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Commit b5890a03 authored by Shucai Xiao's avatar Shucai Xiao
Browse files

add unit test for the op_capture argument

parent 23df5352
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Showing with 52 additions and 228 deletions
src/include/migraphx/quantization.hpp
View file @ b5890a03
...@@ -23,12 +23,6 @@ void capture_arguments(program& prog, ...@@ -23,12 +23,6 @@ void capture_arguments(program& prog,
void capture_arguments(program& prog, const std::vector<std::string>& ins_names); void capture_arguments(program& prog, const std::vector<std::string>& ins_names);
void capture_arguments(program& prog); void capture_arguments(program& prog);
void quantize_int8(program& prog);
void quantize_int8(program& prog, const std::vector<std::string>& ins_names);
void quantize_int8(program& prog,
const std::vector<std::string>& ins_names,
const std::vector<std::pair<float, float>>& quant_params);
} // namespace MIGRAPHX_INLINE_NS } // namespace MIGRAPHX_INLINE_NS
} // namespace migraphx } // namespace migraphx
......
src/py/migraphx_py.cpp
View file @ b5890a03
...@@ -187,17 +187,6 @@ PYBIND11_MODULE(migraphx, m) ...@@ -187,17 +187,6 @@ PYBIND11_MODULE(migraphx, m)
migraphx::quantize(p, ins_names); migraphx::quantize(p, ins_names);
}); });
m.def("quantize", [](migraphx::program& p) { migraphx::quantize(p, {"all"}); }); m.def("quantize", [](migraphx::program& p) { migraphx::quantize(p, {"all"}); });
m.def("quantize_int8", [](migraphx::program& p, std::vector<std::string>& ins_names) {
migraphx::quantize_int8(p, ins_names);
});
m.def("quantize_int8",
[](migraphx::program& p,
std::vector<std::string>& ins_names,
std::vector<std::pair<float, float>>& quant_params) {
migraphx::quantize_int8(p, ins_names, quant_params);
});
m.def("quantize_int8", [](migraphx::program& p) { migraphx::quantize_int8(p); });
m.def("capture_arguments", [](migraphx::program& p, const std::vector<std::string>& ins_names) { m.def("capture_arguments", [](migraphx::program& p, const std::vector<std::string>& ins_names) {
migraphx::capture_arguments(p, ins_names); migraphx::capture_arguments(p, ins_names);
}); });
......
src/quantization.cpp
View file @ b5890a03
...@@ -123,216 +123,6 @@ void quantize(program& prog, const std::vector<std::string>& ins_names) ...@@ -123,216 +123,6 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
void quantize(program& prog) { quantize(prog, {"all"}); } void quantize(program& prog) { quantize(prog, {"all"}); }
// int8 quantization is different from fp16 since int8 can only handle value
// -128 ~ 127. To convert the float or double to int8, we need a scale and
// a shift, then the convert can be done as v_int8 = fp * scale + shift.
// To simplify the changes, we consider shift as 0.0f for now.
void quantize_int8(program& prog,
const std::vector<std::string>& ins_names,
const std::vector<std::pair<float, float>>& quant_params)
{
for(size_t i = 0; i < quant_params.size(); i++)
{
auto param = quant_params.at(i);
std::cout << "index = " << i << ", scale = " << param.first << "\t" << param.second
<< std::endl;
}
std::cout << std::endl;
// For now, we only support the int8 quantization of gemm and convolution
std::vector<std::string> op_names = {"dot", "convolution"};
if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
return (std::find(op_names.begin(), op_names.end(), name) != op_names.end());
}))
{
MIGRAPHX_THROW("QUANTIZE_INT8: only support DOT and CONVOLUTION operation");
}
std::size_t quant_param_index = 0;
std::unordered_map<instruction_ref, instruction_ref> map_quant_ins;
std::unordered_map<instruction_ref, std::size_t> map_index;
for(auto ins : iterator_for(prog))
{
if(not contains(ins_names, ins->name()))
{
continue;
}
shape::type_t orig_type = ins->get_shape().type();
// for the dot operator, there could be 2 or 3 input arguments
// if the 3rd argument is available, convert it to an int32.
std::vector<instruction_ref> converted_inputs;
// process all inputs, if input is a fp32 or fp64, convert it
// to a int8 type by adding a convert operator and replace
// the operator with the corresponding int8 version
auto inputs = ins->inputs();
std::vector<std::pair<float, float>> ins_quant_params;
for(auto input : inputs)
{
// calculate the index of each instruction to be quantized
if(map_index.count(input) == 0)
{
map_index[input] = quant_param_index++;
}
auto param = quant_params[map_index[input]];
ins_quant_params.push_back(param);
// In general, the target_type is int8, but for the dot
// operation, if it has 3 inputs, then the last one should
// be converted to int32_type
shape::type_t quant_type = shape::int8_type;
if(ins->name() == "dot" and inputs.size() == 3 and input == inputs.back())
{
quant_type = shape::int32_type;
}
auto s = input->get_shape();
if((s.type() == shape::float_type || s.type() == shape::double_type ||
s.type() == shape::int32_type) &&
s.type() != quant_type)
{
// if the input is a convert operator, uses its input
// as its current input
instruction_ref quant_input{};
if(input->name() == "convert")
{
auto tmp_ins = input->inputs().front();
if(tmp_ins->get_shape().type() == quant_type)
{
quant_input = input->inputs().front();
}
else
{
quant_input = insert_quant_ins(
prog, input, quant_type, map_quant_ins, param.first, param.second);
}
}
else
{
quant_input = insert_quant_ins(
prog, input, quant_type, map_quant_ins, param.first, param.second);
}
converted_inputs.push_back(quant_input);
}
else
{
converted_inputs.push_back(input);
}
}
// no change for the input, go to the next instruction
if(inputs == converted_inputs)
{
continue;
}
// When converting from other types to int8_type, there are parameters
// used as scale and shift(.0f), which will generate results diffrent from
// the original results. To adjust the output to be "correct(approximatly
// equal)", we need additional calculation for the adjustment
if(ins->name() == "dot")
{
auto dot_op = any_cast<op::dot>(ins->get_operator());
float new_alpha =
dot_op.alpha / (ins_quant_params[0].first * ins_quant_params[1].first);
float new_beta = dot_op.beta;
// We need additional checking about the quant_alpha value. If
// abs(quant_alpha) > 50 (some tmp value set here), we can convert
// it to an integer as the new_alpha in the quant_dot
float threshold = 50.0f;
if(fabs(new_alpha) >= threshold && fabs(new_beta) >= threshold)
{
int32_t quant_alpha = static_cast<int32_t>(new_alpha);
int32_t quant_beta = static_cast<int32_t>(new_beta);
shape quant_shape = compute_shape(op::quant_dot{1, 0}, converted_inputs);
if(quant_shape.type() == orig_type)
{
prog.replace_instruction(
ins, op::quant_dot{quant_alpha, quant_beta}, converted_inputs);
}
else
{
auto quant_dot = prog.insert_instruction(
ins, op::quant_dot{quant_alpha, quant_beta}, converted_inputs);
prog.replace_instruction(ins, op::convert{orig_type}, quant_dot);
}
}
// either alpha or beta cannot be quantized because of too big
// relative rounding error
else
{
auto q_dot = prog.insert_instruction(ins, op::quant_dot{1, 0}, converted_inputs);
if(inputs.size() == 3 and dot_op.beta != 0.0f)
{
auto alpha_ab = prog.insert_instruction(
ins, op::convert{orig_type, new_alpha, 0.0f}, q_dot);
auto c_shape = q_dot->get_shape();
std::vector<float> vec_beta(c_shape.elements(), dot_op.beta);
auto l_beta =
prog.add_literal(literal({shape::float_type, c_shape.lens()}, vec_beta));
instruction_ref beta_c{};
if(orig_type != shape::float_type)
{
auto fp32_c = prog.insert_instruction(
ins, op::convert{shape::float_type}, inputs.back());
auto fp32_beta_c = prog.insert_instruction(ins, op::mul{}, l_beta, fp32_c);
beta_c = prog.insert_instruction(ins, op::convert{orig_type}, fp32_beta_c);
}
else
{
beta_c = prog.insert_instruction(ins, op::mul{}, l_beta, inputs.back());
}
prog.replace_instruction(ins, op::add{}, alpha_ab, beta_c);
}
else
{
prog.replace_instruction(ins, op::convert{orig_type, new_alpha, 0.0f}, q_dot);
}
}
}
else if(ins->name() == "convolution")
{
// Current MIOpen convolution does not support alpha and beta,
// so we need a separate multiply to adjust the output
auto conv_op = any_cast<op::convolution>(ins->get_operator());
auto padding = conv_op.padding;
auto stride = conv_op.stride;
auto dilation = conv_op.dilation;
auto padding_mode = conv_op.padding_mode;
auto group = conv_op.group;
auto adjust_factor = 1.0f / (ins_quant_params[0].first * ins_quant_params[1].first);
auto quant_conv = prog.insert_instruction(
ins,
op::quant_convolution{padding, stride, dilation, padding_mode, group},
converted_inputs);
prog.replace_instruction(ins, op::convert{orig_type, adjust_factor, 0.0f}, quant_conv);
}
else
{
MIGRAPHX_THROW("QUANTIZE_INT8: does not support operator" + ins->name());
}
}
if(quant_param_index != quant_params.size())
{
MIGRAPHX_THROW("QUANTIZE_INT8: number of scales does not match");
}
}
void quantize_int8(program& prog, const std::vector<std::string>& ins_names)
{
quantize_int8(prog, ins_names, *prog.int8_quant_params);
}
void quantize_int8(program& prog)
{
std::vector<std::string> ins_names = {"dot", "convolution"};
quantize_int8(prog, ins_names);
}
// For the input of each input argument, we need to insert a // For the input of each input argument, we need to insert a
// capture operator to compute the scale and shift // capture operator to compute the scale and shift
void capture_arguments(program& prog, void capture_arguments(program& prog,
...@@ -342,7 +132,7 @@ void capture_arguments(program& prog, ...@@ -342,7 +132,7 @@ void capture_arguments(program& prog,
size_t num_quant_params = 0; size_t num_quant_params = 0;
// the int8 quantization only support dot and convolution // the int8 quantization only support dot and convolution
std::vector<std::string> op_names = {"dot", "convolution", "quant_dot", "quant_convolution"}; std::vector<std::string> op_names = {"dot", "convolution"};
if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) { if(!std::all_of(ins_names.begin(), ins_names.end(), [&](auto name) {
return std::find(op_names.begin(), op_names.end(), name) != op_names.end(); return std::find(op_names.begin(), op_names.end(), name) != op_names.end();
})) }))
......
test/type_conversion.cpp
View file @ b5890a03
...@@ -202,4 +202,55 @@ TEST_CASE(literal_add) ...@@ -202,4 +202,55 @@ TEST_CASE(literal_add)
} }
} }
TEST_CASE(op_capture)
{
auto test_func = [&](std::size_t ins_index, std::vector<migraphx::argument> args) {
(void)ins_index;
(void)args;
};
auto create_program_float = [] {
migraphx::program p;
migraphx::shape s1{migraphx::shape::float_type, {3, 3}};
migraphx::shape s2{migraphx::shape::float_type, {3, 6}};
auto p1 = p.add_parameter("x", s1);
auto p2 = p.add_parameter("y", s1);
auto pb = p.add_parameter("b", s2);
auto pc = p.add_parameter("c", s2);
auto pa = p.add_instruction(migraphx::op::add{}, p1, p2);
auto ps = p.add_instruction(migraphx::op::dot{}, pa, pb, pc);
p.add_instruction(migraphx::op::dot{}, pa, ps);
return p;
};
auto create_program_op = [&] {
migraphx::program p;
migraphx::shape s1{migraphx::shape::float_type, {3, 3}};
migraphx::shape s2{migraphx::shape::float_type, {3, 6}};
auto p1 = p.add_parameter("x", s1);
auto p2 = p.add_parameter("y", s1);
auto pb = p.add_parameter("b", s2);
auto pc = p.add_parameter("c", s2);
auto pa = p.add_instruction(migraphx::op::add{}, p1, p2);
auto opb = p.insert_instruction(std::next(pb), migraphx::op::capture{1, test_func}, pb);
auto opc = p.insert_instruction(std::next(pc), migraphx::op::capture{2, test_func}, pc);
auto opa = p.add_instruction(migraphx::op::capture{0, test_func}, pa);
auto ps = p.add_instruction(migraphx::op::dot{}, opa, opb, opc);
auto ops = p.add_instruction(migraphx::op::capture{3, test_func}, ps);
p.add_instruction(migraphx::op::dot{}, opa, ops);
return p;
};
{
auto p = create_program_float();
auto op_capture_p = create_program_op();
migraphx::capture_arguments(p);
EXPECT(p == op_capture_p);
}
}
int main(int argc, const char* argv[]) { test::run(argc, argv); } int main(int argc, const char* argv[]) { test::run(argc, argv); }
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