mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-14 02:02:46 +00:00
Add ckProfiler support for forward 3D convolutions with OUT element-wise operations. (#1354)
[ROCm/composable_kernel commit: eb44e0472a]
This commit is contained in:
Andriy Roshchenko
committed by
GitHub
GitHub
parent
c5f81450e1
commit
0675d258a6
@@ -43,7 +43,15 @@ std::ostream& LogRangeAsType(std::ostream& os, Range&& range, std::string delim)
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first = false;
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else
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os << delim;
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os << static_cast<T>(v);
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if constexpr(std::is_same_v<T, ck::f8_t> || std::is_same_v<T, ck::bf8_t>)
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{
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os << ck::type_convert<float>(v);
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}
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else
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{
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os << static_cast<T>(v);
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}
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}
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return os;
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}
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@@ -0,0 +1,352 @@
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#pragma once
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convscale.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_convinvscale.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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namespace ck {
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namespace profiler {
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template <typename DataType>
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inline constexpr double get_rtol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 1e-1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 1.5e-1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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template <typename DataType>
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inline constexpr double get_atol()
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{
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if constexpr(std::is_same_v<DataType, float>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, double>)
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{
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return 1e-6;
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}
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else if constexpr(std::is_same_v<DataType, ck::half_t>)
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{
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return 1e-3;
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}
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else if constexpr(std::is_same_v<DataType, ck::bhalf_t>)
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{
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return 5e-2;
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}
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else if constexpr(std::is_same_v<DataType, int32_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, int8_t>)
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{
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return 1e-1;
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}
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else if constexpr(std::is_same_v<DataType, ck::f8_t>)
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{
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return 16.1; // 240 and 224 are acceptable
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}
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else if constexpr(std::is_same_v<DataType, ck::bf8_t>)
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{
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return 8192.1; // 57344 and 49152 are acceptable
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}
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else
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{
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return 1e-3;
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}
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}
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template <ck::index_t NDimSpatial,
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typename InLayout,
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typename WeiLayout,
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typename OutLayout,
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typename InDataType,
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typename WeiDataType,
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typename OutDataType,
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typename OutElementOp,
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typename AComputeType = InDataType,
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typename BComputeType = AComputeType>
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bool profile_grouped_conv_fwd_outelementop_impl(int do_verification,
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int init_method,
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bool do_log,
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bool time_kernel,
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const ck::utils::conv::ConvParam& conv_param)
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{
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auto pass = true; // return status
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using CShuffleDataType = float;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using InElementOp = PassThrough;
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using WeiElementOp = PassThrough;
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const auto in_element_op = InElementOp{};
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const auto wei_element_op = WeiElementOp{};
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const auto in_g_n_c_wis_desc =
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ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(conv_param);
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const auto wei_g_k_c_xs_desc =
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ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(conv_param);
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const auto out_g_n_k_wos_desc =
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ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(conv_param);
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> a_g_n_c_wis_strides{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> b_g_k_c_xs_strides{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_lengths{};
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std::array<ck::index_t, NDimSpatial + 3> e_g_n_k_wos_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
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std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
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std::array<ck::index_t, NDimSpatial> input_left_pads{};
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std::array<ck::index_t, NDimSpatial> input_right_pads{};
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auto copy = [](const auto& x, auto& y) { ck::ranges::copy(x, y.begin()); };
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copy(in_g_n_c_wis_desc.GetLengths(), a_g_n_c_wis_lengths);
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copy(in_g_n_c_wis_desc.GetStrides(), a_g_n_c_wis_strides);
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copy(wei_g_k_c_xs_desc.GetLengths(), b_g_k_c_xs_lengths);
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copy(wei_g_k_c_xs_desc.GetStrides(), b_g_k_c_xs_strides);
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copy(out_g_n_k_wos_desc.GetLengths(), e_g_n_k_wos_lengths);
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copy(out_g_n_k_wos_desc.GetStrides(), e_g_n_k_wos_strides);
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copy(conv_param.conv_filter_strides_, conv_filter_strides);
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copy(conv_param.conv_filter_dilations_, conv_filter_dilations);
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copy(conv_param.input_left_pads_, input_left_pads);
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copy(conv_param.input_right_pads_, input_right_pads);
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Tensor<InDataType> input(in_g_n_c_wis_desc);
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Tensor<WeiDataType> weight(wei_g_k_c_xs_desc);
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Tensor<CShuffleDataType> c(out_g_n_k_wos_desc);
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Tensor<OutDataType> host_output(out_g_n_k_wos_desc);
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Tensor<OutDataType> device_output(out_g_n_k_wos_desc);
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std::cout << "input: " << input.mDesc << std::endl;
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std::cout << "weight: " << weight.mDesc << std::endl;
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std::cout << "output: " << host_output.mDesc << std::endl;
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switch(init_method)
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{
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case 0: break;
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case 1:
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input.GenerateTensorValue(GeneratorTensor_2<InDataType>{-5, 5});
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weight.GenerateTensorValue(GeneratorTensor_2<WeiDataType>{-1, 1});
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break;
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default:
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input.GenerateTensorValue(GeneratorTensor_3<InDataType>{-5.0, 5.0});
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weight.GenerateTensorValue(GeneratorTensor_3<WeiDataType>{-1.0, 1.0});
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}
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DeviceMem in_device_buf(sizeof(InDataType) * input.mDesc.GetElementSpaceSize());
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DeviceMem wei_device_buf(sizeof(WeiDataType) * weight.mDesc.GetElementSpaceSize());
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DeviceMem out_device_buf(sizeof(OutDataType) * device_output.mDesc.GetElementSpaceSize());
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in_device_buf.ToDevice(input.mData.data());
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wei_device_buf.ToDevice(weight.mData.data());
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// random scale values
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auto scale_in = type_convert<float>(
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type_convert<f8_t>(2.0f * float(RAND_MAX / 2 - std::rand()) / float(RAND_MAX)));
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auto scale_wei = type_convert<float>(
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type_convert<f8_t>(2.0f * float(RAND_MAX / 2 - std::rand()) / float(RAND_MAX)));
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auto scale_out = type_convert<float>(
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type_convert<f8_t>(2.0f * float(RAND_MAX / 2 - std::rand()) / float(RAND_MAX)));
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// initialize out_element_op for each iteration
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const auto out_element_op = OutElementOp{scale_in, scale_wei, scale_out};
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std::cout << "scale_in: " << scale_in << std::endl;
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std::cout << "scale_wei: " << scale_wei << std::endl;
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std::cout << "scale_out: " << scale_out << std::endl;
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// run reference op
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if(do_verification)
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{
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std::cout << "\nVerifying algorithm against reference convolution..." << std::endl;
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std::cout << "\tUsing (rel_tol,abs_tol) = (" << std::setprecision(7)
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<< get_rtol<OutDataType>() << ", " << get_atol<OutDataType>() << ")" << std::endl;
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auto ref_conv = ck::tensor_operation::host::ReferenceConvFwd<NDimSpatial,
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InDataType,
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WeiDataType,
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CShuffleDataType,
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InElementOp,
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WeiElementOp,
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PassThrough>{};
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auto ref_invoker = ref_conv.MakeInvoker();
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auto ref_argument = ref_conv.MakeArgument(input,
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weight,
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c,
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conv_param.conv_filter_strides_,
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conv_param.conv_filter_dilations_,
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conv_param.input_left_pads_,
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conv_param.input_right_pads_,
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in_element_op,
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wei_element_op,
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PassThrough{});
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c.SetZero();
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ref_invoker.Run(ref_argument);
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host_output.ForEach([&](auto&, auto idx) { out_element_op(host_output(idx), c(idx)); });
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}
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std::string best_op_name;
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float best_avg_time = 0;
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float best_tflops = 0;
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float best_gb_per_sec = 0;
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auto run_impl = [&](auto& op_ptr, auto& argument_ptr) {
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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// re-init output to zero before profiling next kernel
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out_device_buf.SetZero();
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std::string op_name = op_ptr->GetTypeString();
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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float avg_time =
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
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std::size_t flop = conv_param.GetFlops();
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std::size_t num_btype = conv_param.GetByte<InDataType, WeiDataType, OutDataType>();
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_btype / 1.E6 / avg_time;
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std::cout << "Perf: " << std::setw(10) << avg_time << " ms, " << tflops << " TFlops, "
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<< gb_per_sec << " GB/s, " << op_name << std::endl;
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if(tflops > best_tflops)
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{
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best_op_name = op_name;
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best_tflops = tflops;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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}
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if(do_verification)
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{
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out_device_buf.FromDevice(device_output.mData.data());
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pass = pass & ck::utils::check_err(device_output,
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host_output,
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"Error: Device and Host results do not match!",
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get_rtol<OutDataType>(),
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get_atol<OutDataType>());
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if(do_log)
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{
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LogRangeAsType<InDataType>(std::cout << "input : ", input.mData, ",")
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<< std::endl;
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LogRangeAsType<WeiDataType>(std::cout << "weight: ", weight.mData, ",")
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<< std::endl;
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LogRangeAsType<OutDataType>(
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std::cout << "host_output : ", host_output.mData, ",")
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<< std::endl;
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LogRangeAsType<OutDataType>(
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std::cout << "device_output: ", device_output.mData, ",")
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<< std::endl;
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}
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}
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}
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else
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{
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std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
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}
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};
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD<NDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<>,
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OutDataType,
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InElementOp,
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WeiElementOp,
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OutElementOp,
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AComputeType,
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BComputeType>;
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// get device op instances
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const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
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DeviceOp>::GetInstances();
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std::cout << "ckProfiler found " << op_ptrs.size() << " instances" << std::endl;
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for(auto& op_ptr : op_ptrs)
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{
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auto argument_ptr = op_ptr->MakeArgumentPointer(in_device_buf.GetDeviceBuffer(),
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wei_device_buf.GetDeviceBuffer(),
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{},
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out_device_buf.GetDeviceBuffer(),
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a_g_n_c_wis_lengths,
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a_g_n_c_wis_strides,
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b_g_k_c_xs_lengths,
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b_g_k_c_xs_strides,
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{},
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{},
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e_g_n_k_wos_lengths,
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e_g_n_k_wos_strides,
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conv_filter_strides,
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conv_filter_dilations,
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input_left_pads,
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input_right_pads,
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in_element_op,
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wei_element_op,
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out_element_op);
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run_impl(op_ptr, argument_ptr);
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}
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std::cout << "Best configuration parameters:"
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<< "\nname: " << best_op_name << "\navg_time: " << best_avg_time
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<< "\ntflops: " << best_tflops << "\nGB/s: " << best_gb_per_sec << std::endl;
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return pass;
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}
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} // namespace profiler
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} // namespace ck
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@@ -57,6 +57,7 @@ if(GPU_TARGETS MATCHES "gfx9")
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list(APPEND PROFILER_SOURCES profile_conv_fwd_bias_relu_add.cpp)
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list(APPEND PROFILER_SOURCES profile_conv_bwd_data.cpp)
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list(APPEND PROFILER_SOURCES profile_conv_fwd.cpp)
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list(APPEND PROFILER_SOURCES profile_grouped_conv_fwd_outelementop.cpp)
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endif()
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@@ -134,6 +135,8 @@ if(GPU_TARGETS MATCHES "gfx9")
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target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_conv2d_bwd_data_instance)
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target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv1d_bwd_weight_instance)
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target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv2d_bwd_weight_instance)
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target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convscale_instance)
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target_link_libraries(${PROFILER_EXECUTABLE} PRIVATE device_grouped_conv3d_fwd_convinvscale_instance)
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endif()
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if(GPU_TARGETS MATCHES "gfx9" OR GPU_TARGETS MATCHES "gfx11" OR GPU_TARGETS MATCHES "gfx12")
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220
profiler/src/profile_grouped_conv_fwd_outelementop.cpp
Normal file
220
profiler/src/profile_grouped_conv_fwd_outelementop.cpp
Normal file
@@ -0,0 +1,220 @@
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "profiler/profile_grouped_conv_fwd_outelementop_impl.hpp"
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#include "ck/utility/data_type.hpp"
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#include "profiler_operation_registry.hpp"
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#include <iostream>
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enum struct ConvLayout
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{
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GNHWC_GKYXC_GNHWK = 0,
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NHWGC_GKYXC_NHWGK = 1
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};
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enum struct OutElementOp
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{
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ConvScale = 0,
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ConvInvScale = 1
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};
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enum struct ConvDataType
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{
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F8_F8_F8 = 0,
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BF8_BF8_F8 = 1,
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F8_BF8_F8 = 2,
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BF8_F8_F8 = 3
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};
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#define OP_NAME "grouped_conv_fwd_outelementop"
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#define OP_DESC "Grouped Convolution Forward+Elementwise Operation"
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static void print_helper_msg()
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{
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// clang-format off
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std::cout
|
||||
<< "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
|
||||
<< "arg2: data type (0: Input fp8, Weight fp8, Output fp8\n"
|
||||
<< " 1: Input bf8, Weight bf8, Output fp8\n"
|
||||
<< " 2: Input fp8, Weight bf8, Output fp8\n"
|
||||
<< " 3: Input bf8, Weight fp8, Output fp8)\n"
|
||||
<< "arg3: element-wise operation (0: ConvScale\n"
|
||||
<< " 1: ConvInvScale)\n"
|
||||
<< "arg4: tensor layout (0: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]\n"
|
||||
<< " 1: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K])\n"
|
||||
<< "arg5: verification (0: no, 1: yes)\n"
|
||||
<< "arg6: initialization (0: no init, 1: integer value, 2: decimal value)\n"
|
||||
<< "arg7: print tensor value (0: no; 1: yes)\n"
|
||||
<< "arg8: time kernel (0: no, 1: yes)\n"
|
||||
<< ck::utils::conv::get_conv_param_parser_helper_msg() << std::endl;
|
||||
// clang-format on
|
||||
}
|
||||
|
||||
int grouped_conv_fwd_outelementop(int argc, char* argv[])
|
||||
{
|
||||
|
||||
// 9 total, 1 for num_dim_spatial
|
||||
if(argc < 10)
|
||||
{
|
||||
print_helper_msg();
|
||||
return 1;
|
||||
}
|
||||
|
||||
const auto data_type = static_cast<ConvDataType>(std::stoi(argv[2]));
|
||||
const auto op = static_cast<OutElementOp>(std::stoi(argv[3]));
|
||||
const auto layout = static_cast<ConvLayout>(std::stoi(argv[4]));
|
||||
const bool do_verification = std::stoi(argv[5]);
|
||||
const int init_method = std::stoi(argv[6]);
|
||||
const bool do_log = std::stoi(argv[7]);
|
||||
const bool time_kernel = std::stoi(argv[8]);
|
||||
const int num_dim_spatial = std::stoi(argv[9]);
|
||||
|
||||
// 8 for control, 1 for num_dim_spatial, 4 for G/N/K/C, and 6 * num_dim_spatial + 1 for argv[0]
|
||||
if(argc != 8 + 1 + 4 + 6 * num_dim_spatial + 1)
|
||||
{
|
||||
print_helper_msg();
|
||||
return 1;
|
||||
}
|
||||
|
||||
const auto params = ck::utils::conv::parse_conv_param(num_dim_spatial, 10, argv);
|
||||
|
||||
using F8 = ck::f8_t;
|
||||
using BF8 = ck::bf8_t;
|
||||
|
||||
using GKZYXC = ck::tensor_layout::convolution::GKZYXC;
|
||||
using NDHWGC = ck::tensor_layout::convolution::NDHWGC;
|
||||
using NDHWGK = ck::tensor_layout::convolution::NDHWGK;
|
||||
|
||||
using ConvScale = ck::tensor_operation::element_wise::ConvScale;
|
||||
using ConvInvScale = ck::tensor_operation::element_wise::ConvInvscale;
|
||||
|
||||
constexpr auto I3 = ck::Number<3>{};
|
||||
|
||||
auto profile = [&](auto num_dim_spatial_tmp,
|
||||
auto in_layout,
|
||||
auto wei_layout,
|
||||
auto out_layout,
|
||||
auto in_type,
|
||||
auto wei_type,
|
||||
auto out_type,
|
||||
auto out_element_op,
|
||||
auto a_compute_type,
|
||||
auto b_compute_type) {
|
||||
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
|
||||
|
||||
using InLayout = decltype(in_layout);
|
||||
using WeiLayout = decltype(wei_layout);
|
||||
using OutLayout = decltype(out_layout);
|
||||
|
||||
using InDataType = decltype(in_type);
|
||||
using WeiDataType = decltype(wei_type);
|
||||
using OutDataType = decltype(out_type);
|
||||
|
||||
using OutElementOp = decltype(out_element_op);
|
||||
|
||||
using AComputeType = decltype(a_compute_type);
|
||||
using BComputeType = decltype(b_compute_type);
|
||||
|
||||
bool pass = ck::profiler::profile_grouped_conv_fwd_outelementop_impl<NDimSpatial,
|
||||
InLayout,
|
||||
WeiLayout,
|
||||
OutLayout,
|
||||
InDataType,
|
||||
WeiDataType,
|
||||
OutDataType,
|
||||
OutElementOp,
|
||||
AComputeType,
|
||||
BComputeType>(
|
||||
do_verification, init_method, do_log, time_kernel, params);
|
||||
|
||||
return pass ? 0 : 1;
|
||||
};
|
||||
|
||||
if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
|
||||
{
|
||||
if(op == OutElementOp::ConvScale)
|
||||
{
|
||||
if(data_type == ConvDataType::F8_F8_F8)
|
||||
{
|
||||
return profile(
|
||||
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F8{}, F8{}, F8{}, ConvScale{}, F8{}, F8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::BF8_BF8_F8)
|
||||
{
|
||||
return profile(I3,
|
||||
NDHWGC{},
|
||||
GKZYXC{},
|
||||
NDHWGK{},
|
||||
BF8{},
|
||||
BF8{},
|
||||
F8{},
|
||||
ConvScale{},
|
||||
BF8{},
|
||||
BF8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::F8_BF8_F8)
|
||||
{
|
||||
return profile(
|
||||
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F8{}, BF8{}, F8{}, ConvScale{}, F8{}, BF8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::BF8_F8_F8)
|
||||
{
|
||||
return profile(
|
||||
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF8{}, F8{}, F8{}, ConvScale{}, BF8{}, F8{});
|
||||
}
|
||||
}
|
||||
else if(op == OutElementOp::ConvInvScale)
|
||||
{
|
||||
if(data_type == ConvDataType::F8_F8_F8)
|
||||
{
|
||||
return profile(
|
||||
I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F8{}, F8{}, F8{}, ConvInvScale{}, F8{}, F8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::BF8_BF8_F8)
|
||||
{
|
||||
return profile(I3,
|
||||
NDHWGC{},
|
||||
GKZYXC{},
|
||||
NDHWGK{},
|
||||
BF8{},
|
||||
BF8{},
|
||||
F8{},
|
||||
ConvInvScale{},
|
||||
BF8{},
|
||||
BF8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::F8_BF8_F8)
|
||||
{
|
||||
return profile(I3,
|
||||
NDHWGC{},
|
||||
GKZYXC{},
|
||||
NDHWGK{},
|
||||
F8{},
|
||||
BF8{},
|
||||
F8{},
|
||||
ConvInvScale{},
|
||||
F8{},
|
||||
BF8{});
|
||||
}
|
||||
else if(data_type == ConvDataType::BF8_F8_F8)
|
||||
{
|
||||
return profile(I3,
|
||||
NDHWGC{},
|
||||
GKZYXC{},
|
||||
NDHWGK{},
|
||||
BF8{},
|
||||
F8{},
|
||||
F8{},
|
||||
ConvInvScale{},
|
||||
BF8{},
|
||||
F8{});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
std::cout << "this data_type & layout is not implemented" << std::endl;
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
||||
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, grouped_conv_fwd_outelementop);
|
||||
20
script/profile_grouped_conv_fwd_outelementop.sh
Executable file
20
script/profile_grouped_conv_fwd_outelementop.sh
Executable file
@@ -0,0 +1,20 @@
|
||||
#!/bin/bash
|
||||
|
||||
## GPU visibility
|
||||
export HIP_VISIBLE_DEVICES=0
|
||||
DRIVER="../build/bin/ckProfiler"
|
||||
|
||||
OP=$1
|
||||
DATATYPE=$2
|
||||
OUTELEMENTOP=$3
|
||||
LAYOUT=$4
|
||||
VERIFY=$5
|
||||
INIT=$6
|
||||
LOG=$7
|
||||
TIME=$8
|
||||
|
||||
N=$9
|
||||
|
||||
####### op datatype OUTELEMENTOP layout verify init log time Ndims G N K C Z Y X Di Hi Wi Sz Sy Sx Dz Dy Dx Left Pz LeftPy LeftPx RightPz RightPy RightPx
|
||||
$DRIVER $OP $DATATYPE $OUTELEMENTOP $LAYOUT $VERIFY $INIT $LOG $TIME 3 32 $N 96 96 3 3 3 28 28 28 1 1 1 1 1 1 1 1 1 1 1 1
|
||||
$DRIVER $OP $DATATYPE $OUTELEMENTOP $LAYOUT $VERIFY $INIT $LOG $TIME 3 32 $N 192 192 3 3 3 28 28 28 1 1 1 1 1 1 1 1 1 1 1 1
|
||||
Reference in New Issue
Block a user