mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-04-19 22:39:03 +00:00
Conv perlayer int8 quantization (#471)
* Add conv2d requant example * Fix bash error * Rename example * 1. Rename gemm quantization 2. shares the requantization lambda function with conv * Refine declare type * Add conv bias relu quantization exmaple * clang format * Fix compile error due to merge develop * Fix CI error * Extract quantization post operation into another file * Support quantization for non piecewise linear function * Add instance for conv quantization * Add convolution quantization factory * Add convolution quantization client example * Add more instances with different template parameters * clang format * Sync the naming with the develop
This commit is contained in:
rocking5566
5
client_example/09_quantization/CMakeLists.txt
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5
client_example/09_quantization/CMakeLists.txt
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@@ -0,0 +1,5 @@
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add_executable(client_conv2d_fwd_bias_relu_perlayer_quantization conv2d_fwd_bias_relu_perlayer_quantization.cpp)
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target_link_libraries(client_conv2d_fwd_bias_relu_perlayer_quantization PRIVATE composable_kernel::device_operations)
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add_executable(client_conv2d_fwd_perlayer_quantization conv2d_fwd_perlayer_quantization.cpp)
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target_link_libraries(client_conv2d_fwd_perlayer_quantization PRIVATE composable_kernel::device_operations)
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@@ -0,0 +1,198 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iomanip>
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#include <iostream>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_bias_forward_perlayer_quantization.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using InDataType = int8_t;
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using WeiDataType = int8_t;
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using BiasDataType = int32_t;
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using OutDataType = int8_t;
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using InLayout = ck::tensor_layout::convolution::GNHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKYXC;
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using BiasLayout = ck::tensor_layout::convolution::G_K;
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using OutLayout = ck::tensor_layout::convolution::GNHWK;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ActivationOp = ck::tensor_operation::element_wise::Relu;
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using OutElementOp = ck::tensor_operation::element_wise::Add_Activation_Mul_Clamp<ActivationOp>;
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static constexpr ck::index_t NumDimSpatial = 2;
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static constexpr ck::index_t G = 1;
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static constexpr ck::index_t N = 4;
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static constexpr ck::index_t K = 64;
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static constexpr ck::index_t C = 32;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Hi = 71;
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static constexpr ck::index_t Wi = 71;
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static constexpr ck::index_t Ho = 36;
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static constexpr ck::index_t Wo = 36;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main(int argc, char* argv[])
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{
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std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
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std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C};
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std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
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std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
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std::array<ck::index_t, 5> bias_lengths{G, N, K, Ho, Wo};
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std::array<ck::index_t, 5> bias_strides{K, 0, 1, 0, 0};
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std::array<ck::index_t, 5> out_lengths{G, N, C, Ho, Wo};
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std::array<ck::index_t, 5> out_strides{N * Ho * Wo * C, Ho * Wo * C, 1, Wo * C, C};
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std::array<ck::index_t, 2> in_left_pad{1, 1};
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std::array<ck::index_t, 2> in_right_pad{1, 1};
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std::array<ck::index_t, 2> conv_strides{2, 2};
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std::array<ck::index_t, 2> conv_dilations{1, 1};
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SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
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SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
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using DeviceOp =
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ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
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InLayout,
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WeiLayout,
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ck::Tuple<BiasLayout>,
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OutLayout,
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InDataType,
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WeiDataType,
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ck::Tuple<BiasDataType>,
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OutDataType,
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PassThrough,
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PassThrough,
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OutElementOp>;
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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 << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{bias.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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weight_lengths,
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weight_strides,
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{bias_lengths},
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{bias_strides},
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out_lengths,
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out_strides,
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conv_strides,
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conv_dilations,
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in_left_pad,
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in_right_pad,
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PassThrough{},
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PassThrough{},
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OutElementOp{0.5f, ActivationOp{}});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop = G * 2 * N * K * C * Ho * Wo * Y * X;
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std::size_t num_bytes = G * sizeof(InDataType) * N * Hi * Wi * C +
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G * sizeof(WeiDataType) * K * Y * X * C +
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G * sizeof(OutDataType) * N * Ho * Wo * K;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_bytes / 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_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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best_tflops = tflops;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
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<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{bias.GetDeviceBuffer()},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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weight_lengths,
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weight_strides,
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{bias_lengths},
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{bias_strides},
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out_lengths,
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out_strides,
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conv_strides,
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conv_dilations,
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in_left_pad,
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in_right_pad,
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PassThrough{},
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PassThrough{},
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OutElementOp{0.5f, ActivationOp{}});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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@@ -0,0 +1,192 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#include <iomanip>
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#include <iostream>
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#include <vector>
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#include "ck/ck.hpp"
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#include "ck/library/tensor_operation_instance/gpu/grouped_convolution_forward_perlayer_quantization.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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#include "ck/tensor_operation/gpu/device/device_conv_fwd.hpp"
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#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
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using InDataType = int8_t;
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using WeiDataType = int8_t;
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using OutDataType = int8_t;
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using InLayout = ck::tensor_layout::convolution::GNHWC;
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using WeiLayout = ck::tensor_layout::convolution::GKYXC;
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using OutLayout = ck::tensor_layout::convolution::GNHWK;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ActivationOp = PassThrough;
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using OutElementOp = ck::tensor_operation::element_wise::Activation_Mul_Clamp<ActivationOp>;
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static constexpr ck::index_t NumDimSpatial = 2;
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static constexpr ck::index_t G = 1;
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static constexpr ck::index_t N = 4;
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static constexpr ck::index_t K = 64;
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static constexpr ck::index_t C = 32;
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static constexpr ck::index_t Y = 3;
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static constexpr ck::index_t X = 3;
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static constexpr ck::index_t Hi = 71;
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static constexpr ck::index_t Wi = 71;
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static constexpr ck::index_t Ho = 36;
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static constexpr ck::index_t Wo = 36;
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struct SimpleDeviceMem
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{
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SimpleDeviceMem() = delete;
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SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
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{
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(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
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}
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void* GetDeviceBuffer() { return p_mem_; }
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~SimpleDeviceMem() { (void)hipFree(p_mem_); }
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void* p_mem_;
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};
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int main(int argc, char* argv[])
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{
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std::array<ck::index_t, 5> in_lengths{G, N, C, Hi, Wi};
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std::array<ck::index_t, 5> in_strides{N * Hi * Wi * C, Hi * Wi * C, 1, Wi * C, C};
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std::array<ck::index_t, 5> weight_lengths{G, K, C, Y, X};
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std::array<ck::index_t, 5> weight_strides{K * Y * X * C, Y * X * C, 1, X * C, C};
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std::array<ck::index_t, 5> out_lengths{G, N, C, Ho, Wo};
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std::array<ck::index_t, 5> out_strides{N * Ho * Wo * C, Ho * Wo * C, 1, Wo * C, C};
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std::array<ck::index_t, 2> in_left_pad{1, 1};
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std::array<ck::index_t, 2> in_right_pad{1, 1};
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std::array<ck::index_t, 2> conv_strides{2, 2};
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std::array<ck::index_t, 2> conv_dilations{1, 1};
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SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
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SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
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SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
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using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NumDimSpatial,
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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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PassThrough,
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PassThrough,
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OutElementOp>;
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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 << "found " << op_ptrs.size() << " instances" << std::endl;
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std::string best_op_name;
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int best_op_id = -1;
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float best_avg_time = std::numeric_limits<float>::max();
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float best_gb_per_sec = 0;
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float best_tflops = 0;
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// profile device operation instances
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std::cout << "Run all instances and do timing" << std::endl;
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for(int i = 0; i < op_ptrs.size(); ++i)
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{
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auto& op_ptr = op_ptrs[i];
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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weight_lengths,
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weight_strides,
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{},
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{},
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out_lengths,
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out_strides,
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conv_strides,
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conv_dilations,
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in_left_pad,
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in_right_pad,
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PassThrough{},
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PassThrough{},
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OutElementOp{0.5f, ActivationOp{}});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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std::string op_name = op_ptr->GetTypeString();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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float avg_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
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std::size_t flop = G * 2 * N * K * C * Ho * Wo * Y * X;
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std::size_t num_bytes = G * sizeof(InDataType) * N * Hi * Wi * C +
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G * sizeof(WeiDataType) * K * Y * X * C +
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G * sizeof(OutDataType) * N * Ho * Wo * K;
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float tflops = static_cast<float>(flop) / 1.E9 / avg_time;
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float gb_per_sec = num_bytes / 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_id = i;
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best_op_name = op_name;
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best_avg_time = avg_time;
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best_gb_per_sec = gb_per_sec;
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best_tflops = tflops;
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}
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}
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else
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{
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std::cout << op_name << " does not support this problem" << std::endl;
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}
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}
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std::cout << "Best Perf: " << std::setw(10) << best_avg_time << " ms, " << best_tflops
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<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
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// run the best intance
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{
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auto& op_ptr = op_ptrs[best_op_id];
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std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
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<< std::endl;
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auto argument_ptr = op_ptr->MakeArgumentPointer(in.GetDeviceBuffer(),
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wei.GetDeviceBuffer(),
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{},
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out.GetDeviceBuffer(),
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in_lengths,
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in_strides,
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weight_lengths,
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weight_strides,
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{},
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{},
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out_lengths,
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out_strides,
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conv_strides,
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conv_dilations,
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in_left_pad,
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in_right_pad,
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PassThrough{},
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PassThrough{},
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OutElementOp{0.5f, ActivationOp{}});
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auto invoker_ptr = op_ptr->MakeInvokerPointer();
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if(op_ptr->IsSupportedArgument(argument_ptr.get()))
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{
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invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
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}
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std::cout << "Done" << std::endl;
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}
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return 0;
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}
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