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Commit dcb98a60 authored by Paul's avatar Paul
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Merge branch 'develop' into ubuntu-22.04-default

parents d05768a4 d2486dcd
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Showing with 273 additions and 132 deletions
src/module.cpp
View file @ dcb98a60
......@@ -873,12 +873,11 @@ module::print_py(std::ostream& os,
if(ins->name() == "@literal")
{
os << mname << ".add_literal(";
bool use_abs = false;
ins->get_literal().visit([&](auto v) {
use_abs = std::none_of(v.begin(), v.end(), [](auto x) { return x < 0; });
});
const bool use_abs = false;
// Disable abs for now
use_abs = false;
// ins->get_literal().visit([&](auto v) {
// use_abs = std::none_of(v.begin(), v.end(), [](auto x) { return x < 0; });
// });
if(use_abs)
os << "migraphx.abs_literal(";
os << "migraphx.generate_argument(";
......@@ -1011,9 +1010,17 @@ std::vector<module_ref> module::get_sub_modules(bool shallow) const
module& module::sort()
{
auto implicit_deps = calc_implicit_deps();
fix([&](auto self, auto ins) {
this->move_instruction(ins, this->begin());
for(auto child : ins->inputs())
auto ins_inputs = ins->inputs();
if(implicit_deps.find(ins) != implicit_deps.end())
{
auto ins_implict_inputs = implicit_deps.at(ins);
ins_inputs.insert(
ins_inputs.end(), ins_implict_inputs.begin(), ins_implict_inputs.end());
}
for(auto child : ins_inputs)
{
if(not contains(this->impl->instructions, child))
{
......
src/normalize_attributes.cpp
View file @ dcb98a60
......@@ -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 {
......@@ -49,6 +49,10 @@ auto tune_attribute(const std::vector<int64_t>& vec,
Message m)
{
std::vector<int64_t> result(vec);
if(result.empty())
{
return result;
};
int64_t n_rank = input_shape.ndim();
std::vector<op::normalize_attribute> vec_attrs = val.to_vector<op::normalize_attribute>();
if(contains(vec_attrs, op::normalize_attribute::use_output))
......@@ -188,20 +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"))
......@@ -251,5 +262,22 @@ bool normalize_attributes(operation& op, const shape& input_shape)
return tuned;
}
std::vector<int64_t> normalize_axes(const std::vector<int64_t>& axes,
const shape& input_shape,
const value& attr_val,
const std::string& prefix)
{
return tune_attribute(axes, {}, attr_val, input_shape, [&] { return prefix; });
}
std::vector<int64_t> normalize_indices(const std::vector<int64_t>& indices,
const std::vector<int64_t>& axes,
const shape& input_shape,
const value& attr_val,
const std::string& prefix)
{
return tune_attribute(indices, axes, attr_val, input_shape, [&] { return prefix; });
}
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
src/onnx/include/migraphx/onnx/onnx_parser.hpp
View file @ dcb98a60
......@@ -117,6 +117,7 @@ struct onnx_parser
parse_graph(module* mod, const onnx::GraphProto& graph, bool inlining = false);
literal parse_value(const onnx::AttributeProto& attr) const;
literal parse_tensor(const onnx::TensorProto& t) const;
shape parse_type(const onnx::TypeProto& t) const;
shape parse_type(const onnx::TypeProto& t, const std::vector<std::size_t>& input_dims) const;
};
......
src/onnx/onnx_parser.cpp
View file @ dcb98a60
......@@ -357,10 +357,9 @@ parse_inputs(const onnx_parser& parser,
}
shape s;
std::vector<std::size_t> dims;
if(parser.map_input_dims.count(name) > 0)
{
dims = parser.map_input_dims.at(name);
std::vector<std::size_t> dims = parser.map_input_dims.at(name);
s = parser.parse_type(input.type(), dims);
}
else if(parser.map_dyn_input_dims.count(name) > 0)
......@@ -370,7 +369,7 @@ parse_inputs(const onnx_parser& parser,
}
else
{
s = parser.parse_type(input.type(), dims);
s = parser.parse_type(input.type());
}
mod_insts[name] = mod->add_parameter(name, s);
}
......@@ -553,14 +552,9 @@ literal onnx_parser::parse_tensor(const onnx::TensorProto& t) const
}
MIGRAPHX_THROW("PARSE_TENSOR: Invalid tensor type");
}
shape onnx_parser::parse_type(const onnx::TypeProto& t,
const std::vector<std::size_t>& input_dims) const
shape onnx_parser::parse_type(const onnx::TypeProto& t) const
{
shape::type_t shape_type = get_type(t.tensor_type().elem_type());
if(not input_dims.empty())
{
return {shape_type, input_dims};
}
std::vector<shape::dynamic_dimension> dynamic_dims;
auto&& tensor_dims = t.tensor_type().shape().dim();
......@@ -590,6 +584,15 @@ shape onnx_parser::parse_type(const onnx::TypeProto& t,
return shape_from_dyn_dims(shape_type, dynamic_dims);
}
shape onnx_parser::parse_type(const onnx::TypeProto& t,
const std::vector<std::size_t>& input_dims) const
{
shape::type_t shape_type = get_type(t.tensor_type().elem_type());
if(input_dims.empty())
return {shape_type};
return {shape_type, input_dims};
}
shape::type_t get_type(int dtype)
{
switch(dtype)
......
src/onnx/parse_constant_of_shape.cpp
View file @ dcb98a60
......@@ -55,9 +55,6 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
l_val = literal({shape::float_type, {1}, {0}}, {0.0f});
}
// input is empty, output is a scalar
auto type = l_val.get_shape().type();
if(args.empty())
{
MIGRAPHX_THROW("ConstantOfShape : must have 1 input!");
......@@ -65,6 +62,8 @@ struct parse_constant_of_shape : op_parser<parse_constant_of_shape>
else
{
migraphx::shape s;
// input is empty, output is a scalar
auto type = l_val.get_shape().type();
// empty input tensor, output is a scalar
if(args[0]->get_shape().elements() == 0)
{
......
src/onnx/parse_pooling.cpp
View file @ dcb98a60
/*
* 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_randomuniform_ops.cpp
View file @ dcb98a60
......@@ -96,7 +96,7 @@ struct parse_randomuniform_ops : op_parser<parse_randomuniform_ops>
if(contains(info.attributes, "seed"))
gen.seed(info.attributes.at("seed").f());
std::uniform_real_distribution<> d(high, low);
std::uniform_real_distribution<> d(low, high);
std::vector<double> rand_vals(out_shape.elements());
std::generate(rand_vals.begin(), rand_vals.end(), [&]() { return d(gen); });
......
src/onnx/parse_slice.cpp
View file @ dcb98a60
......@@ -34,16 +34,65 @@ namespace onnx {
struct parse_slice : op_parser<parse_slice>
{
std::vector<op_desc> operators() const { return {{"Slice"}}; }
struct slice_desc
{
op::slice op;
std::vector<instruction_ref> op_args;
std::vector<int64_t> steps;
std::vector<int64_t> raxes;
void always_insert(instruction_ref arg) { op_args.insert(op_args.begin(), arg); }
std::vector<int64_t> insert(instruction_ref arg)
{
std::vector<int64_t> result;
migraphx::argument arg_value = arg->eval();
if(arg_value.empty())
{
op_args.insert(op_args.begin(), arg);
}
else
{
arg_value.visit([&](auto s) { result.assign(s.begin(), s.end()); });
}
return result;
}
};
instruction_ref parse(const op_desc& /*opd*/,
const onnx_parser& parser,
onnx_parser::node_info info,
std::vector<instruction_ref> args) const
const onnx_parser::node_info& info,
const std::vector<instruction_ref>& args) const
{
op::slice op;
auto sd = construct_slice_desc(parser, info, args);
auto ins = info.add_instruction(sd.op, sd.op_args);
if(not sd.raxes.empty())
{
ins = info.add_instruction(make_op("reverse", {{"axes", sd.raxes}}), ins);
}
// If any steps are other than default 1, add a "steps" op
if(std::any_of(sd.steps.begin(), sd.steps.end(), [](auto s) { return std::abs(s) != 1; }))
{
std::vector<int64_t> nsteps;
std::transform(sd.steps.begin(),
sd.steps.end(),
std::back_inserter(nsteps),
[](auto s) { return std::abs(s); });
return ins = info.add_instruction(
make_op("step", {{"axes", sd.op.axes}, {"steps", nsteps}}), ins);
}
else
return ins;
}
std::vector<int64_t> steps;
slice_desc construct_slice_desc(const onnx_parser& parser,
onnx_parser::node_info info,
std::vector<instruction_ref> args) const
{
slice_desc sd;
// slice can have up to 5 inputs, we first check the 5th one
// to decide whether MIGRAPHX can handle this slice.
......@@ -51,89 +100,73 @@ struct parse_slice : op_parser<parse_slice>
{
migraphx::argument step_arg = args.back()->eval();
check_arg_empty(step_arg, "PARSE_SLICE: cannot handle variable steps for slice");
step_arg.visit([&](auto s) { steps.assign(s.begin(), s.end()); });
step_arg.visit([&](auto s) { sd.steps.assign(s.begin(), s.end()); });
}
if(args.size() >= 4)
{
migraphx::argument axes_arg = args.at(3)->eval();
check_arg_empty(axes_arg, "PARSE_SLICE: cannot handle variable axes for slice");
axes_arg.visit([&](auto s) { op.axes.assign(s.begin(), s.end()); });
sd.op.axes = sd.insert(args.at(3));
}
else if(contains(info.attributes, "axes"))
{
literal s = parser.parse_value(info.attributes.at("axes"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.axes)); });
s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.axes)); });
}
if(args.size() >= 3)
{
migraphx::argument end_arg = args.at(2)->eval();
check_arg_empty(end_arg, "PARSE_SLICE: cannot handle variable ends for slice");
end_arg.visit([&](auto s) { op.ends.assign(s.begin(), s.end()); });
sd.op.ends = sd.insert(args.at(2));
}
else if(contains(info.attributes, "ends"))
{
literal s = parser.parse_value(info.attributes.at("ends"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.ends)); });
s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.ends)); });
}
if(args.size() >= 2)
{
migraphx::argument start_arg = args.at(1)->eval();
check_arg_empty(start_arg, "PARSE_SLICE: cannot handle variable starts for slice");
start_arg.visit([&](auto s) { op.starts.assign(s.begin(), s.end()); });
sd.op.starts = sd.insert(args.at(1));
}
else if(contains(info.attributes, "starts"))
{
literal s = parser.parse_value(info.attributes.at("starts"));
s.visit([&](auto v) { copy(v, std::back_inserter(op.starts)); });
s.visit([&](auto v) { copy(v, std::back_inserter(sd.op.starts)); });
}
// data input argument
sd.always_insert(args.at(0));
// If axes arg is not given, the default is all of them.
if(op.axes.empty())
if(sd.op.axes.empty() and sd.op_args.size() < 3)
{
std::vector<int64_t> axes(args[0]->get_shape().ndim());
std::iota(axes.begin(), axes.end(), int64_t{0});
op.axes = axes;
sd.op.axes = axes;
}
std::vector<int64_t> raxes;
if(not sd.steps.empty())
{
if(sd.op.starts.empty() or sd.op.ends.empty())
MIGRAPHX_THROW("PARSE_SLICE: steps and variable starts and ends is not supported");
if(sd.op.axes.empty())
MIGRAPHX_THROW("PARSE_SLICE: steps and variable axes is not supported");
}
assert(steps.empty() or steps.size() == op.axes.size());
assert(op.axes.size() == op.starts.size());
assert(op.axes.size() == op.ends.size());
assert(sd.steps.empty() or sd.steps.size() == sd.op.axes.size());
// If any axes have negative step, prepare to add a "reverse" op
for(auto i : range(steps.size()))
for(auto i : range(sd.steps.size()))
{
if(steps[i] >= 0)
if(sd.steps[i] >= 0)
continue;
op.starts[i] += 1;
if(op.starts[i] == 0)
op.starts[i] = INT_MAX;
op.ends[i] += 1;
raxes.push_back(op.axes[i]);
std::swap(op.starts[i], op.ends[i]);
}
auto ins = info.add_instruction(op, args[0]);
if(not raxes.empty())
{
ins = info.add_instruction(make_op("reverse", {{"axes", raxes}}), ins);
sd.op.starts[i] += 1;
if(sd.op.starts[i] == 0)
sd.op.starts[i] = INT_MAX;
sd.op.ends[i] += 1;
sd.raxes.push_back(sd.op.axes[i]);
std::swap(sd.op.starts[i], sd.op.ends[i]);
}
// If any steps are other than default 1, add a "steps" op
if(std::any_of(steps.begin(), steps.end(), [](auto s) { return std::abs(s) != 1; }))
{
std::vector<int64_t> nsteps;
std::transform(steps.begin(), steps.end(), std::back_inserter(nsteps), [](auto s) {
return std::abs(s);
});
return ins = info.add_instruction(
make_op("step", {{"axes", op.axes}, {"steps", nsteps}}), ins);
}
else
return ins;
return sd;
}
};
......
src/pad_calc.cpp
View file @ dcb98a60
/*
* 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 @ dcb98a60
......@@ -40,13 +40,14 @@
#include <migraphx/make_op.hpp>
#include <migraphx/marker.hpp>
#include <migraphx/supported_segments.hpp>
#include <iostream>
#include <queue>
#include <sstream>
#include <algorithm>
#include <set>
#include <unordered_map>
#include <utility>
#include <unordered_set>
#include <map>
#include <cassert>
......@@ -222,7 +223,7 @@ void program::compile(const std::vector<target>& targets, std::vector<compile_op
// Gather all the target roots
std::unordered_multimap<std::size_t, module_ref> roots;
auto mods = this->get_modules();
for(auto* mod : mods)
for(const auto* mod : mods)
{
for(const auto& ins : *mod)
{
......@@ -547,7 +548,7 @@ std::vector<argument> program::eval(parameter_map params, execution_environment
ins_out[x] = ss.str();
});
ret = generic_eval(*this, contexts, std::move(params), [&](instruction_ref ins, auto f) {
auto& ctx = contexts[ins->get_target_id()];
const auto& ctx = contexts[ins->get_target_id()];
ctx.finish();
std::cout << "Run instruction: " << ins_out.at(ins) << std::endl;
timer t{};
......@@ -727,7 +728,7 @@ static void mod_from_val(module_ref mod,
std::back_inserter(module_inputs),
[&](const value& i) { return map_mods.at(i.to<std::string>()); });
for(auto& smod : module_inputs)
for(const auto& smod : module_inputs)
{
mod_from_val(smod, v, instructions, map_mods);
}
......@@ -1185,17 +1186,25 @@ void program::remove_unused_modules()
std::vector<module*> unused;
generic_get_unused_modules(
impl->modules, generic_get_modules(this->get_main_module()), std::back_inserter(unused));
for(auto* m : unused)
for(const auto* m : unused)
this->remove_module(m->name());
}
program& program::sort()
{
for(auto& pp : this->impl->modules)
std::queue<migraphx::module_ref> mqueue;
mqueue.push(get_main_module());
while(not mqueue.empty())
{
pp.second.sort();
module_ref current_mod = mqueue.front();
current_mod->sort();
mqueue.pop();
auto child_mods = current_mod->get_sub_modules(true);
for(auto& sub_mod : child_mods)
{
mqueue.push(sub_mod);
}
}
return *this;
}
......
src/py/CMakeLists.txt
View file @ dcb98a60
......@@ -24,6 +24,7 @@
option(MIGRAPHX_ENABLE_PYTHON "Enable python bindings" ON)
add_library(migraphx_py py_loader.cpp)
migraphx_generate_export_header(migraphx_py)
target_include_directories(migraphx_py PRIVATE include)
target_link_libraries(migraphx_py PUBLIC migraphx)
rocm_install_targets(TARGETS migraphx_py INCLUDE include)
......
src/py/include/migraphx/py.hpp
View file @ dcb98a60
......@@ -26,11 +26,12 @@
#include <migraphx/config.hpp>
#include <migraphx/program.hpp>
#include <migraphx/py/export.h>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
program load_py(const std::string& filename);
MIGRAPHX_PY_EXPORT program load_py(const std::string& filename);
} // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx
......
src/py/py_loader.cpp
View file @ dcb98a60
......@@ -35,9 +35,9 @@ static std::vector<fs::path> find_available_python_versions()
auto path = dynamic_loader::path(&load_py).parent_path();
for(const auto& entry : fs::directory_iterator{path})
{
if(not entry.is_regular_file())
continue;
auto p = entry.path();
if(not fs::is_regular_file(p))
continue;
if(not contains(p.stem().string(), "migraphx_py_"))
continue;
result.push_back(p);
......@@ -64,7 +64,7 @@ static dynamic_loader py_lib()
return lib;
}
program load_py(const std::string& filename)
MIGRAPHX_PY_EXPORT program load_py(const std::string& filename)
{
static auto f = py_lib().get_function<program(const std::string&)>("migraphx_load_py");
return f(filename);
......
src/rewrite_quantization.cpp
View file @ dcb98a60
......@@ -28,6 +28,7 @@
#include <migraphx/tune_axis.hpp>
#include <migraphx/program.hpp>
#include <migraphx/shape.hpp>
#include <migraphx/common.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -61,13 +62,10 @@ void apply_quantizelinear(module& m, instruction_ref ins)
max_quant = qt.max();
min_quant = qt.min();
});
auto s = add_zero_point->get_shape();
std::vector<int> min_data(s.elements(), min_quant);
std::vector<int> max_data(s.elements(), max_quant);
auto min_arg = m.add_literal(literal(s, min_data));
auto max_arg = m.add_literal(literal(s, max_data));
auto saturate = m.insert_instruction(ins, make_op("clip"), add_zero_point, min_arg, max_arg);
auto s = add_zero_point->get_shape();
auto min_arg = m.add_literal(literal{shape{s.type()}, {min_quant}});
auto max_arg = m.add_literal(literal{shape{s.type()}, {max_quant}});
auto saturate = insert_common_op(m, ins, make_op("clip"), {add_zero_point, min_arg, max_arg});
m.replace_instruction(
ins, make_op("convert", {{"target_type", ins->get_shape().type()}}), saturate);
}
......
src/simplify_algebra.cpp
View file @ dcb98a60
......@@ -1095,8 +1095,9 @@ MIGRAPHX_PRED_MATCHER(horiz_conv_dot, instruction_ref ins)
};
};
auto dots = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("dot"));
auto qdots = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("quant_dot"));
auto convs = std::count_if(ins->outputs().begin(), ins->outputs().end(), pred("convolution"));
return (dots >= 2 or convs >= 2);
return (dots >= 2 or convs >= 2 or qdots >= 2);
}
struct find_conv_dot_horiz_fusion
......@@ -1110,7 +1111,7 @@ struct find_conv_dot_horiz_fusion
auto pred = [](auto i, auto j) {
if(i->get_operator() != j->get_operator())
return false;
if(not contains({"dot", "convolution"}, i->name()))
if(not contains({"quant_dot", "dot", "convolution"}, i->name()))
return true;
auto x = i->inputs()[1]->get_shape().lens();
auto y = j->inputs()[1]->get_shape().lens();
......@@ -1118,7 +1119,7 @@ struct find_conv_dot_horiz_fusion
return false;
// Check that non-axes match
int axis = 1;
if(i->name() == "dot")
if(i->name() == "dot" or i->name() == "quant_dot")
{
axis = x.size() - 1;
}
......@@ -1129,7 +1130,7 @@ struct find_conv_dot_horiz_fusion
if(std::distance(start, last) < 2)
return;
auto&& name = (*start)->name();
if(not contains({"dot", "convolution"}, name))
if(not contains({"quant_dot", "dot", "convolution"}, name))
return;
auto op = (*start)->get_operator();
int group = 1;
......@@ -1144,7 +1145,7 @@ struct find_conv_dot_horiz_fusion
start, last, std::back_inserter(args), [&](auto x) { return x->inputs().at(1); });
int axis = 1;
int concat_axis = 0;
if(name == "dot")
if(name == "dot" or name == "quant_dot")
{
axis = int(args.front()->get_shape().lens().size() - 1);
concat_axis = axis;
......
src/sqlite.cpp
View file @ dcb98a60
......@@ -48,6 +48,7 @@ struct sqlite_impl
template <class F>
void exec(const char* sql, F f)
{
// cppcheck-suppress constParameterPointer
auto callback = [](void* obj, auto... xs) -> int {
try
{
......
src/targets/cpu/gemm.cpp
View file @ dcb98a60
......@@ -43,7 +43,11 @@ struct dnnl_gemm : dnnl_extend_op<dnnl_gemm, dnnl::matmul, op::dot>
MIGRAPHX_DNNL_PREFIX(ARG_BIAS)};
}
void required(const check_shapes& cs) const { cs.not_broadcasted(); }
template <class T>
void required(const check_shapes<T>& cs) const
{
cs.not_broadcasted();
}
dnnl::matmul::desc get_desc(const std::unordered_map<int, dnnl::memory::desc>& m) const
{
......
src/targets/cpu/include/migraphx/cpu/dnnl.hpp
View file @ dcb98a60
......@@ -400,7 +400,11 @@ struct dnnl_extend_op : dnnl_op<Derived, Primitive>
}
// dnnl has some issues with non-packed inputs
void required(const check_shapes& cs) const { cs.packed_or_broadcasted(); }
template <class T>
void required(const check_shapes<T>& cs) const
{
cs.packed_or_broadcasted();
}
std::string name() const { return "dnnl::" + op.name(); }
shape compute_shape(std::vector<shape> inputs) const
......
src/targets/cpu/target.cpp
View file @ dcb98a60
......@@ -61,7 +61,7 @@ namespace cpu {
std::string target::name() const { return "cpu"; }
// cppcheck-suppress constParameter
// cppcheck-suppress constParameterReference
std::vector<pass> target::get_passes(migraphx::context& gctx, const compile_options&) const
{
auto& ctx = any_cast<context>(gctx);
......
src/targets/gpu/CMakeLists.txt
View file @ dcb98a60
......@@ -123,6 +123,7 @@ add_library(migraphx_gpu
lrn.cpp
mlir.cpp
multinomial.cpp
no_device.cpp
nonzero.cpp
pack_args.cpp
pack_int8_args.cpp
......
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