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Commit e12ee1f8 authored by Shucai Xiao's avatar Shucai Xiao
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fix cppcheck error

parent a3affafc
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Showing with 158 additions and 165 deletions
src/include/migraphx/quantization.hpp
View file @ e12ee1f8
......@@ -24,11 +24,11 @@ std::size_t capture_arguments(program& prog,
const std::function<void(std::size_t, std::vector<argument>)>& func);
std::shared_ptr<std::vector<std::pair<float, float>>> capture_arguments_impl(
program& prog, const target& t, const std::vector<std::string>& ins_names = {"dot"});
program& prog, const target& t, const std::vector<std::string>& ins_names = {"dot", "convolution"});
template <class T>
std::shared_ptr<std::vector<std::pair<float, float>>>
capture_arguments(program& prog, T&& t, const std::vector<std::string>& ins_names = {"dot"})
capture_arguments(program& prog, T&& t, const std::vector<std::string>& ins_names = {"dot", "convolution"})
{
static_assert(std::is_same<std::remove_cv_t<std::remove_reference_t<T>>, target>{} &&
std::is_lvalue_reference<T>{},
......
src/quantization.cpp
View file @ e12ee1f8
......@@ -162,6 +162,145 @@ void quantize(program& prog, const std::vector<std::string>& ins_names)
void quantize(program& prog) { quantize(prog, {"all"}); }
static void quantize_ins(program& prog, instruction_ref ins,
std::vector<instruction_ref>& converted_inputs,
const std::vector<std::pair<float, float>>& ins_quant_params)
{
auto orig_type = ins->get_shape().type();
auto inputs = ins->inputs();
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);
if(shape::int32_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
{
if(converted_inputs.size() == 3)
{
converted_inputs.pop_back();
}
auto q_dot = prog.insert_instruction(ins, op::quant_dot{1, 0}, converted_inputs);
auto f_dot = prog.insert_instruction(ins, op::convert{shape::float_type}, q_dot);
auto c_shape = q_dot->get_shape();
std::vector<float> vec_alpha(c_shape.elements(), new_alpha);
auto l_alpha =
prog.add_literal(literal({shape::float_type, c_shape.lens()}, vec_alpha));
if(inputs.size() == 3 and dot_op.beta != 0.0f)
{
auto alpha_ab = prog.insert_instruction(ins, op::mul{}, l_alpha, f_dot);
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());
}
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::add{}, alpha_ab, beta_c);
}
else
{
auto f_res = prog.insert_instruction(ins, op::add{}, alpha_ab, beta_c);
prog.replace_instruction(ins, op::convert{orig_type}, f_res);
}
}
else
{
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::mul{}, l_alpha, f_dot);
}
else
{
auto alpha_ab = prog.insert_instruction(ins, op::mul{}, l_alpha, f_dot);
prog.replace_instruction(ins, op::convert{orig_type}, alpha_ab);
}
}
}
}
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);
float threshold = 50.0f;
std::vector<float> vec_factor(quant_conv->get_shape().elements(), adjust_factor);
if(quant_conv->get_shape().type() == orig_type and adjust_factor >= threshold)
{
auto l_factor = prog.add_literal(
literal(quant_conv->get_shape(), vec_factor.begin(), vec_factor.end()));
prog.replace_instruction(ins, op::mul{}, quant_conv, l_factor);
}
// convert quant_conv output to float type, multiply the factor and
// conver back to original type
else
{
auto float_conv =
prog.insert_instruction(ins, op::convert{shape::float_type}, quant_conv);
auto l_factor = prog.add_literal(literal(float_conv->get_shape(), vec_factor));
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::mul{}, l_factor, float_conv);
}
else
{
auto adjusted_conv =
prog.insert_instruction(ins, op::mul{}, l_factor, float_conv);
prog.replace_instruction(ins, op::convert{orig_type}, adjusted_conv);
}
}
}
else
{
MIGRAPHX_THROW("QUANTIZE_INT8: does not support operator" + ins->name());
}
}
// 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.
......@@ -170,13 +309,13 @@ 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(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"};
......@@ -189,7 +328,7 @@ void quantize_int8(program& prog,
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;
std::unordered_map<instruction_ref, std::size_t> map_ins_index;
for(auto ins : iterator_for(prog))
{
if(not contains(ins_names, ins->name()))
......@@ -197,8 +336,6 @@ void quantize_int8(program& prog,
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;
......@@ -211,18 +348,17 @@ void quantize_int8(program& prog,
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]];
std::size_t ins_index = (map_ins_index.count(input) > 0) ? map_ins_index[input] : quant_param_index++;
map_ins_index[input] = ins_index;
auto param = quant_params[map_ins_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())
if((ins->name() == "dot") and (inputs.size() == 3) and (input == inputs.back()))
{
quant_type = shape::int32_type;
}
......@@ -235,18 +371,9 @@ void quantize_int8(program& prog,
// if the input is a convert operator, uses its input
// as its current input
instruction_ref quant_input{};
if(input->name() == "convert")
if(input->name() == "convert" and input->inputs().front()->get_shape().type() == quant_type)
{
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);
}
quant_input = input->inputs().front();
}
else
{
......@@ -267,141 +394,7 @@ void quantize_int8(program& prog,
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);
if(shape::int32_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
{
if(converted_inputs.size() == 3)
{
converted_inputs.pop_back();
}
auto q_dot = prog.insert_instruction(ins, op::quant_dot{1, 0}, converted_inputs);
auto f_dot = prog.insert_instruction(ins, op::convert{shape::float_type}, q_dot);
auto c_shape = q_dot->get_shape();
std::vector<float> vec_alpha(c_shape.elements(), new_alpha);
auto l_alpha =
prog.add_literal(literal({shape::float_type, c_shape.lens()}, vec_alpha));
if(inputs.size() == 3 and dot_op.beta != 0.0f)
{
auto alpha_ab = prog.insert_instruction(ins, op::mul{}, l_alpha, f_dot);
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());
}
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::add{}, alpha_ab, beta_c);
}
else
{
auto f_res = prog.insert_instruction(ins, op::add{}, alpha_ab, beta_c);
prog.replace_instruction(ins, op::convert{orig_type}, f_res);
}
}
else
{
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::mul{}, l_alpha, f_dot);
}
else
{
auto alpha_ab = prog.insert_instruction(ins, op::mul{}, l_alpha, f_dot);
prog.replace_instruction(ins, op::convert{orig_type}, alpha_ab);
}
}
}
}
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);
float threshold = 50.0f;
std::vector<float> vec_factor(quant_conv->get_shape().elements(), adjust_factor);
if(quant_conv->get_shape().type() == orig_type and adjust_factor >= threshold)
{
auto l_factor = prog.add_literal(
literal(quant_conv->get_shape(), vec_factor.begin(), vec_factor.end()));
prog.replace_instruction(ins, op::mul{}, quant_conv, l_factor);
}
// convert quant_conv output to float type, multiply the factor and
// conver back to original type
else
{
auto float_conv =
prog.insert_instruction(ins, op::convert{shape::float_type}, quant_conv);
auto l_factor = prog.add_literal(literal(float_conv->get_shape(), vec_factor));
if(orig_type == shape::float_type)
{
prog.replace_instruction(ins, op::mul{}, l_factor, float_conv);
}
else
{
auto adjusted_conv =
prog.insert_instruction(ins, op::mul{}, l_factor, float_conv);
prog.replace_instruction(ins, op::convert{orig_type}, adjusted_conv);
}
}
}
else
{
MIGRAPHX_THROW("QUANTIZE_INT8: does not support operator" + ins->name());
}
quantize_ins(prog, ins, converted_inputs, ins_quant_params);
}
if(quant_param_index != quant_params.size())
......@@ -462,7 +455,7 @@ std::size_t capture_arguments(program& prog,
size_t num_quant_params = 0;
// 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) {
return std::find(op_names.begin(), op_names.end(), name) != op_names.end();
}))
......
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