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Standalone layernorm (#315)

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* Implement layernorm kernel and deviceOp

* verify gpu kernel with host code

* 1. Separate gamma aand beta from affine
2. Check if argument is valid

* clean

* Sync the naming

* Support sweep once mode if we can put k dimension data inside one block

* [What] Get length from upper length.
[Why] if we get length directly, we may get length after padding.

* We only use one block in K dimension.
Hence, we can simplify the indexing of global R/W.

* Use 1d descriptor for gamma and beta

* Add accElementwiseOp

* Extract layernorm host code

* Support different YVectorDim in GridwiseLayernorm

* Rename XSrcVectorDim to XYSrcVectorDim. Because we use same parameter in deviceOp

* Gamma and beta can share the VGPR.

* Add test for fp32 and fp16

* Fix bug of concurrency and add test case which may fail orignally

* Propagate NaN for layernorm

Co-authored-by: Chao Liu <chao.liu2@amd.com>

[ROCm/composable_kernel commit: 7f21662089]
This commit is contained in:
rocking5566
2022-07-14 00:16:14 +08:00
committed by GitHub
parent 554fec709b
commit 88e9bfd4da
13 changed files with 1291 additions and 1 deletions
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1
test/CMakeLists.txt
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@@ -48,3 +48,4 @@ add_subdirectory(convnd_bwd_weight)
add_subdirectory(convnd_bwd_data)
add_subdirectory(block_to_ctile_map)
add_subdirectory(softmax)
add_subdirectory(layernorm)

8
test/layernorm/CMakeLists.txt Normal file
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@@ -0,0 +1,8 @@
add_custom_target(test_layernorm)
add_gtest_executable(test_layernorm_fp32 test_layernorm_fp32.cpp)
add_gtest_executable(test_layernorm_fp16 test_layernorm_fp16.cpp)
target_link_libraries(test_layernorm_fp32 PRIVATE host_tensor)
target_link_libraries(test_layernorm_fp16 PRIVATE host_tensor)
add_dependencies(test_layernorm test_layernorm_fp32)
add_dependencies(test_layernorm test_layernorm_fp16)

29
test/layernorm/test_layernorm_fp16.cpp Normal file
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@@ -0,0 +1,29 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_layernorm_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
template <typename Tuple>
class TestLayernormFP16 : public ck::TestLayernorm<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, , GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<8>, I<32>, I<2>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<4>, I<64>, I<2>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<2>, I<128>, I<2>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, float, ck::half_t, I<2>, I<1>, I<256>, I<1>, I<256>, I<2>, I<8>, I<1>, I<8>, I<8>, I<8>, I<8>>
>;
// clang-format on
TYPED_TEST_SUITE(TestLayernormFP16, KernelTypes);
TYPED_TEST(TestLayernormFP16, Test_FP16) { this->Run(); }

29
test/layernorm/test_layernorm_fp32.cpp Normal file
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@@ -0,0 +1,29 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include "gtest/gtest.h"
#include "test_layernorm_util.hpp"
template <ck::index_t N>
using I = ck::Number<N>;
template <typename Tuple>
class TestLayernormFP32 : public ck::TestLayernorm<Tuple>
{
};
// clang-format off
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, AccDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, , GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<8>, I<32>, I<1>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<8>, I<32>, I<2>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<4>, I<64>, I<1>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<4>, I<64>, I<2>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<2>, I<128>, I<1>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<2>, I<128>, I<2>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<1>, I<256>, I<1>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>,
std::tuple<float, float, float, float, float, I<2>, I<1>, I<256>, I<1>, I<256>, I<2>, I<8>, I<1>, I<4>, I<4>, I<4>, I<4>>
>;
// clang-format on
TYPED_TEST_SUITE(TestLayernormFP32, KernelTypes);
TYPED_TEST(TestLayernormFP32, Test_FP32) { this->Run(); }

178
test/layernorm/test_layernorm_util.hpp Normal file
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@@ -0,0 +1,178 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <vector>
#include <iostream>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/utility/number.hpp"
#include "ck/tensor_operation/gpu/device/device_layernorm.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_layernorm.hpp"
namespace ck {
template <typename Range>
std::string serialize_range(const Range& range)
{
std::stringstream ss;
for(auto& r : range)
{
ss << r << ", ";
}
std::string str = ss.str();
return std::string(str.begin(), str.end() - 2);
}
template <typename Tuple>
class TestLayernorm : public ::testing::Test
{
protected:
using XDataType = std::tuple_element_t<0, Tuple>;
using GammaDataType = std::tuple_element_t<1, Tuple>;
using BetaDataType = std::tuple_element_t<2, Tuple>;
using AccDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
static constexpr index_t Rank = std::tuple_element_t<5, Tuple>{}.value;
static constexpr index_t NumReduceDim = std::tuple_element_t<6, Tuple>{}.value;
static constexpr index_t BlockSize = std::tuple_element_t<7, Tuple>{}.value;
static constexpr index_t MThreadClusterSize = std::tuple_element_t<8, Tuple>{}.value;
static constexpr index_t KThreadClusterSize = std::tuple_element_t<9, Tuple>{}.value;
static constexpr index_t MThreadSliceSize = std::tuple_element_t<10, Tuple>{}.value;
static constexpr index_t KThreadSliceSize = std::tuple_element_t<11, Tuple>{}.value;
static constexpr index_t XYSrcVectorDim = std::tuple_element_t<12, Tuple>{}.value;
static constexpr index_t XSrcVectorSize = std::tuple_element_t<13, Tuple>{}.value;
static constexpr index_t GammaSrcVectorSize = std::tuple_element_t<14, Tuple>{}.value;
static constexpr index_t BetaSrcVectorSize = std::tuple_element_t<15, Tuple>{}.value;
static constexpr index_t YDstVectorSize = std::tuple_element_t<16, Tuple>{}.value;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ReferenceInstance = tensor_operation::host::ReferenceLayernorm<XDataType,
GammaDataType,
BetaDataType,
YDataType,
AccDataType,
PassThrough,
Rank,
NumReduceDim>;
using DeviceInstance = tensor_operation::device::DeviceLayernorm<XDataType,
GammaDataType,
BetaDataType,
AccDataType,
YDataType,
PassThrough,
Rank,
NumReduceDim,
BlockSize,
MThreadClusterSize,
KThreadClusterSize,
MThreadSliceSize,
KThreadSliceSize,
XYSrcVectorDim,
XSrcVectorSize,
GammaSrcVectorSize,
BetaSrcVectorSize,
YDstVectorSize>;
TestLayernorm() : ref_instance_invoker_(ReferenceInstance{}.MakeInvoker()) {}
void RunSingle(std::vector<index_t> lengths, std::vector<index_t> reduceDims)
{
std::vector<index_t> reduceLength(reduceDims.size());
for(int i = 0; i < NumReduceDim; ++i)
{
reduceLength[i] = lengths[reduceDims[i]];
}
Tensor<XDataType> x(lengths);
Tensor<GammaDataType> gamma(reduceLength);
Tensor<BetaDataType> beta(reduceLength);
Tensor<YDataType> y(lengths);
Tensor<YDataType> y_ref(lengths);
x.GenerateTensorValue(GeneratorTensor_3<XDataType>{0, 1.0});
gamma.GenerateTensorValue(GeneratorTensor_3<GammaDataType>{0.0, 1.0});
beta.GenerateTensorValue(GeneratorTensor_3<BetaDataType>{0.0, 1.0});
DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpace());
DeviceMem gamma_dev(sizeof(GammaDataType) * gamma.mDesc.GetElementSpace());
DeviceMem beta_dev(sizeof(BetaDataType) * beta.mDesc.GetElementSpace());
DeviceMem y_dev(sizeof(YDataType) * y.mDesc.GetElementSpace());
x_dev.ToDevice(x.mData.data());
gamma_dev.ToDevice(gamma.mData.data());
beta_dev.ToDevice(beta.mData.data());
auto device_instance = DeviceInstance{};
auto argument_ptr = device_instance.MakeArgumentPointer(
lengths,
std::vector<ck::index_t>{x.mDesc.GetStrides().begin(), x.mDesc.GetStrides().end()},
std::vector<ck::index_t>{gamma.mDesc.GetStrides().begin(),
gamma.mDesc.GetStrides().end()},
std::vector<ck::index_t>{beta.mDesc.GetStrides().begin(),
beta.mDesc.GetStrides().end()},
reduceDims,
1e-4,
x_dev.GetDeviceBuffer(),
gamma_dev.GetDeviceBuffer(),
beta_dev.GetDeviceBuffer(),
y_dev.GetDeviceBuffer(),
PassThrough{});
if(!device_instance.IsSupportedArgument(argument_ptr.get()))
{
return;
}
auto invoker_ptr = device_instance.MakeInvokerPointer();
invoker_ptr->Run(argument_ptr.get());
ref_instance_invoker_.Run(
{x, gamma, beta, y_ref, PassThrough{}, lengths, reduceDims, 1e-4});
y_dev.FromDevice(y.mData.data());
bool pass;
if(std::is_same<XDataType, int8_t>::value)
{
EXPECT_TRUE(pass = ck::utils::check_err(
y.mData, y_ref.mData, "Error: Incorrect results!", 0, 1));
}
else
{
EXPECT_TRUE(pass = ck::utils::check_err(
y.mData, y_ref.mData, "Error: Incorrect results d1", 1e-3, 1e-3));
}
if(!pass)
{
FAIL() << "Failure in input lengths = [" << serialize_range(lengths) << "], "
<< "reduce dim = [" << serialize_range(reduceDims) << "].";
}
}
void Run()
{
for(auto length : this->lengths_)
{
this->RunSingle(length, reduceDims_[0]);
}
}
std::vector<std::vector<index_t>> lengths_ = {
{4, 256}, {8, 511}, {9, 1032}, {4, 2048}, {1, 8192}, {4000, 2000}};
std::vector<std::vector<index_t>> reduceDims_ = {{1}};
typename ReferenceInstance::Invoker ref_instance_invoker_;
};
} // namespace ck