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Unverified Commit 20a6f4f5 authored by Charlie Lin's avatar Charlie Lin Committed by GitHub
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Merge branch 'develop' into dyn_reshape

parents 983c7c1f 863bdfbf
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Showing with 229 additions and 27 deletions
Dockerfile
View file @ 20a6f4f5
......@@ -87,7 +87,7 @@ RUN git clone --single-branch --branch ${ONNXRUNTIME_BRANCH} --recursive ${ONNXR
ADD tools/build_and_test_onnxrt.sh /onnxruntime/build_and_test_onnxrt.sh
RUN cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@0f38fb33f518b53b94b541feb9b079668c5518e8 -DBUILD_MIXR_TARGET=On -DLLVM_ENABLE_ZSTD=Off -DLLVM_ENABLE_THREADS=Off
RUN cget -p /usr/local install ROCmSoftwarePlatform/rocMLIR@78b706fe9879587ab98b6614ae539265374a3fae -DBUILD_MIXR_TARGET=On -DLLVM_ENABLE_ZSTD=Off -DLLVM_ENABLE_THREADS=Off
ENV MIOPEN_FIND_DB_PATH=/tmp/miopen/find-db
ENV MIOPEN_USER_DB_PATH=/tmp/miopen/user-db
......
Jenkinsfile
View file @ 20a6f4f5
......@@ -11,16 +11,22 @@ def rocmtestnode(Map conf) {
def image = 'migraphxlib'
env.CCACHE_COMPRESSLEVEL = 7
env.CCACHE_DIR = ccache
def cmake_build = { compiler, flags ->
def cmake_build = { bconf ->
def compiler = bconf.get("compiler", "/opt/rocm/llvm/bin/clang++")
def flags = bconf.get("flags", "")
def gpu_debug = bconf.get("gpu_debug", "0")
def cmd = """
env
ulimit -c unlimited
echo "leak:dnnl::impl::malloc" > suppressions.txt
export LSAN_OPTIONS="suppressions=\$(pwd)/suppressions.txt"
export MIGRAPHX_GPU_DEBUG=${gpu_debug}
export CXX=${compiler}
export CXXFLAGS='-Werror'
env
rm -rf build
mkdir build
cd build
CXX=${compiler} CXXFLAGS='-Werror' cmake -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache ${flags} ..
cmake -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache ${flags} ..
make -j\$(nproc) generate all doc package check VERBOSE=1
"""
echo cmd
......@@ -93,28 +99,32 @@ rocmtest clang_debug: rocmnode('vega') { cmake_build ->
stage('Hip Clang Debug') {
def sanitizers = "undefined"
def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
}
}, clang_gpu_debug: rocmnode('vega') { cmake_build ->
stage('Hip Clang GPU Debug') {
cmake_build(flags: "-DCMAKE_BUILD_TYPE=release", gpu_debug: true)
}
}, clang_release: rocmnode('vega') { cmake_build ->
stage('Hip Clang Release') {
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=release")
cmake_build(flags: "-DCMAKE_BUILD_TYPE=release")
stash includes: 'build/*.deb', name: 'migraphx-package'
}
}, mlir_debug: rocmnode('vega') { cmake_build ->
stage('MLIR Debug') {
def sanitizers = "undefined"
def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
}
}, clang_asan: rocmnode('nogpu') { cmake_build ->
stage('Clang ASAN') {
def sanitizers = "undefined,address"
def debug_flags = "-g -O2 -fno-omit-frame-pointer -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=Off -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
cmake_build(flags: "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=Off -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}' -DCMAKE_C_FLAGS_DEBUG='${debug_flags}'")
}
}//, clang_release_navi: rocmnode('navi21') { cmake_build ->
// stage('HIP Clang Release Navi') {
// cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=release")
// cmake_build(flags: "-DCMAKE_BUILD_TYPE=release")
// }
//}
......
src/include/migraphx/op/reduce_op.hpp
View file @ 20a6f4f5
......@@ -26,6 +26,7 @@
#include <migraphx/op/name.hpp>
#include <migraphx/check_shapes.hpp>
#include <migraphx/dyn_output.hpp>
#include <migraphx/argument.hpp>
#include <migraphx/tensor_view.hpp>
#include <migraphx/shape_for_each.hpp>
......@@ -105,18 +106,41 @@ struct reduce_op : op_name<Derived>
return tuned_axes;
}
/**
* @brief returns a shape in which the axis or axes named
* for reduction by this op are set, to size 1.
*
* @param inputs list of input shapes
* @return shape
*/
shape normalize_compute_shape(std::vector<shape> inputs) const
{
check_shapes{inputs, *this}.has(1);
auto s = inputs.at(0);
auto lens = s.lens();
auto tuned_axes = tune_axes(lens.size());
for(auto axis : tuned_axes)
check_shapes{inputs, *this, true}.has(1);
auto s = inputs.at(0);
if(s.dynamic())
{
lens[axis] = 1;
auto output_dyn_dims = s.dyn_dims();
auto tuned_axes = tune_axes(output_dyn_dims.size());
for(const auto& axis : tuned_axes)
{
// At the time of writing, there's no functional difference between
// optimum of 0 (no opt) or 1.
output_dyn_dims[axis] = {1, 1, 0};
}
return shape{s.type(), output_dyn_dims};
}
else
{
auto lens = s.lens();
auto tuned_axes = tune_axes(lens.size());
for(const auto& axis : tuned_axes)
{
lens[axis] = 1;
}
return inputs[0].with_lens(lens);
}
return inputs[0].with_lens(lens);
}
template <class T>
......@@ -124,7 +148,7 @@ struct reduce_op : op_name<Derived>
const std::vector<T>& in_lens,
std::vector<T>& out_lens) const
{
for(auto axis : tuned_axes)
for(const auto& axis : tuned_axes)
{
out_lens[axis] = in_lens[axis];
}
......@@ -151,17 +175,17 @@ struct reduce_op : op_name<Derived>
static_cast<const Derived&>(*this).output(batch_shape)(val);
}
argument compute(const shape& output_shape, std::vector<argument> args) const
argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
{
argument result{output_shape};
argument result{dyn_out.computed_shape};
auto arg_lens = args.front().get_shape().lens();
auto tuned_axes = tune_axes(arg_lens.size());
std::vector<std::size_t> batch_lens(output_shape.lens().size(), 1);
std::vector<std::size_t> batch_lens(dyn_out.computed_shape.lens().size(), 1);
tune_dims(tuned_axes, arg_lens, batch_lens);
shape batch_shape{output_shape.type(), batch_lens};
shape batch_shape{dyn_out.computed_shape.type(), batch_lens};
visit_all(result, args[0])([&](auto output, auto input) {
par_for(output_shape.elements(), [&](auto i) {
auto out_idx = output_shape.multi(i);
par_for(dyn_out.computed_shape.elements(), [&](auto i) {
auto out_idx = dyn_out.computed_shape.multi(i);
this->reduce(input, batch_shape, tuned_axes, out_idx, output);
});
});
......
src/onnx/parse_reduce_op.cpp
View file @ 20a6f4f5
......@@ -68,8 +68,7 @@ instruction_ref parse_reduce_oper(const std::string& op_name,
}
else
{
std::size_t n_dim = args.front()->get_shape().lens().size();
axes.resize(n_dim);
axes.resize(args.front()->get_shape().ndim());
std::iota(axes.begin(), axes.end(), 0);
}
}
......
test/onnx/gen_onnx.py 100755 → 100644
View file @ 20a6f4f5
......@@ -23,7 +23,7 @@
#####################################################################################
# This script generates onnx files for MIGraphX onnx operator tests.
# To generate an individual onnx file, you can use the following
# command: python -c "import gen_onnx; gen_onnx.{test_name}_test()"
# command: python3 -c "import gen_onnx; gen_onnx.{test_name}_test()"
import numpy as np
import onnx
from onnx import helper
......@@ -4675,6 +4675,34 @@ def reducel1_test():
return ([node], [x], [y])
@onnx_test
def reducel1_dyn_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
axes = [-2]
node = onnx.helper.make_node('ReduceL1',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims=0)
return ([node], [x], [y])
@onnx_test
def reducel1_dyn_noaxes_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None])
node = onnx.helper.make_node('ReduceL1',
inputs=['x'],
outputs=['y'],
keepdims=0)
return ([node], [x], [y])
@onnx_test()
def reducel2_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
......@@ -4724,6 +4752,22 @@ def reduce_log_sum_exp_test():
def reducemax_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [3, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [3, 4, 6])
axes = [2]
node = onnx.helper.make_node('ReduceMax',
inputs=['x'],
outputs=['y'],
axes=axes,
keepdims=0)
return ([node], [x], [y])
@onnx_test
def reducemax_dyn_test():
x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [None, 4, 5, 6])
y = helper.make_tensor_value_info('y', TensorProto.FLOAT, [None, 4, 6])
axes = [2]
node = onnx.helper.make_node('ReduceMax',
......
test/onnx/onnx_test.cpp
View file @ 20a6f4f5
......@@ -4468,6 +4468,50 @@ TEST_CASE(reducel1_test)
EXPECT(p == prog);
}
TEST_CASE(reducel1_dyn_test)
{
{
migraphx::program p;
auto* mm = p.get_main_module();
// a shape with 4 dynamic dimensions
auto l0 = mm->add_parameter("x",
migraphx::shape{migraphx::shape::float_type,
{{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
auto sum_ins =
mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {-2}}}), abs_ins);
auto sq_ins = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {-2}}}), sum_ins);
mm->add_return({sq_ins});
migraphx::onnx_options options;
options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
auto prog = migraphx::parse_onnx("reducel1_dyn_test.onnx", options);
EXPECT(p == prog);
}
{
migraphx::program p;
auto* mm = p.get_main_module();
// No axes given in the onnx file. Parser should default to all axes.
auto l0 = mm->add_parameter("x",
migraphx::shape{migraphx::shape::float_type,
{{3, 3, 0}, {3, 5, 0}, {4, 6, 5}, {5, 7, 6}}});
auto abs_ins = mm->add_instruction(migraphx::make_op("abs"), l0);
auto sum_ins =
mm->add_instruction(migraphx::make_op("reduce_sum", {{"axes", {0, 1, 2, 3}}}), abs_ins);
auto sq_ins =
mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {0, 1, 2, 3}}}), sum_ins);
mm->add_return({sq_ins});
migraphx::onnx_options options;
options.map_dyn_input_dims["x"] = {{3, 3}, {3, 5}, {4, 6, 5}, {5, 7, 6}};
auto prog = migraphx::parse_onnx("reducel1_dyn_noaxes_test.onnx", options);
EXPECT(p == prog);
}
}
TEST_CASE(reducel2_test)
{
migraphx::program p;
......@@ -4518,6 +4562,24 @@ TEST_CASE(reducemax_test)
EXPECT(p == prog);
}
TEST_CASE(reducemax_dyn_test)
{
// input shape with 4 dynamic dimensions
migraphx::program p;
auto* mm = p.get_main_module();
auto l0 = mm->add_parameter(
"x", migraphx::shape{migraphx::shape::float_type, {{3, 3}, {4, 4}, {5, 5}, {6, 6}}});
auto r0 = mm->add_instruction(migraphx::make_op("reduce_max", {{"axes", {2}}}), l0);
auto r1 = mm->add_instruction(migraphx::make_op("squeeze", {{"axes", {2}}}), r0);
mm->add_return({r1});
migraphx::onnx_options options;
options.map_dyn_input_dims["x"] = {{3, 3}, {4, 4}, {5, 5}, {6, 6}};
auto prog = migraphx::parse_onnx("reducemax_dyn_test.onnx", options);
EXPECT(p == prog);
}
TEST_CASE(reducemean_test)
{
migraphx::program p;
......
test/op_shape_test.cpp
View file @ 20a6f4f5
......@@ -1946,9 +1946,51 @@ void test_reduce_ops()
}
}
// dynamic shape
template <class T>
void test_dyn_reduce_ops()
{
{
migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
expect_shape(migraphx::shape{migraphx::shape::float_type,
std::vector<migraphx::shape::dynamic_dimension>(
{{2, 3, 3}, {1, 1, 0}})},
T{{-1}},
input);
}
{
migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
expect_shape(migraphx::shape{migraphx::shape::float_type,
std::vector<migraphx::shape::dynamic_dimension>(
{{1, 1, 0}, {2, 4, 4}})},
T{{0}},
input);
}
{
// Empty axis argument reduces all axes
migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
expect_shape(migraphx::shape{migraphx::shape::float_type,
std::vector<migraphx::shape::dynamic_dimension>(
{{1, 1, 0}, {1, 1, 0}})},
T{{}},
input);
}
{
migraphx::shape input{migraphx::shape::float_type, {{2, 3, 3}, {2, 4, 4}}};
throws_shape(T{{4}}, input);
}
}
TEST_CASE(reduce_max) { test_reduce_ops<migraphx::op::reduce_max>(); }
TEST_CASE(reduce_mean) { test_reduce_ops<migraphx::op::reduce_mean>(); }
TEST_CASE(reduce_prod) { test_reduce_ops<migraphx::op::reduce_prod>(); }
TEST_CASE(reduce_sum) { test_reduce_ops<migraphx::op::reduce_sum>(); }
TEST_CASE(reduce_max_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_max>(); }
TEST_CASE(reduce_mean_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_mean>(); }
TEST_CASE(reduce_prod_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_prod>(); }
TEST_CASE(reduce_sum_dyn) { test_dyn_reduce_ops<migraphx::op::reduce_sum>(); }
TEST_CASE(reshape_shape)
{
migraphx::shape input{migraphx::shape::float_type, {24, 1, 1, 1}};
......
test/ref_ops_test.cpp
View file @ 20a6f4f5
......@@ -5753,6 +5753,27 @@ TEST_CASE(reduce_max_axis0)
EXPECT(results_vector == gold);
}
TEST_CASE(reduce_max_dynamic_axis0)
{
migraphx::program p;
auto* mm = p.get_main_module();
migraphx::shape s{migraphx::shape::float_type, {{2, 4, 2}, {3, 5, 3}}};
auto input = mm->add_parameter("X", s);
auto reduce_max_op = migraphx::make_op("reduce_max", {{"axes", {0}}});
mm->add_instruction(reduce_max_op, input);
p.compile(migraphx::ref::target{});
migraphx::parameter_map params;
migraphx::shape input_fixed_shape{migraphx::shape::float_type, {2, 5}};
std::vector<float> input_data{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
params["X"] = migraphx::argument(input_fixed_shape, input_data.data());
auto result = p.eval(params).back();
std::vector<float> results_vector;
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
std::vector<float> gold = {6, 7, 8, 9, 10};
EXPECT(migraphx::verify_range(results_vector, gold));
}
TEST_CASE(reduce_max_axis01)
{
migraphx::program p;
......
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