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Commit a045fb19 authored by Alan Turner's avatar Alan Turner
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Merge branch 'develop' into ck-flash-attn

parents 135eb63e 434a06cf
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Showing with 610 additions and 191 deletions
src/load_save.cpp
View file @ a045fb19
......@@ -21,6 +21,7 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
#include <migraphx/instruction.hpp>
#include <migraphx/load_save.hpp>
#include <migraphx/file_buffer.hpp>
#include <migraphx/json.hpp>
......@@ -60,9 +61,29 @@ void save(const program& p, const std::string& filename, const file_options& opt
{
write_buffer(filename, save_buffer(p, options));
}
// MIOpen doesn't support serializing fusion plans with Find-2.0 APIs
void print_miopen_warning(const program& p)
{
auto mods = p.get_modules();
if(std::any_of(mods.begin(), mods.end(), [](const auto* m) {
return std::any_of(m->begin(), m->end(), [](const instruction& i) {
return i.name() == "gpu::miopen_fusion";
});
}))
{
std::cout << "[WARNING]: Program has miopen_fusion instructions for which tuned solutions "
"are not stored inside serialized MIGraphX program. Consider serializing with "
"MIGRAPHX_DISABLE_MIOPEN_FUSION=1 flag set."
<< std::endl;
;
}
}
std::vector<char> save_buffer(const program& p, const file_options& options)
{
value v = p.to_value();
print_miopen_warning(p);
std::vector<char> buffer;
if(options.format == "msgpack")
{
......
src/msgpack.cpp
View file @ a045fb19
......@@ -25,6 +25,33 @@
#include <migraphx/serialize.hpp>
#include <msgpack.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
// Leave an extra byte for error checking
constexpr std::size_t msgpack_size_limit = std::numeric_limits<uint32_t>::max() - 1;
template <class Range>
std::size_t msgpack_chunk_size(const Range& r)
{
return 1 + (r.size() - 1) / msgpack_size_limit;
}
template <class Iterator, class F>
void msgpack_chunk_for_each(Iterator start, Iterator last, F f)
{
while(std::distance(start, last) > msgpack_size_limit)
{
auto next = std::next(start, msgpack_size_limit);
f(start, next);
start = next;
}
f(start, last);
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
namespace msgpack {
MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
{
......@@ -63,16 +90,31 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
break;
}
case msgpack::type::BIN: {
// For backwards compatibility
v = migraphx::value::binary{o.via.bin.ptr, o.via.bin.size};
break;
}
case msgpack::type::ARRAY: {
migraphx::value r = migraphx::value::array{};
std::for_each(
o.via.array.ptr,
o.via.array.ptr + o.via.array.size,
[&](const msgpack::object& so) { r.push_back(so.as<migraphx::value>()); });
v = r;
if(o.via.array.size != 0 and o.via.array.ptr->type == msgpack::type::BIN)
{
auto bin = migraphx::value::binary{};
std::for_each(
o.via.array.ptr,
o.via.array.ptr + o.via.array.size,
[&](const msgpack::object& so) {
bin.insert(bin.end(), so.via.bin.ptr, so.via.bin.ptr + so.via.bin.size);
});
v = bin;
}
else
{
migraphx::value r = migraphx::value::array{};
std::for_each(
o.via.array.ptr,
o.via.array.ptr + o.via.array.size,
[&](const msgpack::object& so) { r.push_back(so.as<migraphx::value>()); });
v = r;
}
break;
}
case msgpack::type::MAP: {
......@@ -102,8 +144,12 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
{
const auto* data = reinterpret_cast<const char*>(x.data());
auto size = x.size();
o.pack_bin(size);
o.pack_bin_body(data, size);
o.pack_array(migraphx::msgpack_chunk_size(x));
migraphx::msgpack_chunk_for_each(
data, data + size, [&](const char* start, const char* last) {
o.pack_bin(last - start);
o.pack_bin_body(start, last - start);
});
return o;
}
};
......@@ -129,6 +175,8 @@ MSGPACK_API_VERSION_NAMESPACE(MSGPACK_DEFAULT_API_NS)
o.pack_array(0);
return;
}
if(v.size() > migraphx::msgpack_size_limit)
MIGRAPHX_THROW("Size is too large for msgpack");
if(not v.front().get_key().empty())
{
o.pack_map(v.size());
......
src/normalize_attributes.cpp
View file @ a045fb19
......@@ -26,7 +26,7 @@
#include <migraphx/normalize_attributes.hpp>
#include <migraphx/stringutils.hpp>
#include <migraphx/op/normalize_attribute.hpp>
#include <migraphx/op/common.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -192,20 +192,27 @@ bool normalize_attributes(operation& op, const shape& input_shape)
auto val = op.to_value();
if(attrs.contains("normalize_padding"))
{
auto padding = val.at(attrs.at("normalize_padding").to<std::string>());
auto padding_size = padding.size();
auto padding_start = 2;
if(padding_size == 2 * (input_shape.ndim() - padding_start))
tuned = true;
else if(padding_size != (input_shape.ndim() - padding_start))
MIGRAPHX_THROW("inconsistent padding size");
else
bool use_auto_padding =
(val.contains("padding_mode") and
(val.at("padding_mode").to<int>() != migraphx::op::padding_mode_t::default_));
if(not use_auto_padding)
{
auto result = tune_pad_attribute(padding);
val["padding"] = result;
op.from_value(val);
tuned = true;
auto padding = val.at(attrs.at("normalize_padding").to<std::string>());
auto padding_size = padding.size();
auto padding_start = 2;
if(padding_size == 2 * (input_shape.ndim() - padding_start))
tuned = true;
else if(padding_size != (input_shape.ndim() - padding_start))
{
MIGRAPHX_THROW("normalize_attributes: inconsistent padding vector size ");
}
else
{
auto result = tune_pad_attribute(padding);
val["padding"] = result;
op.from_value(val);
tuned = true;
}
}
}
if(not attrs.contains("normalize_axes"))
......
src/onnx/parse_constant.cpp
View file @ a045fb19
......@@ -25,6 +25,7 @@
#include <migraphx/ranges.hpp>
#include <migraphx/literal.hpp>
#include <migraphx/make_op.hpp>
#include <migraphx/stringutils.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -39,16 +40,38 @@ struct parse_constant : op_parser<parse_constant>
onnx_parser::node_info info,
const std::vector<instruction_ref>& /*args*/) const
{
literal v = parser.parse_value(info.attributes.at("value"));
static const std::vector<std::string> attributes = {
"value", "value_float", "value_floats", "value_int", "value_ints"};
std::vector<std::string> present_attributes;
std::copy_if(attributes.begin(),
attributes.end(),
std::back_inserter(present_attributes),
[&](const std::string& a) { return contains(info.attributes, a); });
if(present_attributes.empty())
{
MIGRAPHX_THROW("Constant node does not contain any supported attribute");
}
if(present_attributes.size() > 1)
{
MIGRAPHX_THROW("Constant contains multiple attributes: " +
join_strings(std::move(present_attributes), ", "));
}
// cppcheck-suppress accessMoved
auto&& attr = info.attributes[present_attributes[0]];
literal v = parser.parse_value(attr);
// return empty literal
if(v.get_shape().elements() == 0)
{
return info.add_literal(literal{v.get_shape().type()});
}
auto dim_size = info.attributes.at("value").t().dims_size();
// if dim_size is 0, it is a scalar
if(dim_size == 0)
if(attr.has_t() and attr.t().dims_size() == 0)
{
migraphx::shape scalar_shape{v.get_shape().type()};
return info.add_literal(migraphx::literal{scalar_shape, v.data()});
......
src/onnx/parse_pooling.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -151,26 +151,6 @@ struct parse_pooling : op_parser<parse_pooling>
kdims, paddings.size() / 2, "PARSE_POOLING: inconsistent explicit paddings");
}
if(contains(info.attributes, "auto_pad"))
{
if(in_shape.dynamic())
{
MIGRAPHX_THROW(
"PARSE_POOLING: Auto padding pooling with dynamic input shape not supported");
}
else
{
values["padding"].clear();
// return paddings could be empty, then setting to 0 for no padding
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
{1, 1},
in_shape.lens(),
paddings);
}
}
if(paddings.size() != 2 * kdims)
{
paddings.resize(kdims * 2);
......@@ -192,6 +172,36 @@ struct parse_pooling : op_parser<parse_pooling>
// used to calculate the supposed output shape
std::vector<int64_t> orig_padding = paddings;
// TODO: add parsing for dilations
if(contains(info.attributes, "auto_pad") and
to_upper(info.attributes["auto_pad"].s()) != "NOTSET")
{
auto auto_pad = to_upper(info.attributes["auto_pad"].s());
// don't use the given padding sizes, if any
// values["padding"].clear();
if(in_shape.dynamic())
{
// set padding_mode to trigger auto padding at runtime
bool is_same_upper = (auto_pad.find("SAME_UPPER") != std::string::npos);
values["padding_mode"] = is_same_upper ? to_value(op::padding_mode_t::same_upper)
: to_value(op::padding_mode_t::same_lower);
}
else
{
// Calculate auto padding
// dilations (argument 4) not supported; default to all 1's
cal_auto_padding_size(info,
values,
values["lengths"].to_vector<std::size_t>(),
std::vector<size_t>(in_shape.ndim() - 2, 1),
in_shape.lens(),
paddings);
values["padding"] = paddings;
// default padding_mode indicates that padding sizes are not calculated dynamically
values["padding_mode"] = migraphx::op::padding_mode_t::default_;
}
}
std::vector<int64_t> slice_start;
std::vector<int64_t> slice_end;
tune_padding_size(values, paddings, count_include_pad, slice_start);
......@@ -208,8 +218,9 @@ struct parse_pooling : op_parser<parse_pooling>
orig_padding.insert(orig_padding.begin(), 2, 0);
op::pad pad{orig_padding, 0.0f};
shape padded_shape = pad.compute_shape({l0->get_shape()});
auto out_lens = make_op("pooling", values).compute_shape({padded_shape}).lens();
// make an op just to get its output shape
auto out_lens = make_op("pooling", values).compute_shape({padded_shape}).lens();
// compute slice_end information
slice_end.resize(slice_start.size());
std::transform(out_lens.begin() + 2,
......
src/onnx/parse_resize.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -97,22 +97,19 @@ const auto& get_original_idx_op(const std::string& mode)
static std::vector<int>
calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& vvv_ind,
int i_dim,
const std::vector<std::vector<std::size_t>>& vec_dims,
std::vector<std::vector<std::size_t>> vec_dims,
const shape& in_s)
{
if(i_dim == vvv_ind.size())
{
std::vector<int> vec_ind;
vec_ind.resize(vec_dims.size());
std::vector<int> vec_ind(vec_dims.size());
std::transform(vec_dims.begin(), vec_dims.end(), vec_ind.begin(), [&](auto idx) {
return static_cast<int>(in_s.index(idx));
});
return vec_ind;
}
const auto& vv_ind = vvv_ind[i_dim];
const auto& vv_lo = vv_ind.at(0);
const auto& vv_lo = vvv_ind[i_dim][0];
std::vector<std::vector<std::size_t>> vec_dims1;
for(std::size_t start = 0; start < vec_dims.size(); start += vv_lo.size())
{
......@@ -126,8 +123,8 @@ calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& v
});
}
const auto& vv_hi = vv_ind.at(1);
for(std::size_t start = 0; start < vec_dims.size(); start += vv_lo.size())
const auto& vv_hi = vvv_ind[i_dim][1];
for(std::size_t start = 0; start < vec_dims.size(); start += vv_hi.size())
{
std::transform(vv_hi.begin(),
vv_hi.end(),
......@@ -138,8 +135,8 @@ calc_neighbor_points(const std::vector<std::vector<std::vector<std::size_t>>>& v
return dim;
});
}
return calc_neighbor_points(vvv_ind, i_dim + 1, vec_dims1, in_s);
vec_dims.clear();
return calc_neighbor_points(vvv_ind, i_dim + 1, std::move(vec_dims1), in_s);
}
static std::string get_coord_trans_mode(const onnx_parser::attribute_map& attr)
......@@ -240,7 +237,7 @@ struct parse_resize : op_parser<parse_resize>
auto arg_out_s = arg->eval();
check_arg_empty(arg_out_s,
"PARSE_" + opd.op_name + ": dynamic output size is not supported!");
arg_out_s.visit([&](auto ol) { out_lens.assign(ol.begin(), ol.end()); });
arg_out_s.visit([&](const auto& ol) { out_lens.assign(ol.begin(), ol.end()); });
if(out_lens.size() != in_lens.size())
{
......@@ -267,7 +264,7 @@ struct parse_resize : op_parser<parse_resize>
"PARSE_" + opd.op_name +
": dynamic input scale is not supported!");
arg_scale.visit([&](auto v) { vec_scale.assign(v.begin(), v.end()); });
arg_scale.visit([&](const auto& v) { vec_scale.assign(v.begin(), v.end()); });
if(in_lens.size() != vec_scale.size())
{
MIGRAPHX_THROW("PARSE_" + opd.op_name +
......@@ -300,15 +297,15 @@ struct parse_resize : op_parser<parse_resize>
// map out_idx to in_idx
auto nearest_op = get_nearest_op(nearest_mode);
shape_for_each(out_s, [&](auto idx) {
auto in_idx = idx;
shape_for_each(out_s, [&](const auto& out_idx_v, size_t out_idx) {
std::vector<size_t> in_idx(out_idx_v.size());
for(auto ii = 0; ii < in_lens.size(); ++ii)
{
auto idx_val = idx_op(in_lens[ii], out_lens[ii], idx[ii], vec_scale[ii]);
auto idx_val = idx_op(in_lens[ii], out_lens[ii], out_idx_v[ii], vec_scale[ii]);
in_idx[ii] = nearest_op(in_lens[ii], idx_val);
}
ind[out_s.index(idx)] = static_cast<int64_t>(in_s.index(in_idx));
ind[out_idx] = static_cast<int64_t>(in_s.index(in_idx));
});
shape ind_s{shape::int32_type, out_lens};
......@@ -323,24 +320,21 @@ struct parse_resize : op_parser<parse_resize>
// get the number of dimensions
std::size_t n_dim = out_lens.size();
std::vector<std::vector<std::size_t>> vv_ind(2, std::vector<std::size_t>(out_elements));
std::vector<std::vector<std::vector<std::size_t>>> vvv_ind(n_dim, vv_ind);
auto vvv_ind = std::vector(n_dim, std::vector(2, std::vector<size_t>(out_elements)));
std::vector<std::vector<float>> delta(n_dim, std::vector<float>(out_elements));
shape_for_each(out_s, [&](auto idx) {
auto in_idx = idx;
auto out_idx = out_s.index(idx);
shape_for_each(out_s, [&](const auto& out_idx_v, size_t out_idx) {
for(auto ii = 0; ii < in_lens.size(); ++ii)
{
auto idx_val = idx_op(in_lens[ii], out_lens[ii], idx[ii], vec_scale[ii]);
auto idx_val = idx_op(in_lens[ii], out_lens[ii], out_idx_v[ii], vec_scale[ii]);
vvv_ind[ii][0][out_idx] = nearest_floor(in_lens[ii], idx_val);
vvv_ind[ii][1][out_idx] = nearest_ceil(in_lens[ii], idx_val);
delta[ii][out_idx] = idx_val - vvv_ind[ii][0][out_idx];
}
});
std::vector<std::vector<std::size_t>> vec_dims(out_elements);
auto ind = calc_neighbor_points(vvv_ind, 0, vec_dims, in_s);
auto ind = calc_neighbor_points(
vvv_ind, 0, std::vector<std::vector<std::size_t>>(out_elements), in_s);
auto ind_lens = out_lens;
ind_lens[0] *= (std::size_t{1} << n_dim);
shape ind_s{shape::int32_type, ind_lens};
......
src/onnx/parse_roialign.cpp
View file @ a045fb19
......@@ -37,15 +37,18 @@ struct parse_roialign : op_parser<parse_roialign>
std::vector<op_desc> operators() const { return {{"RoiAlign"}}; }
instruction_ref parse(const op_desc& /*opd*/,
const onnx_parser& /*parser*/,
const onnx_parser& parser,
onnx_parser::node_info info,
const std::vector<instruction_ref>& args) const
{
std::string coord_trans_mode = "half_pixel";
if(contains(info.attributes, "coordinate_transformation_mode"))
std::string coord_trans_mode =
parser.opset_version >= 16 ? "half_pixel" : "output_half_pixel";
if(const auto* a = "coordinate_transformation_mode"; contains(info.attributes, a))
{
coord_trans_mode = info.attributes.at("coordinate_transformation_mode").s();
coord_trans_mode = info.attributes.at(a).s();
}
if(not contains({"half_pixel", "output_half_pixel"}, coord_trans_mode))
{
MIGRAPHX_THROW("coordinate_transformation_mode \"" + coord_trans_mode +
......
src/optimize_module.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -36,8 +36,12 @@ void optimize_module::apply(module_pass_manager& mpm) const
{
for(int i = 0; i < 2; i++)
{
mpm.run_pass(simplify_reshapes{});
mpm.run_pass(simplify_algebra{});
// loop to further optimize after initial transformations
for(int j = 0; j < 2; j++)
{
mpm.run_pass(simplify_reshapes{});
mpm.run_pass(simplify_algebra{});
}
mpm.run_pass(eliminate_common_subexpression{});
mpm.run_pass(dead_code_elimination{});
mpm.run_pass(propagate_constant{});
......
src/pad_calc.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -52,6 +52,11 @@ void calculate_padding(int64_t idx,
}
}
/**
* Given the input array dimensions; kernel (wei_lens); strides; and dilations,
* calculate the padding value in each dimension.
*
*/
std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input_lens,
const std::vector<std::size_t>& wei_lens,
const std::vector<std::size_t>& strides,
......@@ -60,6 +65,7 @@ std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input
{
std::vector<std::size_t> padding;
assert(input_lens.size() >= 3);
assert(input_lens.size() == wei_lens.size());
std::size_t num_spatial_dims = input_lens.size() - 2;
padding.resize(2 * num_spatial_dims);
for(std::size_t i = 0; i < num_spatial_dims; i++)
......@@ -88,6 +94,11 @@ std::vector<std::size_t> calc_dyn_auto_pad(const std::vector<std::size_t>& input
return padding;
}
/**
* Calculate the correct output shape for a convolution with
* a given input size and other parameters.
*
*/
shape compute_padded_shape(const shape& input,
const shape& weights,
const std::vector<std::size_t>& padding,
......@@ -111,5 +122,33 @@ shape compute_padded_shape(const shape& input,
return input.with_lens(output_lens);
}
/**
* Calculate the correct output shape for a pooling with
* a given input size and other parameters. This uses
* the same formula for pooling that compute_padded_shape() uses
* for convolutions, but takes slightly different inputs.
*
*/
shape compute_padded_pool_shape(const shape& input,
const shape& kernel,
const std::vector<std::size_t>& padding,
const std::vector<std::size_t>& stride,
const std::vector<std::size_t>& dilation)
{
const size_t num_spatial_dims = input.lens().size() - 2;
std::vector<size_t> output_lens{input.lens()[0], input.lens()[1]};
// calculate the output shape of the pooling: ((W - K + 2P) / S) + 1
for(size_t i = 0; i < num_spatial_dims; ++i)
{
auto padding_factor = padding[i] + padding[i + num_spatial_dims];
output_lens.push_back(std::size_t(std::max<std::ptrdiff_t>(
1,
(input.lens()[i + 2] - (1 + dilation[i] * (kernel.lens()[i] - 1)) + padding_factor) /
stride[i] +
1)));
}
return input.with_lens(output_lens);
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/program.cpp
View file @ a045fb19
......@@ -624,7 +624,7 @@ std::string get_migraphx_version()
program file version is for the data structure or format of the MXR file. Version should be bumped
if any changes occur to the format of the MXR file.
*/
const int program_file_version = 6;
const int program_file_version = 7;
value program::to_value() const
{
......
src/propagate_constant.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -35,10 +35,10 @@ inline namespace MIGRAPHX_INLINE_NS {
MIGRAPHX_DECLARE_ENV_VAR(MIGRAPHX_TRACE_PROPAGATE_CONSTANT)
bool skip_propogate(instruction_ref ins)
bool skip_propagate(instruction_ref ins)
{
if(ins->name() == "contiguous")
return skip_propogate(ins->inputs().front());
return skip_propagate(ins->inputs().front());
auto&& s = ins->get_shape();
if(s.broadcasted() and not s.scalar())
return true;
......@@ -47,7 +47,7 @@ bool skip_propogate(instruction_ref ins)
return false;
}
bool is_const_ins(instruction_ref ins) { return ins->can_eval() and not skip_propogate(ins); }
bool is_const_ins(instruction_ref ins) { return ins->can_eval() and not skip_propagate(ins); }
void propagate_constant::apply(module& m) const
{
......
src/shape.cpp
View file @ a045fb19
......@@ -50,13 +50,14 @@ struct shape_impl
{
assert(t != shape::tuple_type);
}
shape_impl(shape::type_t t, std::vector<std::size_t> l)
: m_type(t), m_lens(std::move(l)), m_standard(true)
{
assert(t != shape::tuple_type);
this->calculate_strides();
assert(m_lens.size() == m_strides.size());
}
shape_impl(shape::type_t t, std::vector<std::size_t> l, std::vector<std::size_t> s)
: m_type(t), m_lens(std::move(l)), m_strides(std::move(s))
{
......@@ -151,6 +152,22 @@ struct shape_impl
m_lens.begin(), m_lens.end(), std::size_t{1}, std::multiplies<std::size_t>());
}
std::size_t get_index(size_t i) const
{
std::size_t result = 0;
std::size_t s = 1;
for(auto k : migraphx::reverse(migraphx::range(m_lens.size())))
{
std::size_t stride = m_strides[k];
std::size_t len = m_lens[k];
std::size_t idx = (i % (s * len)) / s;
result += stride * idx;
s *= len;
}
return result;
}
std::vector<std::size_t> min_lens() const
{
std::vector<std::size_t> ret(m_dyn_dims.size());
......@@ -213,6 +230,7 @@ std::string shape::name(shape::type_t t)
}
MIGRAPHX_THROW("Invalid type");
}
std::string shape::cpp_type(shape::type_t t)
{
switch(t)
......@@ -229,10 +247,12 @@ std::string shape::cpp_type(shape::type_t t)
shape::shape() : impl(shape_impl::default_shape()) {}
shape::shape(type_t t) : impl(std::make_shared<shape_impl>(t)) {}
shape::shape(type_t t, std::vector<std::size_t> l)
: impl(std::make_shared<shape_impl>(t, std::move(l)))
{
}
shape::shape(type_t t, std::vector<std::size_t> l, std::vector<std::size_t> s)
: impl(std::make_shared<shape_impl>(t, std::move(l), std::move(s)))
{
......@@ -358,21 +378,8 @@ std::size_t shape::index(std::size_t i) const
assert(this->lens().size() == this->strides().size());
if(this->standard())
return i;
else
{
std::size_t s = 1;
std::size_t result = 0;
for(std::size_t j = 0; j < this->lens().size(); j++)
{
const std::size_t k = this->lens().size() - j - 1;
const std::size_t stride = this->strides()[k];
const std::size_t len = this->lens()[k];
const std::size_t idx = (i % (s * len)) / s;
result += stride * idx;
s *= len;
}
return result;
}
return impl->get_index(i);
}
std::vector<std::size_t> shape::multi(std::size_t idx) const
......
src/simplify_algebra.cpp
View file @ a045fb19
......@@ -1325,48 +1325,59 @@ struct find_split_reshape
void apply(module& m, const match::matcher_result& r) const
{
auto slc = r.instructions["slice"];
auto rsp = r.instructions["reshape"];
auto slc = r.instructions["slice"];
auto rsp = r.instructions["reshape"];
auto input = slc->inputs().front();
// Only apply simplification when slices are on a single axis
auto axes = any_cast<op::slice>(slc->get_operator()).axes;
if(axes.size() > 1)
{
return;
}
auto input = slc->inputs().front();
auto split_outputs = get_splits(input);
if(split_outputs.empty())
{
return;
}
// Only want to apply this optimization if each split output is followed by
// a contiguous op and a reshape
if(std::any_of(split_outputs.begin(), split_outputs.end(), [](auto i) {
if(i->outputs().size() == 1)
{
auto cont = i->outputs().front();
return cont->outputs().size() != 1;
}
return false;
}))
// Find all the reshapes (similar to rsp) that can be simplified
std::vector<instruction_ref> conts;
std::vector<instruction_ref> vec_rsp;
// Iterate through slice and contiguous outputs to allow simplifications when
// slice is followed by multiple reshapes
for(auto& i : split_outputs)
{
return;
std::copy_if(i->outputs().begin(),
i->outputs().end(),
std::back_inserter(conts),
[](auto j) { return j->name() == "contiguous"; });
}
std::vector<instruction_ref> vec_rsp(split_outputs.size());
std::transform(split_outputs.begin(), split_outputs.end(), vec_rsp.begin(), [](auto i) {
auto cont = i->outputs().front();
return cont->outputs().front();
});
for(auto& i : conts)
{
std::copy_if(i->outputs().begin(),
i->outputs().end(),
std::back_inserter(vec_rsp),
[&](auto j) { return j->get_operator() == rsp->get_operator(); });
}
// all outputs are reshape and of the same shape
auto dims = any_cast<op::reshape>(rsp->get_operator()).dims;
if(not same_ops(vec_rsp))
// No simplification needed if there is only one slice -> cont -> reshape
if(vec_rsp.size() <= 1)
{
return;
}
// ensure reshape happens after the axis dimension
auto axis = any_cast<op::slice>(slc->get_operator()).axes[0];
auto axis = axes[0];
auto slc_lens = slc->get_shape().lens();
auto slc_dim_size = std::accumulate(
slc_lens.begin() + axis, slc_lens.end(), 1, std::multiplies<std::size_t>());
auto input_lens = input->get_shape().lens();
auto input_size = input->get_shape().elements();
auto slc_axis_len = input_lens[axis];
// search the reshape output (standard shape) to decide which axis are
// in its output corresponding to the slc_dim_size
......@@ -1393,16 +1404,67 @@ struct find_split_reshape
{
rsp_axis = std::distance(rsp_strides.begin(), ait);
}
// calculate reshape output shape
std::vector<int64_t> vec_dims(vec_rsp.size());
std::transform(vec_rsp.begin(), vec_rsp.end(), vec_dims.begin(), [&](auto is) {
return is->get_shape().lens()[rsp_axis];
});
// Calculate reshape output shape
// Need to find a reshape such that data represented by instructions in vec_rsp can be
// written as slices of this new reshape. This is done by holding all the dims constant in
// rsp_lens to compute the required dim for rsp_axis (axis that will be sliced)
// ex 1: Input Shape: {2, 12, 4}, Slice Axis: 1, Slices are: (0:4), (4:8), (8:12),
// Reshape Outputs: {2, 2, 2, 4}, {2, 2, 2, 4}, {2, 2, 2, 4}
// rsp_axis = 1, rsp_out_lens (initial) = {2, 1, 2, 4}, rsp_fixed_size = 2*1*2*4 = 16
// rsp_axis_len = 2*12*4 / 16 = 6
// rsp_out_lens (final) = {2, 6, 2, 4}
// ex 2: Input Shape: {2, 12, 4}, Slice Axis: 1, Slices are: (0:4), (4:8), (8:12),
// Reshape Outputs: {2, 16}, {2, 16}, {2, 16}
// rsp_axis = 1, rsp_out_lens (initial) = {2, 1}, rsp_fixed_size = 2*1 = 2
// rsp_axis_len = 2*12*4 / 2 = 48
// rsp_out_lens (final) = {2, 48}
std::vector<int64_t> rsp_out_lens(rsp_lens.begin(), rsp_lens.end());
rsp_out_lens[rsp_axis] = 1;
auto rsp_fixed_size = std::accumulate(
rsp_out_lens.begin(), rsp_out_lens.end(), 1, std::multiplies<std::size_t>());
rsp_out_lens[rsp_axis] = std::accumulate(vec_dims.begin(), vec_dims.end(), std::int64_t{0});
// cannot create a valid reshape for simplification
if(input_size % rsp_fixed_size != 0)
{
return;
}
auto rsp_axis_len = input_size / rsp_fixed_size;
rsp_out_lens[rsp_axis] = rsp_axis_len;
// Calculate new slice start and end indices. Indices are scaled using the new reshape axis
// and the original slice axis. See examples:
// ex 1: Input Shape: {2, 12, 4}, Slice Axis: 1, Slices are: (0:4), (4:8), (8:12),
// Reshape Outputs: {2, 2, 2, 4}, {2, 2, 2, 4}, {2, 2, 2, 4}
// slc_axis_len = 12, rsp_axis_len = 6
// New Starts: {0*6/12, 4*6/12, 8*6/12} = {0, 2, 4}
// New Ends: {4*6/12, 8*6/12, 12*6/12} = {2, 4, 6}
// ex 2: Input Shape: {2, 12, 4}, Slice Axis: 1, Slices are: (0:4), (4:8), (8:12),
// Reshape Outputs: {2, 16}, {2, 16}, {2, 16}
// slc_axis_len = 12, rsp_axis_len = 48
// New Starts: {0*48/12, 4*48/12, 8*48/12} = { 0, 16, 32}
// New Ends: {4*48/12, 8*48/12, 12*48/12} = {16, 32, 48}
std::vector<int64_t> new_starts(vec_rsp.size());
std::transform(vec_rsp.begin(), vec_rsp.end(), new_starts.begin(), [&](auto is) {
auto cont = is->inputs().front();
auto og_slc = cont->inputs().front();
return any_cast<op::slice>(og_slc->get_operator()).starts[0] * rsp_axis_len /
slc_axis_len;
});
std::vector<int64_t> new_ends(vec_rsp.size());
std::transform(vec_rsp.begin(), vec_rsp.end(), new_ends.begin(), [&](auto is) {
auto cont = is->inputs().front();
auto og_slc = cont->inputs().front();
return any_cast<op::slice>(og_slc->get_operator()).ends[0] * rsp_axis_len /
slc_axis_len;
});
// insert the reshape instruction and add contiguous if needed
if(not input->get_shape().standard())
......@@ -1413,16 +1475,14 @@ struct find_split_reshape
std::next(input), make_op("reshape", {{"dims", rsp_out_lens}}), input);
// replace the original reshape with slice
int64_t start = 0;
for(std::size_t i = 0; i < vec_rsp.size(); ++i)
{
m.replace_instruction(
vec_rsp[i],
make_op(
"slice",
{{"axes", {rsp_axis}}, {"starts", {start}}, {"ends", {start + vec_dims[i]}}}),
{{"axes", {rsp_axis}}, {"starts", {new_starts[i]}}, {"ends", {new_ends[i]}}}),
rsp_ins);
start += vec_dims[i];
}
}
};
......@@ -1446,10 +1506,13 @@ struct find_split_transpose
{
return;
}
if(std::any_of(split_outputs.begin(), split_outputs.end(), [](auto i) {
return i->outputs().size() != 1;
}))
return;
std::vector<instruction_ref> vec_trans(split_outputs.size());
std::transform(split_outputs.begin(), split_outputs.end(), vec_trans.begin(), [](auto i) {
assert(i->outputs().size() == 1);
return i->outputs().front();
});
......
src/simplify_dyn_ops.cpp 0 → 100644
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2023 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/simplify_dyn_ops.hpp>
#include <migraphx/matcher.hpp>
#include <migraphx/make_op.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
/**
* Convert 2 input static shape broadcast/multibroadcast into 1 input version.
* Some compiler passes (ex. simplify_algebra) only support the 1 input versions
* of the broadcasting operators.
*/
struct find_static_2in_broadcasts
{
auto matcher() const
{
return match::broadcast(match::nargs(2),
match::arg(0)(match::static_shape()),
match::arg(1)(match::static_shape()));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto out_lens = ins->get_shape().lens();
auto broadcast_op = ins->get_operator();
if(broadcast_op.name() == "broadcast")
{
broadcast_op.from_value({{"out_lens", out_lens}});
}
else
{
broadcast_op.from_value({{"out_lens", out_lens}, {"out_dyn_dims", {}}});
}
m.replace_instruction(ins, broadcast_op, ins->inputs().at(0));
}
};
/**
* Simplify slice with variable `starts` and `ends` to the constant version if
* the `input_starts` and `input_ends` inputs are constant.
*/
struct find_const_3in_slice
{
auto matcher() const
{
return match::name("slice")(match::nargs(3),
match::arg(1)(match::is_constant()),
match::arg(2)(match::is_constant()));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto inputs = ins->inputs();
argument starts_arg = inputs.at(1)->eval();
argument ends_arg = inputs.at(2)->eval();
if(not starts_arg.empty() and not ends_arg.empty())
{
std::vector<int64_t> starts_vec;
std::vector<int64_t> ends_vec;
starts_arg.visit([&](auto output) { starts_vec.assign(output.begin(), output.end()); });
ends_arg.visit([&](auto output) { ends_vec.assign(output.begin(), output.end()); });
auto slice_val = ins->get_operator().to_value();
auto axes_vec = slice_val.at("axes").to_vector<int64_t>();
m.replace_instruction(
ins,
make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
inputs.at(0));
}
}
};
/**
* Simplify slice with variable `starts`, `ends`, and `input_axes` to the constant version if
* the `input_starts`, `input_ends`, and `input_axes` inputs are constant.
*/
struct find_const_4in_slice
{
auto matcher() const
{
return match::name("slice")(match::nargs(4),
match::arg(1)(match::is_constant()),
match::arg(2)(match::is_constant()),
match::arg(3)(match::is_constant()));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto inputs = ins->inputs();
argument starts_arg = inputs.at(1)->eval();
argument ends_arg = inputs.at(2)->eval();
argument axes_arg = inputs.at(3)->eval();
if(not starts_arg.empty() and not ends_arg.empty() and not axes_arg.empty())
{
std::vector<int64_t> starts_vec;
std::vector<int64_t> ends_vec;
std::vector<int64_t> axes_vec;
starts_arg.visit([&](auto output) { starts_vec.assign(output.begin(), output.end()); });
ends_arg.visit([&](auto output) { ends_vec.assign(output.begin(), output.end()); });
axes_arg.visit([&](auto output) { axes_vec.assign(output.begin(), output.end()); });
m.replace_instruction(
ins,
make_op("slice", {{"starts", starts_vec}, {"ends", ends_vec}, {"axes", axes_vec}}),
inputs.at(0));
}
}
};
void simplify_dyn_ops::apply(module& m) const
{
match::find_matches(
m, find_static_2in_broadcasts{}, find_const_3in_slice{}, find_const_4in_slice{});
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/simplify_reshapes.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......@@ -627,6 +627,30 @@ struct find_transpose_contiguous_reshaper_unary
}
};
struct find_broadcast_transpose
{
auto matcher() const
{
return match::name("transpose")(
match::arg(0)(match::name("multibroadcast").bind("bcast_ins")));
}
void apply(module& m, const match::matcher_result& r) const
{
auto ins = r.result;
auto ins_lens = ins->get_shape().lens();
auto bcast_ins = r.instructions["bcast_ins"];
auto input = bcast_ins->inputs().front();
// for now, focusing on scalar transformation
if(not input->get_shape().scalar())
return;
auto new_mbcast = m.insert_instruction(
bcast_ins, make_op("multibroadcast", {{"out_lens", ins_lens}}), input);
m.replace_instruction(ins, new_mbcast);
}
};
struct find_slice_transpose
{
auto matcher() const
......@@ -784,7 +808,7 @@ struct find_transpose_slice
void simplify_reshapes::apply(module& m) const
{
for(int i = 0; i < 4; i++)
for(int i = 0; i < depth; i++)
{
match::find_matches(m,
find_where_op{},
......@@ -799,6 +823,7 @@ void simplify_reshapes::apply(module& m) const
find_nested_slice{},
find_nested_concat{},
find_transpose_slice{},
find_broadcast_transpose{},
find_slice_transpose{},
find_transpose_contiguous_reshaper_unary{});
dead_code_elimination{}.apply(m);
......
src/split_single_dyn_dim.cpp
View file @ a045fb19
......@@ -68,37 +68,6 @@ has_one_dyn_dim(const std::unordered_map<std::string, shape>& param_shapes)
dds_it->max};
}
namespace {
struct find_static_2in_broadcasts
{
// Convert 2 input static shape broadcast/multibroadcast into 1 input version.
// Some compiler passes (ex. simplify_algebra) only support the 1 input versions
// of the broadcasting operators.
auto matcher() const
{
return match::broadcast(match::nargs(2),
match::arg(0)(match::static_shape()),
match::arg(1)(match::static_shape()));
}
void apply(module& m, const match::matcher_result& mr) const
{
auto ins = mr.result;
auto out_lens = ins->get_shape().lens();
auto broadcast_op = ins->get_operator();
if(broadcast_op.name() == "broadcast")
{
broadcast_op.from_value({{"out_lens", out_lens}});
}
else
{
broadcast_op.from_value({{"out_lens", out_lens}, {"out_dyn_dims", {}}});
}
m.replace_instruction(ins, broadcast_op, ins->inputs().at(0));
}
};
} // namespace
/**
* Makes all the shapes in the dynamic_dimension range. Probably won't work for `if`
* and `loop` instructions, depending on how the submodules for those
......@@ -135,7 +104,6 @@ void split_single_dyn_dim::apply(module_pass_manager& mpm) const
dd_check->dyn_param_str, migraphx::shape{dyn_param_shape.type(), static_lens});
auto outputs = submod->add_instructions(mm, map_ins);
submod->add_return({outputs});
match::find_matches(*submod, find_static_2in_broadcasts{});
submodules.push_back(submod);
}
// redirect to select_module operator and return
......
src/targets/cpu/lowering.cpp
View file @ a045fb19
/*
* The MIT License (MIT)
*
* Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
* Copyright (c) 2015-2023 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
......
src/targets/gpu/CMakeLists.txt
View file @ a045fb19
......@@ -50,6 +50,7 @@ file(GLOB KERNEL_FILES CONFIGURE_DEPENDS
message(STATUS "KERNEL_FILES: ${KERNEL_FILES}")
add_embed_library(migraphx_kernels ${KERNEL_FILES} RELATIVE ${CMAKE_CURRENT_SOURCE_DIR}/kernels/include/)
configure_file(device/targets.hpp.in include/migraphx/gpu/device/targets.hpp)
file(GLOB DEVICE_GPU_SRCS CONFIGURE_DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/device/*.cpp)
add_library(migraphx_device ${DEVICE_GPU_SRCS})
......@@ -69,6 +70,7 @@ rocm_clang_tidy_check(migraphx_device)
target_link_libraries(migraphx_device PUBLIC migraphx)
target_link_libraries(migraphx_device PRIVATE compile_for_gpu)
target_include_directories(migraphx_device PUBLIC $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>)
target_include_directories(migraphx_device PRIVATE $<BUILD_INTERFACE:${CMAKE_CURRENT_BINAR_DIR}/include>)
target_include_directories(migraphx_device PRIVATE $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/device/include>)
target_compile_options(migraphx_device PRIVATE -Wno-ignored-attributes)
migraphx_generate_export_header(migraphx_device DIRECTORY migraphx/gpu/device)
......@@ -123,6 +125,7 @@ add_library(migraphx_gpu
lrn.cpp
mlir.cpp
multinomial.cpp
no_device.cpp
nonzero.cpp
pack_args.cpp
pack_int8_args.cpp
......
src/targets/gpu/compile_hip.cpp
View file @ a045fb19
......@@ -115,6 +115,12 @@ struct hiprtc_program
std::string cpp_src = "";
std::string cpp_name = "";
hiprtc_program(const std::string& src, const std::string& name = "main.cpp")
: cpp_src(src), cpp_name(name)
{
create_program();
}
hiprtc_program(std::vector<hiprtc_src_file> srcs)
{
for(auto&& src : srcs)
......@@ -130,6 +136,14 @@ struct hiprtc_program
include_names.push_back(std::move(src.path));
}
}
create_program();
}
void create_program()
{
assert(not cpp_src.empty());
assert(not cpp_name.empty());
assert(headers.size() == include_names.size());
prog = hiprtc_program_create(cpp_src.c_str(),
cpp_name.c_str(),
headers.size(),
......@@ -137,7 +151,7 @@ struct hiprtc_program
include_names.data());
}
void compile(const std::vector<std::string>& options) const
void compile(const std::vector<std::string>& options, bool quiet = false) const
{
if(enabled(MIGRAPHX_TRACE_HIPRTC{}))
std::cout << "hiprtc " << join_strings(options, " ") << " " << cpp_name << std::endl;
......@@ -148,7 +162,7 @@ struct hiprtc_program
[](const std::string& s) { return s.c_str(); });
auto result = hiprtcCompileProgram(prog.get(), c_options.size(), c_options.data());
auto prog_log = log();
if(not prog_log.empty())
if(not prog_log.empty() and not quiet)
{
std::cerr << prog_log << std::endl;
}
......@@ -210,6 +224,20 @@ std::vector<std::vector<char>> compile_hip_src_with_hiprtc(std::vector<hiprtc_sr
return {prog.get_code_obj()};
}
bool hip_has_flags(const std::vector<std::string>& flags)
{
hiprtc_program prog{" "};
try
{
prog.compile(flags, true);
return true;
}
catch(...)
{
return false;
}
}
std::vector<std::vector<char>>
compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std::string& arch)
{
......@@ -323,6 +351,29 @@ compile_hip_src(const std::vector<src_file>& srcs, std::string params, const std
return {compiler.compile(srcs)};
}
bool hip_has_flags(const std::vector<std::string>& flags)
{
src_compiler compiler;
compiler.compiler = MIGRAPHX_STRINGIZE(MIGRAPHX_HIP_COMPILER);
compiler.flags =
join_strings(flags, " ") + " -x hip -c --offload-arch=gfx900 --cuda-device-only";
std::string src;
src_file input;
input.path = "main.cpp";
input.content = std::make_pair(src.data(), src.data() + src.size());
try
{
compiler.compile({input});
return true;
}
catch(...)
{
return false;
}
}
#endif // MIGRAPHX_USE_HIPRTC
std::string enum_params(std::size_t count, std::string param)
......
src/targets/gpu/compile_hip_code_object.cpp
View file @ a045fb19
......@@ -91,28 +91,39 @@ __content__
return replace_string(args_hpp, "__content__", inner);
}
static std::vector<std::string> get_compiler_warnings()
{
std::vector<std::string> warnings = {
"-Weverything",
"-Wno-c++98-compat",
"-Wno-c++98-compat-pedantic",
"-Wno-conversion",
"-Wno-double-promotion",
"-Wno-exit-time-destructors",
"-Wno-extra-semi",
"-Wno-extra-semi-stmt",
"-Wno-float-conversion",
"-Wno-gnu-anonymous-struct",
"-Wno-gnu-zero-variadic-macro-arguments",
"-Wno-missing-prototypes",
"-Wno-nested-anon-types",
"-Wno-padded",
"-Wno-shorten-64-to-32",
"-Wno-sign-conversion",
"-Wno-sign-compare",
"-Wno-unused-command-line-argument",
"-Wno-weak-vtables",
"-Wno-c99-extensions",
};
if(hip_has_flags({"-Werror", "-Wunsafe-buffer-usage"}))
warnings.push_back("-Wno-unsafe-buffer-usage");
return warnings;
}
const std::vector<std::string>& compiler_warnings()
{
static std::vector<std::string> warnings = {"-Weverything",
"-Wno-c++98-compat",
"-Wno-c++98-compat-pedantic",
"-Wno-conversion",
"-Wno-double-promotion",
"-Wno-exit-time-destructors",
"-Wno-extra-semi",
"-Wno-extra-semi-stmt",
"-Wno-float-conversion",
"-Wno-gnu-anonymous-struct",
"-Wno-gnu-zero-variadic-macro-arguments",
"-Wno-missing-prototypes",
"-Wno-nested-anon-types",
"-Wno-padded",
"-Wno-shorten-64-to-32",
"-Wno-sign-conversion",
"-Wno-sign-compare",
"-Wno-unused-command-line-argument",
"-Wno-weak-vtables",
"-Wno-c99-extensions"};
static std::vector<std::string> warnings = get_compiler_warnings();
return warnings;
}
......
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