape>&)
{
return {};
}
// NOLINTEND(performance-unnecessary-value-param)
#endif
......
test/gpu/mlir.cpp
View file @ 065d06af
......@@ -140,7 +140,7 @@ TEST_CASE(conv)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_convolution(%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", num_cu = 0 : i64} {
%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 @mlir_convolution_add_relu(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_convolution_add_relu(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x8x3x3xf32>, %arg2: tensor<1x8x4x4xf32>) -> tensor<1x2x2x2xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%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>
......@@ -191,7 +191,7 @@ TEST_CASE(quant_dot_add)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_quant_dot_add(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_quant_dot_add(%arg0: tensor<1x5x4xi8>, %arg1: tensor<1x4x3xi8>, %arg2: tensor<1x5x3xi32>) -> tensor<1x5x3xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_dot(%arg0, %arg1) : (tensor<1x5x4xi8>, tensor<1x4x3xi8>) -> tensor<1x5x3xi32>
%1 = migraphx.add(%0, %arg2) : (tensor<1x5x3xi32>, tensor<1x5x3xi32>) -> tensor<1x5x3xi32>
return %1 : tensor<1x5x3xi32>
......@@ -218,7 +218,7 @@ TEST_CASE(dot_add)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_add(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_dot_add(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%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>
......@@ -244,7 +244,7 @@ TEST_CASE(conv_int8_dequantize_quantize)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_quant_convolution_dequantizelinear_quantizelinear(%arg0: tensor<2x8x3x3xi8>, %arg1: tensor<1x8x4x4xi8>, %arg2: tensor<1x2x2x2xf32>, %arg3: tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.quant_convolution(%arg1, %arg0) {dilation = [1, 1], group = 1 : i64, padding = [0, 0, 0, 0], padding_mode = 0 : i64, stride = [1, 1]} : (tensor<1x8x4x4xi8>, tensor<2x8x3x3xi8>) -> tensor<1x2x2x2xi32>
%1 = migraphx.dequantizelinear(%0, %arg2, %arg3) : (tensor<1x2x2x2xi32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xf32>
%2 = migraphx.quantizelinear(%1, %arg2, %arg3) : (tensor<1x2x2x2xf32>, tensor<1x2x2x2xf32>, tensor<1x2x2x2xi32>) -> tensor<1x2x2x2xi32>
......@@ -277,7 +277,7 @@ TEST_CASE(dot_convert)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_convert(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_dot_convert(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>) -> tensor<1x5x3xf16> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
%1 = migraphx.convert(%0) {target_type = 1 : i64} : (tensor<1x5x3xf32>) -> tensor<1x5x3xf16>
return %1 : tensor<1x5x3xf16>
......@@ -303,7 +303,7 @@ TEST_CASE(dot_where)
{
const std::string mlir_output = R"__migraphx__(
module {
func.func @mlir_dot_where(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr"} {
func.func @mlir_dot_where(%arg0: tensor<1x5x4xf32>, %arg1: tensor<1x4x3xf32>, %arg2: tensor<1x5x3xi8>, %arg3: tensor<1x5x3xf32>) -> tensor<1x5x3xf32> attributes {arch = "", kernel = "mixr", num_cu = 0 : i64} {
%0 = migraphx.dot(%arg0, %arg1) : (tensor<1x5x4xf32>, tensor<1x4x3xf32>) -> tensor<1x5x3xf32>
%1 = migraphx.where(%arg2, %0, %arg3) : (tensor<1x5x3xi8>, tensor<1x5x3xf32>, tensor<1x5x3xf32>) -> tensor<1x5x3xf32>
return %1 : tensor<1x5x3xf32>
......
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