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Group norm (#417)
* Add groupnorm example by layernorm 1. Reference is not ready 2. shape of gamma and beta need to be fix * Let shape of gamma and beta can be same as x * Modify test, instance and client example * [What] Fix bug of layernorm for greater than 2 dimension. [Why] We need to get upper length from merge transform instead of embed transform. * Add reference for groupnorm * Fuse sigmoid after groupnorm * [What] Rename original layernorm into layernorm2d [Why] Prepare to add groupnorm using layernorm5d * clang-format * Add groupnorm test * Refine error message * Add groupnorm ckProfiler * Test groupnorm kernel from device_instance * update example * upadte profiler * Fix test naming * Fix argc number * Move descriptor and sweeponce to argument for quick debugging Co-authored-by: Chao Liu <chao.liu2@amd.com>
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rocking5566
179
test/layernorm/test_layernorm2d_util.hpp
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179
test/layernorm/test_layernorm2d_util.hpp
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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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#pragma once
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#include <vector>
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#include <iostream>
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#include <gtest/gtest.h>
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#include "ck/ck.hpp"
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#include "ck/utility/number.hpp"
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#include "ck/tensor_operation/gpu/device/device_layernorm_impl.hpp"
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#include "ck/library/utility/check_err.hpp"
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/device_memory.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
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namespace ck {
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template <typename Range>
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std::string serialize_range(const Range& range)
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{
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std::stringstream ss;
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for(auto& r : range)
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{
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ss << r << ", ";
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}
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std::string str = ss.str();
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return std::string(str.begin(), str.end() - 2);
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}
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template <typename Tuple>
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class TestLayernorm2d : public ::testing::Test
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{
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protected:
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using XDataType = std::tuple_element_t<0, Tuple>;
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using GammaDataType = std::tuple_element_t<1, Tuple>;
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using BetaDataType = std::tuple_element_t<2, Tuple>;
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using AccDataType = std::tuple_element_t<3, Tuple>;
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using YDataType = std::tuple_element_t<4, Tuple>;
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static constexpr index_t Rank = std::tuple_element_t<5, Tuple>{}.value;
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static constexpr index_t NumReduceDim = std::tuple_element_t<6, Tuple>{}.value;
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static constexpr index_t BlockSize = std::tuple_element_t<7, Tuple>{}.value;
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static constexpr index_t MThreadClusterSize = std::tuple_element_t<8, Tuple>{}.value;
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static constexpr index_t KThreadClusterSize = std::tuple_element_t<9, Tuple>{}.value;
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static constexpr index_t MThreadSliceSize = std::tuple_element_t<10, Tuple>{}.value;
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static constexpr index_t KThreadSliceSize = std::tuple_element_t<11, Tuple>{}.value;
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static constexpr index_t XYSrcVectorDim = std::tuple_element_t<12, Tuple>{}.value;
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static constexpr index_t XSrcVectorSize = std::tuple_element_t<13, Tuple>{}.value;
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static constexpr index_t GammaSrcVectorDim = std::tuple_element_t<14, Tuple>{}.value;
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static constexpr index_t GammaSrcVectorSize = std::tuple_element_t<15, Tuple>{}.value;
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static constexpr index_t BetaSrcVectorDim = std::tuple_element_t<16, Tuple>{}.value;
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static constexpr index_t BetaSrcVectorSize = std::tuple_element_t<17, Tuple>{}.value;
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static constexpr index_t YDstVectorSize = std::tuple_element_t<18, Tuple>{}.value;
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using PassThrough = ck::tensor_operation::element_wise::PassThrough;
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using ReferenceInstance = tensor_operation::host::ReferenceLayernorm<XDataType,
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GammaDataType,
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BetaDataType,
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YDataType,
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AccDataType,
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PassThrough,
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Rank,
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NumReduceDim>;
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using DeviceInstance = tensor_operation::device::DeviceLayernormImpl<XDataType,
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GammaDataType,
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BetaDataType,
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AccDataType,
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YDataType,
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PassThrough,
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Rank,
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NumReduceDim,
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BlockSize,
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MThreadClusterSize,
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KThreadClusterSize,
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MThreadSliceSize,
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KThreadSliceSize,
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XYSrcVectorDim,
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XSrcVectorSize,
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GammaSrcVectorDim,
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GammaSrcVectorSize,
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BetaSrcVectorDim,
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BetaSrcVectorSize,
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YDstVectorSize>;
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TestLayernorm2d() : ref_instance_invoker_(ReferenceInstance{}.MakeInvoker()) {}
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void RunSingle(const std::vector<index_t>& lengths,
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const std::vector<index_t>& reduceDims,
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const std::vector<index_t>& GammaLength,
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const std::vector<index_t>& GammaStride,
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const std::vector<index_t>& BetaLength,
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const std::vector<index_t>& BetaStride)
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{
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Tensor<XDataType> x(lengths);
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Tensor<GammaDataType> gamma(GammaLength);
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Tensor<BetaDataType> beta(BetaLength);
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Tensor<YDataType> y(lengths);
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Tensor<YDataType> y_ref(lengths);
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x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1.0});
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gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
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beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
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DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
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DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpaceSize());
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DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpaceSize());
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DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpaceSize());
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x_dev.ToDevice(x.mData.data());
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gamma_dev.ToDevice(gamma.mData.data());
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beta_dev.ToDevice(beta.mData.data());
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auto device_instance = DeviceInstance{};
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auto argument_ptr = device_instance.MakeArgumentPointer(
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lengths,
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std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
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GammaStride,
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BetaStride,
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std::vector<ck::index_t>{y.mDesc.GetStrides().begin(), y.mDesc.GetStrides().end()},
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reduceDims,
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1e-4,
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x_dev.GetDeviceBuffer(),
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gamma_dev.GetDeviceBuffer(),
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beta_dev.GetDeviceBuffer(),
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y_dev.GetDeviceBuffer(),
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PassThrough{});
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if(!device_instance.IsSupportedArgument(argument_ptr.get()))
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{
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return;
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}
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auto invoker_ptr = device_instance.MakeInvokerPointer();
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invoker_ptr->Run(argument_ptr.get());
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ref_instance_invoker_.Run(
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{x, gamma, beta, y_ref, PassThrough{}, lengths, reduceDims, 1e-4});
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y_dev.FromDevice(y.mData.data());
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bool pass;
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if(std::is_same<XDataType, int8_t>::value)
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{
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EXPECT_TRUE(pass = ck::utils::check_err(
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y.mData, y_ref.mData, "Error: Incorrect results!", 0, 1));
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}
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else
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{
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EXPECT_TRUE(pass = ck::utils::check_err(
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y.mData, y_ref.mData, "Error: Incorrect results d1", 1e-3, 1e-3));
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}
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if(!pass)
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{
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FAIL() << "Failure in input lengths = [" << serialize_range(lengths) << "], "
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<< "reduce dim = [" << serialize_range(reduceDims) << "].";
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}
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}
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void Run()
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{
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std::vector<std::vector<index_t>> lengths = {
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{4, 256}, {8, 511}, {9, 1032}, {4, 2048}, {1, 8192}, {4000, 2000}};
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for(auto length : lengths)
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{
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this->RunSingle(length, {1}, {length[1]}, {0, 1}, {length[1]}, {0, 1});
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}
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}
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typename ReferenceInstance::Invoker ref_instance_invoker_;
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};
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} // namespace ck
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