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Commit e8deff52 authored by mei-ye's avatar mei-ye
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resolve merge conflicts

parents edfee372 6c649c7b
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Showing with 802 additions and 165 deletions
src/include/migraph/verify.hpp
View file @ e8deff52
......@@ -140,7 +140,7 @@ std::size_t mismatch_diff(R1&& r1, R2&& r2, T diff)
{
return mismatch_idx(r1, r2, [&](auto x, auto y) {
auto d = abs_diff(x, y);
return !(d > diff && d < diff);
return float_equal(d, diff);
});
}
......
src/include/migraph/verify_args.hpp
View file @ e8deff52
......@@ -6,14 +6,16 @@
namespace migraph {
inline void verify_args(const std::string& name,
inline bool verify_args(const std::string& name,
const argument& cpu_arg,
const argument& gpu_arg,
double tolerance = 80)
{
bool passed = true;
visit_all(cpu_arg, gpu_arg)([&](auto cpu, auto gpu) {
double error;
if(not verify_range(cpu, gpu, tolerance, &error))
passed = verify_range(cpu, gpu, tolerance, &error);
if(not passed)
{
// TODO: Check for nans
std::cout << "FAILED: " << name << std::endl;
......@@ -27,6 +29,9 @@ inline void verify_args(const std::string& name,
if(range_zero(gpu))
std::cout << "Gpu data is all zeros" << std::endl;
auto mxdiff = max_diff(cpu, gpu);
std::cout << "Max diff: " << mxdiff << std::endl;
auto idx = mismatch_idx(cpu, gpu, float_equal);
if(idx < range_distance(cpu))
{
......@@ -45,7 +50,36 @@ inline void verify_args(const std::string& name,
<< gpu[gpu_nan_idx] << std::endl;
std::cout << std::endl;
}
else
{
if(range_zero(cpu))
std::cout << "Cpu data is all zeros" << std::endl;
if(range_zero(gpu))
std::cout << "Gpu data is all zeros" << std::endl;
// auto mxdiff = max_diff(cpu, gpu);
// std::cout << "Max diff: " << mxdiff << std::endl;
// auto idx = mismatch_idx(cpu, gpu, float_equal);
// if(idx < range_distance(cpu))
// {
// std::cout << "Mismatch at " << idx << ": " << cpu[idx] << " != " << gpu[idx]
// << std::endl;
// }
auto cpu_nan_idx = find_idx(cpu, not_finite);
if(cpu_nan_idx >= 0)
std::cout << "Non finite number found in cpu at " << cpu_nan_idx << ": "
<< cpu[cpu_nan_idx] << std::endl;
auto gpu_nan_idx = find_idx(gpu, not_finite);
if(gpu_nan_idx >= 0)
std::cout << "Non finite number found in gpu at " << gpu_nan_idx << ": "
<< gpu[gpu_nan_idx] << std::endl;
// std::cout << std::endl;
}
});
return passed;
}
} // namespace migraph
......
src/instruction.cpp 0 → 100644
View file @ e8deff52
#include <migraph/instruction.hpp>
#include <migraph/builtin.hpp>
#include <migraph/erase.hpp>
namespace migraph {
instruction::instruction(operation o, shape r, std::vector<instruction_ref> args)
: op(std::move(o)), result(std::move(r)), arguments(std::move(args))
{
}
instruction::instruction(literal l)
: op(builtin::literal{}), result(l.get_shape()), lit(std::move(l))
{
}
void instruction::replace(const shape& r)
{
if(r != result)
{
result = r;
for(auto&& ins : output)
{
assert(ins->name().front() != '@');
ins->recompute_shape();
}
}
}
void instruction::recompute_shape() { replace(compute_shape(op, arguments)); }
void instruction::clear_arguments()
{
for(auto&& arg : arguments)
{
arg->remove_output(*this);
}
arguments.clear();
}
bool operator==(const instruction& i, instruction_ref ref)
{
return std::addressof(i) == std::addressof(*ref);
}
bool instruction::valid(instruction_ref start) const
{
return valid() && std::all_of(arguments.begin(), arguments.end(), [&](instruction_ref i) {
auto self = std::find(i->outputs().begin(), i->outputs().end(), *this);
return self != i->outputs().end() &&
std::distance(start, i) < std::distance(start, *self);
});
}
bool instruction::valid() const
{
shape computed;
if(op.name() == "@literal")
{
computed = lit.get_shape();
}
else if(op.name() == "@param")
{
computed = result;
}
else
{
try
{
computed = compute_shape(op, arguments);
}
catch(migraph::exception&)
{
return false;
}
}
return result == computed && std::all_of(output.begin(), output.end(), [&](instruction_ref i) {
return std::find(i->inputs().begin(), i->inputs().end(), *this) != i->inputs().end();
});
}
shape instruction::get_shape() const { return result; }
const literal& instruction::get_literal() const
{
assert(op.name() == "@literal");
return lit;
}
const operation& instruction::get_operator() const { return op; }
std::string instruction::name() const { return op.name(); }
const std::vector<instruction_ref>& instruction::inputs() const { return arguments; }
const std::vector<instruction_ref>& instruction::outputs() const { return output; }
bool operator==(instruction_ref ref, const instruction& i) { return i == ref; }
bool operator!=(const instruction& i, instruction_ref ref) { return !(i == ref); }
bool operator!=(instruction_ref ref, const instruction& i) { return !(i == ref); }
void instruction::add_output(instruction_ref ins)
{
if(std::find(output.begin(), output.end(), ins) == output.end())
output.push_back(ins);
}
template <class T>
void instruction::remove_output(const T& ins)
{
migraph::erase(output, ins);
}
void instruction::backreference(instruction_ref ref)
{
for(auto&& arg : ref->inputs())
arg->add_output(ref);
}
void instruction::replace_argument(instruction_ref ins,
instruction_ref old,
instruction_ref new_ins)
{
ins->replace_argument(old, new_ins);
backreference(ins);
ins->recompute_shape();
}
void instruction::replace(instruction_ref ins,
operation o,
const shape& r,
std::vector<instruction_ref> args)
{
ins->replace(std::move(o), r, std::move(args));
backreference(ins);
}
void instruction::replace(operation o, const shape& r, std::vector<instruction_ref> args)
{
op = std::move(o);
replace(r);
replace(std::move(args));
}
void instruction::replace(std::vector<instruction_ref> args)
{
clear_arguments();
arguments = std::move(args);
}
void instruction::replace_argument(instruction_ref old, instruction_ref new_ins)
{
std::replace(arguments.begin(), arguments.end(), old, new_ins);
old->remove_output(*this);
}
shape compute_shape(const operation& op, const std::vector<instruction_ref>& args)
{
std::vector<shape> shapes(args.size());
std::transform(
args.begin(), args.end(), shapes.begin(), [](instruction_ref i) { return i->get_shape(); });
return op.compute_shape(shapes);
}
} // namespace migraph
src/onnx/onnx.cpp
View file @ e8deff52
......@@ -28,10 +28,6 @@ struct unknown
else
return input.front();
}
argument compute(context&, const shape&, const std::vector<argument>&) const
{
MIGRAPH_THROW("not computable");
}
friend std::ostream& operator<<(std::ostream& os, const unknown& x)
{
os << x.name();
......@@ -58,6 +54,7 @@ struct onnx_parser
add_generic_op("Mul", mul{});
add_generic_op("Relu", activation{"relu"});
add_generic_op("Sub", sub{});
add_generic_op("Sum", add{});
add_mem_op("Constant", &onnx_parser::parse_constant);
add_mem_op("Conv", &onnx_parser::parse_conv);
......@@ -67,6 +64,7 @@ struct onnx_parser
add_mem_op("Flatten", &onnx_parser::parse_flatten);
add_mem_op("Gemm", &onnx_parser::parse_gemm);
add_mem_op("BatchNormalization", &onnx_parser::parse_batchnorm);
add_mem_op("Softmax", &onnx_parser::parse_softmax);
}
template <class F>
......@@ -103,6 +101,15 @@ struct onnx_parser
});
}
instruction_ref
parse_softmax(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
{
auto dims = args.front()->get_shape().lens();
auto r = prog.add_instruction(reshape{{long(dims[0]), long(dims[1]), 1, 1}}, args.front());
auto s = prog.add_instruction(softmax{}, r);
return prog.add_instruction(reshape{{long(dims[0]), long(dims[1])}}, s);
}
instruction_ref
parse_conv(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
{
......@@ -160,7 +167,7 @@ struct onnx_parser
}
if(args.size() == 2)
{
literal s = args[1]->lit;
literal s = args[1]->get_literal();
s.visit([&](auto v) { copy(v, std::back_inserter(op.dims)); });
}
return prog.add_instruction(op, args[0]);
......@@ -344,11 +351,10 @@ struct onnx_parser
if(node.name().empty())
{
std::string generated = "migraph_unnamed_node";
for(auto&& output : node.output())
{
generated += "_" + output;
}
return generated;
return std::accumulate(node.output().begin(),
node.output().end(),
generated,
[](auto x, auto y) { return x + "_" + y; });
}
return node.name();
}
......@@ -481,11 +487,11 @@ struct onnx_parser
break; // throw std::runtime_error("Unsupported type COMPLEX128");
}
std::vector<std::size_t> dims;
// TODO: USe std::transform
for(auto&& d : t.tensor_type().shape().dim())
{
dims.push_back(d.dim_value());
}
auto&& tensor_dims = t.tensor_type().shape().dim();
std::transform(tensor_dims.begin(),
tensor_dims.end(),
std::back_inserter(dims),
[](auto&& d) { return d.dim_value(); });
return {shape_type, dims};
}
};
......
src/onnx/verify_onnx.cpp
View file @ e8deff52
......@@ -51,48 +51,76 @@ void verify_program(const std::string& name, F f, double tolerance = 100)
auto x = run_cpu(f);
auto y = run_gpu(f);
migraph::verify_args(name, x, y, tolerance);
// std::cout << "cpu: " << x << std::endl;
// std::cout << "gpu: " << y << std::endl;
}
void verify_instructions(const migraph::program& prog, double tolerance = 80)
{
for(auto&& ins : prog)
{
if(ins.op.name().front() == '@')
if(ins.name().front() == '@')
continue;
if(ins.op.name() == "broadcast")
if(ins.name() == "broadcast")
continue;
if(ins.op.name() == "transpose")
if(ins.name() == "transpose")
continue;
if(ins.op.name() == "reshape")
if(ins.name() == "reshape")
continue;
auto create_program = [&] {
migraph::program p;
std::vector<migraph::instruction_ref> inputs;
for(auto&& arg : ins.arguments)
for(auto&& arg : ins.inputs())
{
if(arg->op.name() == "@literal")
inputs.push_back(p.add_literal(arg->lit));
if(arg->name() == "@literal")
inputs.push_back(p.add_literal(arg->get_literal()));
else
inputs.push_back(
p.add_parameter(std::to_string(inputs.size()), arg->get_shape()));
}
p.add_instruction(ins.op, inputs);
p.add_instruction(ins.get_operator(), inputs);
return p;
};
try
{
std::cout << "Verify: " << ins.op.name() << std::endl;
std::cout << "Verify: " << ins.name() << std::endl;
std::cout << create_program() << std::endl;
verify_program(ins.op.name(), create_program, tolerance);
verify_program(ins.name(), create_program, tolerance);
}
catch(...)
{
std::cout << "Instruction " << ins.op.name() << " threw an exception." << std::endl;
std::cout << "Instruction " << ins.name() << " threw an exception." << std::endl;
throw;
}
}
}
template <class F>
void verify_reduced(F f, int n, double tolerance = 80)
{
auto create_program = [&] {
migraph::program p = f();
auto last = std::prev(p.end(), n + 1);
p.remove_instructions(last, p.end());
return p;
};
std::cout << "Verify: " << std::endl;
std::cout << create_program() << std::endl;
verify_program(std::to_string(n), create_program, tolerance);
}
template <class F>
void verify_reduced_program(F f, double tolerance = 80)
{
migraph::program p = f();
auto n = std::distance(p.begin(), p.end());
for(int i = 0; i < n; i++)
{
verify_reduced(f, i, tolerance);
}
}
int main(int argc, char const* argv[])
{
std::vector<std::string> args(argv + 1, argv + argc);
......@@ -106,6 +134,10 @@ int main(int argc, char const* argv[])
{
verify_instructions(p);
}
else if(std::any_of(args.begin(), args.end(), [](const auto& s) { return s == "-r"; }))
{
verify_reduced_program([&] { return migraph::parse_onnx(file); });
}
else
{
verify_program(file, [&] { return migraph::parse_onnx(file); });
......
src/opt/memory_coloring_impl.cpp
View file @ e8deff52
......@@ -115,13 +115,13 @@ void memory_coloring_impl::build()
if(is_allocate(iter) || is_lit)
{
live_range& range = def_interval->segment;
def_interval->result = iter->result;
def_interval->result = iter->get_shape();
def_interval->is_literal = is_lit;
if(!is_lit || unify_literals)
alloc_queue.push(def_interval);
range.begin = cur_points;
def_interval->def_point = cur_points;
range.size = (iter->result).bytes();
range.size = (iter->get_shape()).bytes();
live_set.erase(range.vn);
}
}
......@@ -131,7 +131,7 @@ void memory_coloring_impl::build()
}
int tie_ndx = get_input_tie_ndx(iter);
int cnt = -1;
for(auto&& arg : iter->arguments)
for(auto&& arg : iter->inputs())
{
cnt++;
if(is_param(arg) || is_outline(arg))
......
src/opt/memory_coloring_impl.hpp
View file @ e8deff52
......@@ -79,23 +79,23 @@ struct memory_coloring_impl
void rewrite();
private:
static bool is_param(const instruction_ref ins) { return ins->op.name() == "@param"; }
static bool is_param(const instruction_ref ins) { return ins->name() == "@param"; }
static bool is_output_param(const instruction_ref ins)
{
return is_param(ins) && any_cast<builtin::param>(ins->op).parameter == "output";
return is_param(ins) && any_cast<builtin::param>(ins->get_operator()).parameter == "output";
}
bool is_allocate(const instruction_ref ins) { return ins->op.name() == allocation_op; }
static bool is_outline(const instruction_ref ins) { return ins->op.name() == "@outline"; }
static bool is_literal(const instruction_ref ins) { return ins->op.name() == "@literal"; }
bool is_allocate(const instruction_ref ins) { return ins->name() == allocation_op; }
static bool is_outline(const instruction_ref ins) { return ins->name() == "@outline"; }
static bool is_literal(const instruction_ref ins) { return ins->name() == "@literal"; }
static bool is_check_context(const instruction_ref ins)
{
return ins->op.name() == "check_context";
return ins->name() == "check_context";
}
// get operand alias info. This is a temporary workaround.
int get_input_tie_ndx(const instruction_ref ins)
{
std::string name = ins->op.name();
std::string name = ins->name();
if(operand_alias.find(name) != operand_alias.end())
return operand_alias[name];
if(is_allocate(ins))
......@@ -106,7 +106,7 @@ struct memory_coloring_impl
}
int cnt = -1;
int last_allocate = -1;
for(auto&& arg : ins->arguments)
for(auto&& arg : ins->inputs())
{
cnt++;
if(is_allocate(arg) || is_output_param(arg))
......
src/program.cpp
View file @ e8deff52
......@@ -12,6 +12,7 @@
namespace migraph {
MIGRAPH_DECLARE_ENV_VAR(MIGRAPH_TRACE_COMPILE)
MIGRAPH_DECLARE_ENV_VAR(MIGRAPH_TRACE_EVAL)
struct program_impl
{
......@@ -20,7 +21,7 @@ struct program_impl
context ctx;
};
const operation& get_operation(instruction_ref ins) { return ins->op; }
const operation& get_operation(instruction_ref ins) { return ins->get_operator(); }
template <class F>
static void print_program(std::ostream& os, const program& p, F annonate)
......@@ -31,27 +32,27 @@ static void print_program(std::ostream& os, const program& p, F annonate)
for(auto ins : iterator_for(p))
{
std::string var_name = "@" + std::to_string(count);
if(ins->op.name() == "@param")
if(ins->name() == "@param")
{
var_name = any_cast<builtin::param>(ins->op).parameter;
var_name = any_cast<builtin::param>(ins->get_operator()).parameter;
}
os << var_name << " = ";
os << ins->op;
os << ins->get_operator();
if(ins->op.name() == "@literal")
if(ins->name() == "@literal")
{
if(ins->lit.get_shape().elements() > 10)
if(ins->get_literal().get_shape().elements() > 10)
os << "{ ... }";
else
os << "{" << ins->lit << "}";
os << "{" << ins->get_literal() << "}";
}
if(!ins->arguments.empty())
if(!ins->inputs().empty())
{
char delim = '(';
for(auto&& arg : ins->arguments)
for(auto&& arg : ins->inputs())
{
assert(p.has_instruction(arg) && "Instruction not found");
os << delim << names.at(arg);
......@@ -60,7 +61,7 @@ static void print_program(std::ostream& os, const program& p, F annonate)
os << ")";
}
os << " -> " << ins->result;
os << " -> " << ins->get_shape();
annonate(ins, names);
......@@ -92,8 +93,8 @@ instruction_ref program::insert_instruction(instruction_ref ins,
// TODO: Use move
shape r = compute_shape(op, args);
auto result = impl->instructions.insert(ins, {op, r, std::move(args)});
backreference(result);
// assert(result->arguments == args);
instruction::backreference(result);
// assert(result->inputs() == args);
assert(result->valid(begin()));
return result;
}
......@@ -108,8 +109,7 @@ instruction_ref program::replace_instruction(instruction_ref ins,
assert(not starts_with(op.name(), "@"));
shape r = compute_shape(op, args);
ins->replace(op, r, std::move(args));
backreference(ins);
instruction::replace(ins, op, r, std::move(args));
assert(ins->valid(begin()));
return ins;
}
......@@ -120,21 +120,21 @@ instruction_ref program::replace_instruction(instruction_ref ins, instruction_re
assert(has_instruction(rep));
assert(ins != rep);
// TODO: Should it be an error if the output is empty?
if(ins->output.empty())
if(ins->outputs().empty())
{
return rep;
}
for(auto&& out : ins->output)
for(auto&& out : ins->outputs())
{
// TODO: Check for possible cycles
if(out != rep)
{
replace_argument(out, ins, rep);
instruction::replace_argument(out, ins, rep);
}
assert(out->valid(begin()));
}
// Replacement should not be dead code unless its the last instruction
assert(!rep->output.empty() or rep == std::prev(end()));
assert(!rep->outputs().empty() or rep == std::prev(end()));
assert(ins->valid(begin()));
assert(rep->valid(begin()));
return rep;
......@@ -143,7 +143,7 @@ instruction_ref program::replace_instruction(instruction_ref ins, instruction_re
instruction_ref program::remove_instruction(instruction_ref ins)
{
assert(has_instruction(ins));
assert(ins->output.empty());
assert(ins->outputs().empty());
ins->clear_arguments();
return impl->instructions.erase(ins);
}
......@@ -155,7 +155,7 @@ instruction_ref program::remove_instructions(instruction_ref first, instruction_
// TODO: Check every element
assert(has_instruction(first));
std::for_each(first, last, [&](instruction& ins) { ins.clear_arguments(); });
assert(std::all_of(first, last, [&](instruction& ins) { return ins.output.empty(); }));
assert(std::all_of(first, last, [&](instruction& ins) { return ins.outputs().empty(); }));
return impl->instructions.erase(first, last);
}
......@@ -188,9 +188,9 @@ shape program::get_parameter_shape(std::string name) const
{
auto ins = std::find_if(
impl->instructions.begin(), impl->instructions.end(), [&](const instruction& x) {
if(x.op.name() == "@param")
if(x.name() == "@param")
{
return any_cast<builtin::param>(x.op).parameter == name;
return any_cast<builtin::param>(x.get_operator()).parameter == name;
}
else
{
......@@ -198,7 +198,7 @@ shape program::get_parameter_shape(std::string name) const
}
});
if(ins != this->end())
return ins->result;
return ins->get_shape();
else
return {};
}
......@@ -207,9 +207,9 @@ instruction_ref program::get_parameter(std::string name) const
{
auto ins = std::find_if(
impl->instructions.begin(), impl->instructions.end(), [&](const instruction& x) {
if(x.op.name() == "@param")
if(x.name() == "@param")
{
return any_cast<builtin::param>(x.op).parameter == name;
return any_cast<builtin::param>(x.get_operator()).parameter == name;
}
else
{
......@@ -227,10 +227,10 @@ std::unordered_map<std::string, shape> program::get_parameter_shapes() const
std::unordered_map<std::string, shape> result;
for(auto&& ins : impl->instructions)
{
if(ins.op.name() == "@param")
if(ins.name() == "@param")
{
auto&& name = any_cast<builtin::param>(ins.op).parameter;
result[name] = ins.result;
auto&& name = any_cast<builtin::param>(ins.get_operator()).parameter;
result[name] = ins.get_shape();
}
}
return result;
......@@ -248,7 +248,7 @@ std::size_t program::size() const { return impl->instructions.size(); }
instruction_ref program::begin() const { return impl->instructions.begin(); }
instruction_ref program::end() const { return impl->instructions.end(); }
shape program::get_shape() const { return impl->instructions.back().result; }
shape program::get_shape() const { return impl->instructions.back().get_shape(); }
instruction_ref program::validate() const
{
......@@ -277,7 +277,7 @@ void program::compile(const target& t, tracer trace)
{
auto index = std::distance(impl->instructions.begin(), invalid);
MIGRAPH_THROW(p.name() + " pass produces invalid program at instruction " +
std::to_string(index) + ": " + invalid->op.name());
std::to_string(index) + ": " + invalid->name());
}
trace();
#endif
......@@ -303,32 +303,32 @@ argument generic_eval(const program& p,
values.reserve(16);
for(auto ins : iterator_for(p))
{
if(ins->op.name() == "@literal")
if(ins->name() == "@literal")
{
results.emplace(ins, trace(ins, [&] { return ins->lit.get_argument(); }));
results.emplace(ins, trace(ins, [&] { return ins->get_literal().get_argument(); }));
}
else if(ins->op.name() == "@param")
else if(ins->name() == "@param")
{
results.emplace(ins, trace(ins, [&] {
return params.at(any_cast<builtin::param>(ins->op).parameter);
return params.at(
any_cast<builtin::param>(ins->get_operator()).parameter);
}));
}
else if(ins->op.name() == "@outline")
else if(ins->name() == "@outline")
{
results.emplace(ins, trace(ins, [&] { return argument{ins->result, nullptr}; }));
results.emplace(ins, trace(ins, [&] { return argument{ins->get_shape(), nullptr}; }));
}
else
{
values.resize(ins->arguments.size());
std::transform(ins->arguments.begin(),
ins->arguments.end(),
values.begin(),
[&](instruction_ref i) {
assert(results.find(i) != results.end());
return results[i];
});
results.emplace(ins,
trace(ins, [&] { return ins->op.compute(ctx, ins->result, values); }));
values.resize(ins->inputs().size());
std::transform(
ins->inputs().begin(), ins->inputs().end(), values.begin(), [&](instruction_ref i) {
assert(results.find(i) != results.end());
return results[i];
});
results.emplace(ins, trace(ins, [&] {
return ins->get_operator().compute(ctx, ins->get_shape(), values);
}));
}
assert(results.find(ins) != results.end());
}
......@@ -337,8 +337,20 @@ argument generic_eval(const program& p,
argument program::eval(std::unordered_map<std::string, argument> params) const
{
return generic_eval(
*this, this->impl->ctx, std::move(params), [](auto&, auto f) { return f(); });
if(enabled(MIGRAPH_TRACE_EVAL{}))
{
auto& ctx = this->impl->ctx;
return generic_eval(*this, this->impl->ctx, std::move(params), [&](auto& ins, auto f) {
ctx.finish();
std::cout << "Run instruction: " << ins->name() << std::endl;
return f();
});
}
else
{
return generic_eval(
*this, this->impl->ctx, std::move(params), [](auto&, auto f) { return f(); });
}
}
double common_average(const std::vector<double>& v)
......@@ -404,7 +416,7 @@ void program::perf_report(std::ostream& os, std::size_t n, parameter_map params)
for(auto&& p : ins_vec)
{
double avg = common_average(p.second);
op_times[p.first->op.name()] += avg;
op_times[p.first->name()] += avg;
total_instruction_time += avg;
}
double calculate_overhead_time = total_time - total_instruction_time;
......
src/simplify_reshapes.cpp
View file @ e8deff52
......@@ -25,26 +25,26 @@ void simplify_reshapes::apply(program& p) const
{
for(auto ins : iterator_for(p))
{
if(not is_reshaper(ins->op.name()))
if(not is_reshaper(ins->name()))
continue;
if(ins->output.size() != 1)
if(ins->outputs().size() != 1)
continue;
if(is_reshaper(ins->output.front()->op.name()))
if(is_reshaper(ins->outputs().front()->name()))
continue;
// Gather reshapes
std::vector<instruction_ref> reshapes{ins};
while(is_reshaper(reshapes.back()->op.name()))
while(is_reshaper(reshapes.back()->name()))
{
assert(!reshapes.back()->arguments.empty());
assert(p.has_instruction(reshapes.back()->arguments.front()));
reshapes.push_back(reshapes.back()->arguments.front());
assert(!reshapes.back()->inputs().empty());
assert(p.has_instruction(reshapes.back()->inputs().front()));
reshapes.push_back(reshapes.back()->inputs().front());
}
std::pair<instruction_ref, instruction_ref> r{p.end(), p.end()};
for(auto start : iterator_for(reshapes))
{
auto last = std::find_if(reshapes.rbegin(), reshapes.rend(), [&](auto&& i) {
return i->result == (*start)->result and i != (*start);
return i->get_shape() == (*start)->get_shape() and i != (*start);
});
if(last != reshapes.rend())
{
......
src/targets/cpu/cpu_lowering.cpp
View file @ e8deff52
......@@ -134,6 +134,63 @@ struct cpu_convolution
}
};
struct cpu_im2col
{
im2col op;
static std::string name() { return "cpu::im2col"; }
shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
argument compute(context&, const shape& output_shape, std::vector<argument> args) const
{
argument result{output_shape};
auto input_shape = args[0].get_shape();
auto weights_shape = args[1].get_shape();
visit_all(result, args[0])([&](auto col, auto input) {
const std::size_t& height = input_shape.lens()[2];
const std::size_t& width = input_shape.lens()[3];
const std::size_t& channels = weights_shape.lens()[1];
const std::size_t& kernel_h = weights_shape.lens()[2];
const std::size_t& kernel_w = weights_shape.lens()[3];
const std::size_t& pad_h = op.padding[0];
const std::size_t& pad_w = op.padding[1];
const std::size_t& stride_h = op.stride[0];
const std::size_t& stride_w = op.stride[1];
int kdiv2_h, kdiv2_w;
kdiv2_h = kernel_h / 2;
kdiv2_w = kernel_w / 2;
// calculate output sizes
const std::size_t col_height = (height - kernel_h + 2 * pad_h) / stride_h + 1;
const std::size_t col_width = (width - kernel_w + 2 * pad_w) / stride_w + 1;
// account for padding for the starting position of the input pixels
std::size_t iinput = kdiv2_h - pad_h;
// loop over output pixels (ioutput, joutput)
for(std::size_t ioutput = 0; ioutput < col_height; ioutput++, iinput += stride_h)
{
std::size_t jinput = kdiv2_w - pad_w;
for(std::size_t joutput = 0; joutput < col_width; joutput++, jinput += stride_w)
{
// compute linear index for output
std::size_t ldx = ioutput * col_width + joutput;
std::size_t p = 0;
dfor(channels,
kernel_h,
kernel_w)([&](std::size_t c, std::size_t koffset, std::size_t loffset) {
int idx = iinput + koffset - kdiv2_h;
int jdx = jinput + loffset - kdiv2_w;
col(ldx, p) = ((idx >= 0) && (idx < height) && (jdx >= 0) && (jdx < width))
? input(0, c, idx, jdx)
: 0;
p++;
});
}
}
});
return result;
}
};
struct max_pool
{
static std::string name() { return "max"; }
......@@ -494,6 +551,7 @@ struct cpu_apply
void init()
{
apply_map["im2col"] = extend_op<cpu_im2col, im2col>();
apply_map["convolution"] = extend_op<cpu_convolution, convolution>();
apply_map["gemm"] = extend_op<cpu_gemm, gemm>();
apply_map["batch_norm_inference"] =
......@@ -521,17 +579,17 @@ struct cpu_apply
init();
for(auto it : iterator_for(*prog))
{
if(it->op.name() == "activation")
if(it->name() == "activation")
{
apply_activation(it);
}
else if(it->op.name() == "pooling")
else if(it->name() == "pooling")
{
apply_pooling(it);
}
else if(apply_map.count(it->op.name()) > 0)
else if(apply_map.count(it->name()) > 0)
{
apply_map.at(it->op.name())(it);
apply_map.at(it->name())(it);
}
}
}
......@@ -539,30 +597,30 @@ struct cpu_apply
template <class T>
void apply_simple_op(instruction_ref ins)
{
prog->replace_instruction(ins, T{}, ins->arguments);
prog->replace_instruction(ins, T{}, ins->inputs());
}
template <class T, class Op>
void apply_extend_op(instruction_ref ins)
{
auto&& op = any_cast<Op>(ins->op);
prog->replace_instruction(ins, T{op}, ins->arguments);
auto&& op = any_cast<Op>(ins->get_operator());
prog->replace_instruction(ins, T{op}, ins->inputs());
}
void apply_activation(instruction_ref ins)
{
auto&& op = any_cast<activation>(ins->op);
auto&& op = any_cast<activation>(ins->get_operator());
if(op.mode == "relu")
prog->replace_instruction(ins, cpu_unary<relu_op>{}, ins->arguments);
prog->replace_instruction(ins, cpu_unary<relu_op>{}, ins->inputs());
}
void apply_pooling(instruction_ref ins)
{
auto&& op = any_cast<pooling>(ins->op);
auto&& op = any_cast<pooling>(ins->get_operator());
if(op.mode == "max")
prog->replace_instruction(ins, cpu_pooling<max_pool>{op}, ins->arguments);
prog->replace_instruction(ins, cpu_pooling<max_pool>{op}, ins->inputs());
else if(op.mode == "average")
prog->replace_instruction(ins, cpu_pooling<avg_pool>{op}, ins->arguments);
prog->replace_instruction(ins, cpu_pooling<avg_pool>{op}, ins->inputs());
}
};
......
src/targets/gpu/eliminate_workspace.cpp
View file @ e8deff52
......@@ -20,11 +20,11 @@ void eliminate_workspace::apply(program& p) const
std::vector<instruction_ref> allocs;
for(auto ins : iterator_for(p))
{
if(ins->output.size() != 1)
if(ins->outputs().size() != 1)
continue;
if(ins->op.name() != "hip::allocate")
if(ins->name() != "hip::allocate")
continue;
auto&& a = any_cast<hip_allocate>(ins->op);
auto&& a = any_cast<hip_allocate>(ins->get_operator());
if(a.tag == "workspace")
{
n = std::max(n, ins->get_shape().bytes());
......
src/targets/gpu/fuse_ops.cpp
View file @ e8deff52
......@@ -26,14 +26,14 @@ void fuse_ops::apply(program& p) const
{
for(auto ins : iterator_for(p))
{
if(ins->op.name() != "gpu::relu")
if(ins->name() != "gpu::relu")
continue;
auto add_ins = ins->arguments.front();
if(add_ins->op.name() != "gpu::add")
auto add_ins = ins->inputs().front();
if(add_ins->name() != "gpu::add")
continue;
auto args = add_ins->arguments;
auto args = add_ins->inputs();
// Use the allocation from the relu operator
args.back() = ins->arguments.back();
args.back() = ins->inputs().back();
p.replace_instruction(ins, hip_add_relu{}, args);
}
}
......
src/targets/gpu/lowering.cpp
View file @ e8deff52
......@@ -132,8 +132,16 @@ struct miopen_convolution
workspace_size,
false);
algo = perf.fwd_algo;
return algo == miopenConvolutionFwdAlgoWinograd ? shape{shape::int8_type, {0}}
: workspace_shape;
return shape{shape::int8_type, {perf.memory}};
}
friend std::ostream& operator<<(std::ostream& os, const miopen_convolution& self)
{
os << self.name() << "[";
os << self.op << ", ";
os << "algo=" << self.algo;
os << "]";
return os;
}
};
......@@ -308,6 +316,34 @@ struct miopen_relu
}
};
struct miopen_softmax
{
softmax op;
std::string name() const { return "gpu::softmax"; }
shape compute_shape(const std::vector<shape>& inputs) const
{
check_shapes{inputs, *this}.has(2).standard();
return op.compute_shape({inputs.at(0)});
}
argument
compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const
{
float alpha = 1, beta = 0;
auto x_desc = make_tensor(args[0].get_shape());
auto y_desc = make_tensor(output_shape);
miopenSoftmaxForward(ctx.handle.get(),
&alpha,
x_desc.get(),
args[0].implicit(),
&beta,
y_desc.get(),
args[1].implicit());
return args[1];
}
};
struct miopen_apply
{
program* prog = nullptr;
......@@ -325,34 +361,38 @@ struct miopen_apply
for(auto it = prog->begin(); it != prog->end(); it++)
{
auto s = it->get_shape();
if(it->op.name() == "convolution")
if(it->name() == "convolution")
{
check_shape(s, apply_convolution(it));
}
else if(it->op.name() == "activation")
else if(it->name() == "activation")
{
check_shape(s, apply_activation(it));
}
else if(it->op.name() == "pooling")
else if(it->name() == "pooling")
{
check_shape(s, apply_pooling(it));
}
else if(it->op.name() == "add")
else if(it->name() == "add")
{
check_shape(s, apply_add(it));
}
else if(it->op.name() == "gemm")
else if(it->name() == "gemm")
{
check_shape(s, apply_gemm(it));
}
else if(it->op.name() == "contiguous")
else if(it->name() == "contiguous")
{
check_shape(s, apply_contiguous(it));
}
else if(it->op.name() == "batch_norm_inference")
else if(it->name() == "batch_norm_inference")
{
check_shape(s, apply_batch_norm_inference(it));
}
else if(it->name() == "softmax")
{
check_shape(s, apply_softmax(it));
}
}
}
......@@ -372,78 +412,85 @@ struct miopen_apply
instruction_ref apply_convolution(instruction_ref ins)
{
auto&& op = any_cast<convolution>(ins->op);
auto&& op = any_cast<convolution>(ins->get_operator());
auto conv = miopen_convolution{op, make_conv(op)};
auto ws = conv.compile(ctx, ins->result, ins->arguments);
auto ws = conv.compile(ctx, ins->get_shape(), ins->inputs());
auto workspace = insert_allocation(ins, ws, "workspace");
auto output = insert_allocation(ins, ins->result);
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(
ins, conv, ins->arguments.at(0), ins->arguments.at(1), workspace, output);
ins, conv, ins->inputs().at(0), ins->inputs().at(1), workspace, output);
}
instruction_ref apply_pooling(instruction_ref ins)
{
auto&& op = any_cast<pooling>(ins->op);
auto&& op = any_cast<pooling>(ins->get_operator());
auto pd = make_pooling(op);
auto output = insert_allocation(ins, ins->result);
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(
ins, miopen_pooling{op, std::move(pd)}, ins->arguments.at(0), output);
ins, miopen_pooling{op, std::move(pd)}, ins->inputs().at(0), output);
}
instruction_ref apply_activation(instruction_ref ins)
{
auto&& op = any_cast<activation>(ins->op);
auto&& op = any_cast<activation>(ins->get_operator());
auto ad = make_relu();
if(op.mode == "relu")
{
auto output = insert_allocation(ins, ins->result);
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(
ins, miopen_relu{std::move(ad)}, ins->arguments.at(0), output);
ins, miopen_relu{std::move(ad)}, ins->inputs().at(0), output);
}
return ins;
}
instruction_ref apply_softmax(instruction_ref ins)
{
auto&& op = any_cast<softmax>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(ins, miopen_softmax{op}, ins->inputs().at(0), output);
}
instruction_ref apply_add(instruction_ref ins)
{
auto output = insert_allocation(ins, ins->result);
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(
ins, hip_add{}, ins->arguments.at(0), ins->arguments.at(1), output);
ins, hip_add{}, ins->inputs().at(0), ins->inputs().at(1), output);
}
instruction_ref apply_gemm(instruction_ref ins)
{
auto&& op = any_cast<gemm>(ins->op);
auto output = insert_allocation(ins, ins->result);
auto&& op = any_cast<gemm>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(
ins, miopen_gemm{op}, ins->arguments.at(0), ins->arguments.at(1), output);
ins, miopen_gemm{op}, ins->inputs().at(0), ins->inputs().at(1), output);
}
instruction_ref apply_contiguous(instruction_ref ins)
{
auto&& op = any_cast<contiguous>(ins->op);
auto output = insert_allocation(ins, ins->result);
return prog->replace_instruction(ins, miopen_contiguous{op}, ins->arguments.at(0), output);
auto&& op = any_cast<contiguous>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
return prog->replace_instruction(ins, miopen_contiguous{op}, ins->inputs().at(0), output);
}
instruction_ref apply_batch_norm_inference(instruction_ref ins)
{
auto&& op = any_cast<batch_norm_inference>(ins->op);
auto output = insert_allocation(ins, ins->result);
shape old_shape = ins->arguments.at(1)->get_shape();
auto&& op = any_cast<batch_norm_inference>(ins->get_operator());
auto output = insert_allocation(ins, ins->get_shape());
shape old_shape = ins->inputs().at(1)->get_shape();
std::vector<int64_t> new_shape{1, static_cast<int64_t>(old_shape.elements()), 1, 1};
auto reshape_op = reshape{new_shape};
std::vector<instruction_ref> reshapes;
std::transform(ins->arguments.begin() + 1,
ins->arguments.end(),
std::transform(ins->inputs().begin() + 1,
ins->inputs().end(),
std::back_inserter(reshapes),
[&](auto i) { return prog->insert_instruction(ins, reshape_op, i); });
return prog->replace_instruction(ins,
miopen_batch_norm_inference{op},
ins->arguments.at(0),
ins->inputs().at(0),
reshapes[0],
reshapes[1],
reshapes[2],
......
src/targets/gpu/target.cpp
View file @ e8deff52
......@@ -32,11 +32,11 @@ std::vector<pass> target::get_passes(migraph::context& gctx) const
memory_coloring{"hip::allocate"},
fuse_ops{},
dead_code_elimination{},
eliminate_workspace{},
eliminate_contiguous{},
dead_code_elimination{},
write_literals{&ctx},
eliminate_allocation{""},
eliminate_workspace{},
eliminate_allocation{"hip::allocate"},
check_context<context>{},
dead_code_elimination{}
};
......
src/targets/gpu/write_literals.cpp
View file @ e8deff52
......@@ -28,9 +28,9 @@ void write_literals::apply(program& p) const
assert(ctx != nullptr);
for(auto ins : iterator_for(p))
{
if(ins->op.name() == "@literal")
if(ins->name() == "@literal")
{
argument a = to_gpu(ins->lit.get_argument());
argument a = to_gpu(ins->get_literal().get_argument());
std::size_t n = ctx->literals.size();
ctx->literals.push_back(a);
p.replace_instruction(ins, hip_load_literal{a.get_shape(), n});
......
test/cpu_ops_test.cpp
View file @ e8deff52
......@@ -6,6 +6,132 @@
#include <migraph/verify.hpp>
#include "test.hpp"
void im2col_3x3_no_pad_identity_test()
{
std::size_t f[2] = {3, 3};
std::size_t size[2] = {3, 3};
std::array<std::size_t, 2> padding{{0, 0}};
std::array<std::size_t, 2> stride{{1, 1}};
std::array<std::size_t, 2> dilation{{1, 1}};
std::size_t channels = 1;
std::vector<int32_t> weights(channels * f[0] * f[1]);
std::vector<int32_t> input(channels * size[0] * size[1]);
std::iota(input.begin(), input.end(), 0);
migraph::program p;
migraph::shape s_image{migraph::shape::int32_type, {1, channels, size[0], size[1]}};
migraph::shape s_weights{migraph::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_image = p.add_literal(migraph::literal{s_image, input});
auto l_weights = p.add_literal(migraph::literal{s_weights, weights});
p.add_instruction(migraph::im2col{padding, stride, dilation}, l_image, l_weights);
p.compile(migraph::cpu::cpu_target{});
auto result = p.eval({});
std::size_t col_height = (size[0] - f[0] + 2 * padding[0]) / stride[0] + 1;
std::size_t col_width = (size[1] - f[1] + 2 * padding[1]) / stride[1] + 1;
std::vector<float> results_vector(channels * f[0] * f[1] * col_height * col_width);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraph::verify_range(results_vector, input));
}
void im2col_3x3_no_pad_test()
{
std::size_t f[2] = {3, 3};
std::size_t size[2] = {4, 4};
std::array<std::size_t, 2> padding{{0, 0}};
std::array<std::size_t, 2> stride{{1, 1}};
std::array<std::size_t, 2> dilation{{1, 1}};
std::size_t channels = 1;
std::vector<int32_t> weights(channels * f[0] * f[1]);
std::vector<int32_t> input(channels * size[0] * size[1]);
std::iota(input.begin(), input.end(), 0);
migraph::program p;
migraph::shape s_image{migraph::shape::int32_type, {1, channels, size[0], size[1]}};
migraph::shape s_weights{migraph::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_image = p.add_literal(migraph::literal{s_image, input});
auto l_weights = p.add_literal(migraph::literal{s_weights, weights});
p.add_instruction(migraph::im2col{padding, stride, dilation}, l_image, l_weights);
p.compile(migraph::cpu::cpu_target{});
auto result = p.eval({});
std::vector<int> correct = {0, 1, 2, 4, 5, 6, 8, 9, 10, 1, 2, 3, 5, 6, 7, 9, 10, 11,
4, 5, 6, 8, 9, 10, 12, 13, 14, 5, 6, 7, 9, 10, 11, 13, 14, 15};
std::size_t col_height = (size[0] - f[0] + 2 * padding[0]) / stride[0] + 1;
std::size_t col_width = (size[1] - f[1] + 2 * padding[1]) / stride[1] + 1;
std::vector<float> results_vector(channels * f[0] * f[1] * col_height * col_width);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraph::verify_range(results_vector, correct));
}
void im2col_3x3_stride_2_no_pad_test()
{
std::size_t f[2] = {3, 3};
std::size_t size[2] = {6, 6};
std::array<std::size_t, 2> padding{{0, 0}};
std::array<std::size_t, 2> stride{{2, 2}};
std::array<std::size_t, 2> dilation{{1, 1}};
std::size_t channels = 1;
std::vector<int32_t> weights(channels * f[0] * f[1]);
std::vector<int32_t> input(channels * size[0] * size[1]);
std::iota(input.begin(), input.end(), 0);
migraph::program p;
migraph::shape s_image{migraph::shape::int32_type, {1, channels, size[0], size[1]}};
migraph::shape s_weights{migraph::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_image = p.add_literal(migraph::literal{s_image, input});
auto l_weights = p.add_literal(migraph::literal{s_weights, weights});
p.add_instruction(migraph::im2col{padding, stride, dilation}, l_image, l_weights);
p.compile(migraph::cpu::cpu_target{});
auto result = p.eval({});
std::vector<int> correct = {0, 1, 2, 6, 7, 8, 12, 13, 14, 2, 3, 4,
8, 9, 10, 14, 15, 16, 12, 13, 14, 18, 19, 20,
24, 25, 26, 14, 15, 16, 20, 21, 22, 26, 27, 28};
std::size_t col_height = (size[0] - f[0] + 2 * padding[0]) / stride[0] + 1;
std::size_t col_width = (size[1] - f[1] + 2 * padding[1]) / stride[1] + 1;
std::vector<float> results_vector(channels * f[0] * f[1] * col_height * col_width);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraph::verify_range(results_vector, correct));
}
void im2col_3x3_with_padding_test()
{
std::size_t f[2] = {3, 3};
std::size_t size[2] = {2, 2};
std::array<std::size_t, 2> padding{{1, 1}};
std::array<std::size_t, 2> stride{{1, 1}};
std::array<std::size_t, 2> dilation{{1, 1}};
std::size_t channels = 1;
std::vector<int32_t> weights(channels * f[0] * f[1]);
std::vector<int32_t> input(channels * size[0] * size[1]);
std::iota(input.begin(), input.end(), 0);
migraph::program p;
migraph::shape s_image{migraph::shape::int32_type, {1, channels, size[0], size[1]}};
migraph::shape s_weights{migraph::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_image = p.add_literal(migraph::literal{s_image, input});
auto l_weights = p.add_literal(migraph::literal{s_weights, weights});
p.add_instruction(migraph::im2col{padding, stride, dilation}, l_image, l_weights);
p.compile(migraph::cpu::cpu_target{});
auto result = p.eval({});
std::vector<int> correct = {0, 0, 0, 0, 0, 1, 0, 2, 3, 0, 0, 0, 0, 1, 0, 2, 3, 0,
0, 0, 1, 0, 2, 3, 0, 0, 0, 0, 1, 0, 2, 3, 0, 0, 0, 0};
std::size_t col_height = (size[0] - f[0] + 2 * padding[0]) / stride[0] + 1;
std::size_t col_width = (size[1] - f[1] + 2 * padding[1]) / stride[1] + 1;
std::vector<float> results_vector(channels * f[0] * f[1] * col_height * col_width);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraph::verify_range(results_vector, correct));
}
void batch_norm_inference_test()
{
migraph::program p;
......@@ -46,6 +172,35 @@ void batch_norm_inference_test()
EXPECT(migraph::verify_range(result_vector, gold));
}
void im2col_3x3_with_channels_identity_test()
{
std::size_t f[2] = {3, 3};
std::size_t size[2] = {3, 3};
std::array<std::size_t, 2> padding{{0, 0}};
std::array<std::size_t, 2> stride{{1, 1}};
std::array<std::size_t, 2> dilation{{1, 1}};
std::size_t channels = 2;
std::vector<int32_t> weights(channels * f[0] * f[1]);
std::vector<int32_t> input(channels * size[0] * size[1]);
std::iota(input.begin(), input.end(), 0);
migraph::program p;
migraph::shape s_image{migraph::shape::int32_type, {1, channels, size[0], size[1]}};
migraph::shape s_weights{migraph::shape::int32_type, {1, channels, f[0], f[1]}};
auto l_image = p.add_literal(migraph::literal{s_image, input});
auto l_weights = p.add_literal(migraph::literal{s_weights, weights});
p.add_instruction(migraph::im2col{padding, stride, dilation}, l_image, l_weights);
p.compile(migraph::cpu::cpu_target{});
auto result = p.eval({});
std::size_t col_height = (size[0] - f[0] + 2 * padding[0]) / stride[0] + 1;
std::size_t col_width = (size[1] - f[1] + 2 * padding[1]) / stride[1] + 1;
std::vector<float> results_vector(channels * f[0] * f[1] * col_height * col_width);
result.visit([&](auto output) { results_vector.assign(output.begin(), output.end()); });
EXPECT(migraph::verify_range(results_vector, input));
}
void exp_test()
{
migraph::program p;
......@@ -666,4 +821,9 @@ int main()
conv2d_padding_test();
conv2d_padding_stride_test();
batch_norm_inference_test();
im2col_3x3_no_pad_identity_test();
im2col_3x3_no_pad_test();
im2col_3x3_stride_2_no_pad_test();
im2col_3x3_with_channels_identity_test();
im2col_3x3_with_padding_test();
}
test/eval_test.cpp
View file @ e8deff52
......@@ -21,13 +21,13 @@ struct reverse_pass
{
for(auto ins : migraph::iterator_for(p))
{
if(ins->op.name() == "sum")
if(ins->name() == "sum")
{
p.replace_instruction(ins, minus_op{}, ins->arguments);
p.replace_instruction(ins, minus_op{}, ins->inputs());
}
else if(ins->op.name() == "minus")
else if(ins->name() == "minus")
{
p.replace_instruction(ins, sum_op{}, ins->arguments);
p.replace_instruction(ins, sum_op{}, ins->inputs());
}
}
}
......
test/gpu/miopen.cpp
View file @ e8deff52
......@@ -97,10 +97,10 @@ void compile_check(migraph::program& p, const migraph::target& t)
}
template <class V>
migraph::argument run_cpu()
migraph::argument run_cpu(migraph::program& p)
{
V v;
auto p = v.create_program();
p = v.create_program();
auto_print pp{p, 0};
compile_check(p, migraph::cpu::cpu_target{});
migraph::program::parameter_map m;
......@@ -112,10 +112,10 @@ migraph::argument run_cpu()
}
template <class V>
migraph::argument run_gpu()
migraph::argument run_gpu(migraph::program& p)
{
V v;
auto p = v.create_program();
p = v.create_program();
auto_print pp{p, 1};
compile_check(p, migraph::gpu::target{});
migraph::program::parameter_map m;
......@@ -131,9 +131,20 @@ template <class V>
void verify_program()
{
auto_print::set_terminate_handler(migraph::get_type_name<V>());
auto cpu_arg_f = detach_async([] { return run_cpu<V>(); });
auto gpu_arg = run_gpu<V>();
verify_args(migraph::get_type_name<V>(), cpu_arg_f.get(), gpu_arg);
migraph::program cpu_prog;
migraph::program gpu_prog;
auto cpu_arg_f = detach_async([&] { return run_cpu<V>(cpu_prog); });
auto gpu_arg = run_gpu<V>(gpu_prog);
bool passed = verify_args(migraph::get_type_name<V>(), cpu_arg_f.get(), gpu_arg);
if(not passed)
{
V v;
auto p = v.create_program();
std::cout << p << std::endl;
std::cout << "cpu:\n" << cpu_prog << std::endl;
std::cout << "gpu:\n" << gpu_prog << std::endl;
std::cout << std::endl;
}
std::set_terminate(nullptr);
}
......@@ -235,6 +246,28 @@ struct test_add_broadcast5
}
};
struct test_softmax
{
migraph::program create_program() const
{
migraph::program p;
auto x = p.add_parameter("x", migraph::shape{migraph::shape::float_type, {5, 3, 4, 2}});
p.add_instruction(migraph::softmax{}, x);
return p;
}
};
struct test_softmax2
{
migraph::program create_program() const
{
migraph::program p;
auto x = p.add_parameter("x", migraph::shape{migraph::shape::float_type, {1, 1000, 1, 1}});
p.add_instruction(migraph::softmax{}, x);
return p;
}
};
struct test_conv
{
migraph::program create_program() const
......@@ -248,6 +281,20 @@ struct test_conv
}
};
struct test_conv2
{
migraph::program create_program() const
{
migraph::program p;
auto input =
p.add_parameter("x", migraph::shape{migraph::shape::float_type, {1, 512, 28, 28}});
auto weights =
p.add_parameter("w", migraph::shape{migraph::shape::float_type, {256, 512, 1, 1}});
p.add_instruction(migraph::convolution{{0, 0}, {1, 1}, {1, 1}}, input, weights);
return p;
}
};
struct test_conv_relu
{
migraph::program create_program() const
......@@ -428,6 +475,27 @@ struct test_batchnorm_inference
}
};
struct test_conv_bn
{
migraph::program create_program() const
{
migraph::program p;
migraph::shape xs{migraph::shape::float_type, {1, 3, 224, 224}};
migraph::shape ws{migraph::shape::float_type, {64, 3, 7, 7}};
migraph::shape vars{migraph::shape::float_type, {64}};
auto x = p.add_parameter("x", xs);
auto w = p.add_parameter("w", ws);
auto conv = p.add_instruction(migraph::convolution{{3, 3}, {2, 2}, {1, 1}}, x, w);
auto scale = p.add_literal(migraph::abs(migraph::generate_literal(vars, 1)));
auto bias = p.add_literal(migraph::abs(migraph::generate_literal(vars, 2)));
auto mean = p.add_literal(migraph::abs(migraph::generate_literal(vars, 3)));
auto variance = p.add_literal(migraph::abs(migraph::generate_literal(vars, 4)));
p.add_instruction(migraph::batch_norm_inference{}, conv, scale, bias, mean, variance);
return p;
}
};
struct test_conv_bn_relu_pooling
{
migraph::program create_program() const
......@@ -495,7 +563,10 @@ int main()
verify_program<test_add_broadcast3>();
verify_program<test_add_broadcast4>();
verify_program<test_add_broadcast5>();
verify_program<test_softmax>();
verify_program<test_softmax2>();
verify_program<test_conv>();
verify_program<test_conv2>();
verify_program<test_conv_relu>();
verify_program<test_add_relu>();
verify_program<test_conv_pooling>();
......@@ -508,6 +579,7 @@ int main()
verify_program<test_transpose>();
verify_program<test_batchnorm_inference>();
verify_program<test_batchnorm_inference_2>();
verify_program<test_conv_bn>();
verify_program<test_conv_bn_relu_pooling>();
verify_program<test_conv_bn_relu_pooling2>();
}
test/include/rob.hpp 0 → 100644
View file @ e8deff52
#ifndef MIGRAPH_GUARD_ROB_HPP
#define MIGRAPH_GUARD_ROB_HPP
#ifdef __clang__
#pragma clang diagnostic push
#pragma clang diagnostic ignored "-Wglobal-constructors"
#endif
// Used to access private member variables
template <class Tag>
struct stowed
{
static typename Tag::type value;
};
template <class Tag>
typename Tag::type stowed<Tag>::value;
template <class Tag, typename Tag::type X>
struct stow_private
{
stow_private() noexcept { stowed<Tag>::value = X; }
static stow_private instance;
};
template <class Tag, typename Tag::type X>
stow_private<Tag, X> stow_private<Tag, X>::instance;
template <class C, class T>
struct mem_data_ptr
{
using type = T C::*;
};
#define MIGRAPH_ROB(name, Type, C, mem) \
struct name##_tag : mem_data_ptr<C, Type> \
{ \
}; \
template struct stow_private<name##_tag, &C::mem>; \
template <class T> \
auto& name(T&& x) \
{ \
return x.*stowed<name##_tag>::value; \
}
#ifdef __clang__
#pragma clang diagnostic pop
#endif
#endif
test/operation.cpp
View file @ e8deff52
......@@ -43,7 +43,9 @@ void operation_copy_test()
simple_operation s{};
migraph::operation op1 = s; // NOLINT
migraph::operation op2 = op1; // NOLINT
// cppcheck-suppress duplicateExpression
EXPECT(s.name() == op1.name());
// cppcheck-suppress duplicateExpression
EXPECT(op2.name() == op1.name());
}
......
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