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[CK_Tile] Refactor MOE Sorting and Smoothquant ctests to gtests (#2596)
* refactor moe_sorting ctests to use gtest framework * Refactor ctests for smoothquant to gtests * fix clang format to use version 18 * Print local_eid in MOE sorting gtests * Remove extra space in smoothquant output
This commit is contained in:
Emily Martins
181
test/ck_tile/smoothquant/test_smoothquant_util.hpp
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181
test/ck_tile/smoothquant/test_smoothquant_util.hpp
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// Copyright © Advanced Micro Devices, Inc., or its affiliates.
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// SPDX-License-Identifier: MIT
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#include "ck_tile/host.hpp"
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#include "smoothquant.hpp"
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#include <cstring>
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// different threshold for different dtype
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template <typename DataType>
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auto get_elimit()
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{
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double rtol = 1e-5;
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double atol = 1e-5;
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return ck_tile::make_tuple(rtol, atol);
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}
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template <>
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auto get_elimit<ck_tile::bf16_t>()
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{
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double rtol = 1e-5;
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double atol = 1e-5;
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return ck_tile::make_tuple(rtol, atol);
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}
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template <>
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auto get_elimit<ck_tile::int8_t>()
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{
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// due to rounding, int8 quantization might have 1 abs error
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double rtol = 1;
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double atol = 1;
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return ck_tile::make_tuple(rtol, atol);
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}
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template <typename Tuple>
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class TestCkTileSmoothquant : public ::testing::Test
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{
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protected:
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using DataType = std::tuple_element_t<0, Tuple>;
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void Run(ck_tile::index_t m,
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ck_tile::index_t n,
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ck_tile::index_t x_stride = -1,
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ck_tile::index_t y_stride = -1)
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{
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if(x_stride < 0)
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x_stride = n;
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if(y_stride < 0)
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y_stride = n;
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assert(x_stride >= n);
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using TypeConfig = SmoothquantTypeConfig<DataType>;
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using XDataType = typename TypeConfig::XDataType;
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using SmoothScaleDataType = typename TypeConfig::SmoothScaleDataType;
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using YScaleDataType = typename TypeConfig::YScaleDataType;
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using QYDataType = typename TypeConfig::QYDataType;
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using ComputeDataType = typename TypeConfig::ComputeDataType;
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// host verify
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ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
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ck_tile::HostTensor<SmoothScaleDataType> smscale_host({n});
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ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
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ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
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ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
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ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
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ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
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ck_tile::FillUniformDistribution<SmoothScaleDataType>{1e-3, .5f}(smscale_host);
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ck_tile::DeviceMem x_buf(x_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem smscale_buf(smscale_host.get_element_space_size_in_bytes());
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ck_tile::DeviceMem yscale_buf(yscale_host_dev.get_element_space_size_in_bytes());
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ck_tile::DeviceMem qy_buf(qy_host_dev.get_element_space_size_in_bytes());
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x_buf.ToDevice(x_host.data());
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smscale_buf.ToDevice(smscale_host.data());
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std::cout << "m:" << m << ", n:" << n << ", x_stride:" << x_stride
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<< ", y_stride:" << y_stride << std::flush;
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smoothquant_args args{x_buf.GetDeviceBuffer(),
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smscale_buf.GetDeviceBuffer(),
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yscale_buf.GetDeviceBuffer(),
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qy_buf.GetDeviceBuffer(),
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m,
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n,
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x_stride,
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y_stride};
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smoothquant<DataType>(args, ck_tile::stream_config{nullptr, false});
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bool pass = true;
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using YDataType = ComputeDataType;
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ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
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// smooth outlier
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{
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auto f = [&](auto n_) {
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auto v_smscale = ck_tile::type_convert<ComputeDataType>(smscale_host(n_));
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for(int m_ = 0; m_ < m; ++m_)
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{
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auto v_x = ck_tile::type_convert<ComputeDataType>(x_host(m_, n_));
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y_host(m_, n_) = v_x * v_smscale;
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}
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};
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ck_tile::make_ParallelTensorFunctor(f, smscale_host.get_element_space_size())(
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std::thread::hardware_concurrency());
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}
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// yscale
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{
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ck_tile::HostTensor<YDataType> y_rowwise_amax_host({m});
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using ReduceAmax = ck_tile::ReduceOp::AbsMax;
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ck_tile::reference_reduce<ComputeDataType, ComputeDataType, YDataType>(
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y_host, y_rowwise_amax_host, ReduceAmax{});
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auto op = [](const auto& v0) {
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return v0 /
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ck_tile::type_convert<ComputeDataType>(ck_tile::numeric<QYDataType>::max());
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};
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ck_tile::reference_unary_elementwise<YDataType, YScaleDataType, ComputeDataType>(
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y_rowwise_amax_host, yscale_host_ref, op);
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yscale_buf.FromDevice(yscale_host_dev.mData.data());
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auto [rtol, atol] = get_elimit<YScaleDataType>();
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pass &= ck_tile::check_err(yscale_host_dev,
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yscale_host_ref,
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std::string("yscale Error: Incorrect results!"),
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rtol,
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atol);
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}
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// rowwise quantization
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{
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ck_tile::reference_rowwise_quantization2d<YDataType, YScaleDataType, QYDataType>(
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y_host, yscale_host_ref, qy_host_ref);
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qy_buf.FromDevice(qy_host_dev.data());
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auto [rtol, atol] = get_elimit<QYDataType>();
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if(y_stride == n)
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{
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pass = ck_tile::check_err(qy_host_dev,
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qy_host_ref,
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std::string("qy Error: Incorrect results!"),
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rtol,
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atol);
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}
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else
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{
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for(int i_r = 0; i_r < m; i_r++)
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{
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std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
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qy_host_dev.begin() + i_r * y_stride +
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n);
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std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
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qy_host_ref.begin() + i_r * y_stride +
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n);
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pass &= ck_tile::check_err(qy_host_dev_row,
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qy_host_ref_row,
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std::string("qy[") + std::to_string(i_r) +
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std::string("] Error: Incorrect results!"),
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rtol,
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atol);
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}
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}
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}
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std::cout << ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
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EXPECT_TRUE(pass);
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}
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};
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