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Commit ce72f286 authored by Jun Liu's avatar Jun Liu
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Merge branch 'amd-develop' into amd-master

parents 50320413 f30e5975
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Showing with 272 additions and 29 deletions
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp
View file @ ce72f286
......@@ -11,6 +11,7 @@
#include "ck/utility/common_header.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/host_utility/device_prop.hpp"
#include "profiler/profile_grouped_conv_bwd_weight_impl.hpp"
......@@ -33,8 +34,9 @@ class TestGroupedConvndBwdWeight : public ::testing::Test
bool skip_case(const ck::utils::conv::ConvParam& params, const ck::index_t split_k)
{
// Odd K or C values are supported only by DL kernel (only applies to fp16)
// DL kernel currently supports only `split_k=1`
// Odd K or C values are supported only by DL and WMMA
// kernels (only applies to fp16)
// DL and WMMA kernels currently support only `split_k=1`
if constexpr(std::is_same_v<InDataType, ck::half_t>)
{
if(split_k != 1 && (params.K_ % 2 != 0 || params.C_ % 2 != 0))
......@@ -53,6 +55,42 @@ class TestGroupedConvndBwdWeight : public ::testing::Test
}
}
const bool is_navi3x = ck::get_device_name() == "gfx1100" ||
ck::get_device_name() == "gfx1101" ||
ck::get_device_name() == "gfx1102";
if(is_navi3x)
{
// on navi3x only support for 3d is implemented
if constexpr(NDimSpatial{} != 3)
{
return true;
}
// on navi3x only support for i8 and fp16 is implemented
if constexpr(!((std::is_same_v<InDataType, int8_t> &&
std::is_same_v<WeiDataType, int8_t> &&
std::is_same_v<OutDataType, int8_t>) ||
(std::is_same_v<InDataType, ck::half_t> &&
std::is_same_v<WeiDataType, ck::half_t> &&
std::is_same_v<OutDataType, ck::half_t>)))
{
return true;
}
// WMMA kernel is only supported for split_k=1
if(split_k != 1)
{
return true;
}
}
else
{
// support for i8 is only implemented on navi3x
if constexpr(std::is_same_v<InDataType, int8_t> &&
std::is_same_v<WeiDataType, int8_t> && std::is_same_v<OutDataType, int8_t>)
{
return true;
}
}
return false;
}
......@@ -120,9 +158,11 @@ using KernelTypes3d = ::testing::Types<
std::tuple<float, float, float, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<int8_t, int8_t, int8_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
std::tuple<float, float, float, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
std::tuple<ck::half_t, ck::half_t, ck::half_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
std::tuple<ck::bhalf_t, float, ck::bhalf_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>>;
std::tuple<ck::bhalf_t, float, ck::bhalf_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
std::tuple<int8_t, int8_t, int8_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>>;
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight1d, KernelTypes1d);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeight2d, KernelTypes2d);
......
test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight_interface_wmma.cpp 0 → 100644
View file @ ce72f286
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/convolution_backward_weight_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_bwd_weight_wmma_cshuffle.hpp"
#include "ck/library/utility/convolution_parameter.hpp"
#include "ck/library/utility/algorithm.hpp"
#include "ck/library/utility/convolution_host_tensor_descriptor_helper.hpp"
#include <gtest/gtest.h>
using F16 = ck::half_t;
using F32 = float;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using ConvolutionBackwardWeightSpecialization =
ck::tensor_operation::device::ConvolutionBackwardWeightSpecialization;
static constexpr auto ConvBwdWeightDefault = ConvolutionBackwardWeightSpecialization::Default;
static constexpr auto Filter1x1Stride1Pad0 =
ConvolutionBackwardWeightSpecialization::Filter1x1Stride1Pad0;
template <typename Tuple, ConvolutionBackwardWeightSpecialization ConvSpec>
class TestGroupedConvndBwdWeight : public ::testing::Test
{
protected:
using OutLayout = std::tuple_element_t<0, Tuple>;
using WeiLayout = std::tuple_element_t<1, Tuple>;
using InLayout = std::tuple_element_t<2, Tuple>;
static constexpr ck::index_t NDimSpatial = std::tuple_element_t<3, Tuple>{};
// clang-format off
using GroupedConvBwdWeightDeviceInstance = ck::tensor_operation::device::DeviceGroupedConvBwdWeight_Wmma_CShuffle
//| NumDim| A| B| C| AData| BData| CData| AccData| A| B| C| ConvForward| Block| MPer| NPer| KPer| K1| MPer| NPer| MRepeat| NRepeat| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//| Spatial| Layout| Layout| Layout| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Specialization| Size| Block| Block| Block| | WMMA| WMMA| | | ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MRepeatPerWave| NRepeatPerWave| _MBlock_MPerBlock| ScalarPerVector|
//| | | | | | | | | Operation| Operation| Operation| | | | | | | | | | | Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NPerBlock| _NPerBlock|
//| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
<NDimSpatial, InLayout, WeiLayout, OutLayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 128, 128, 128, 8, 8, 16, 16, 4, 4, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>;
// clang-format on
ck::utils::conv::ConvParam conv_param;
template <ck::index_t SplitK>
bool Run()
{
const auto in_g_n_c_wis_desc =
ck::utils::conv::make_input_host_tensor_descriptor_g_n_c_wis_packed<InLayout>(
conv_param);
const auto wei_g_k_c_xs_desc =
ck::utils::conv::make_weight_host_tensor_descriptor_g_k_c_xs_packed<WeiLayout>(
conv_param);
const auto out_g_n_k_wos_desc =
ck::utils::conv::make_output_host_tensor_descriptor_g_n_k_wos_packed<OutLayout>(
conv_param);
std::array<ck::index_t, NDimSpatial + 3> input_lengths{};
std::array<ck::index_t, NDimSpatial + 3> filter_lengths{};
std::array<ck::index_t, NDimSpatial + 3> output_lengths{};
std::array<ck::index_t, NDimSpatial + 3> input_strides{};
std::array<ck::index_t, NDimSpatial + 3> weights_strides{};
std::array<ck::index_t, NDimSpatial + 3> output_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_strides{};
std::array<ck::index_t, NDimSpatial> conv_filter_dilations{};
std::array<ck::index_t, NDimSpatial> input_left_pads{};
std::array<ck::index_t, NDimSpatial> input_right_pads{};
auto range_copy = [](const auto& from, auto to) { std::copy(begin(from), end(from), to); };
range_copy(in_g_n_c_wis_desc.GetLengths(), begin(input_lengths));
range_copy(in_g_n_c_wis_desc.GetStrides(), begin(input_strides));
range_copy(wei_g_k_c_xs_desc.GetLengths(), begin(filter_lengths));
range_copy(wei_g_k_c_xs_desc.GetStrides(), begin(weights_strides));
range_copy(out_g_n_k_wos_desc.GetLengths(), begin(output_lengths));
range_copy(out_g_n_k_wos_desc.GetStrides(), begin(output_strides));
range_copy(conv_param.conv_filter_strides_, begin(conv_filter_strides));
range_copy(conv_param.conv_filter_dilations_, begin(conv_filter_dilations));
range_copy(conv_param.input_left_pads_, begin(input_left_pads));
range_copy(conv_param.input_right_pads_, begin(input_right_pads));
auto conv = GroupedConvBwdWeightDeviceInstance{};
auto argument = conv.MakeArgument(nullptr,
nullptr,
nullptr,
input_lengths,
input_strides,
filter_lengths,
weights_strides,
output_lengths,
output_strides,
conv_filter_strides,
conv_filter_dilations,
input_left_pads,
input_right_pads,
PassThrough{},
PassThrough{},
PassThrough{},
SplitK);
return conv.IsSupportedArgument(argument);
}
};
using namespace ck::tensor_layout::convolution;
using KernelTypes3d = ::testing::Types<std::tuple<GNDHWK, GKZYXC, GNDHWC, ck::Number<3>>,
std::tuple<NDHWGK, GKZYXC, NDHWGC, ck::Number<3>>>;
template <typename Tuple>
class TestGroupedConvndBwdWeightFilter1x13d
: public TestGroupedConvndBwdWeight<Tuple, Filter1x1Stride1Pad0>
{
};
template <typename Tuple>
class TestGroupedConvndBwdWeightDefault3d
: public TestGroupedConvndBwdWeight<Tuple, ConvBwdWeightDefault>
{
};
TYPED_TEST_SUITE(TestGroupedConvndBwdWeightFilter1x13d, KernelTypes3d);
TYPED_TEST_SUITE(TestGroupedConvndBwdWeightDefault3d, KernelTypes3d);
TYPED_TEST(TestGroupedConvndBwdWeightFilter1x13d, SpecializationCheck)
{
// Check filter 3x3x3 instead of 1x1x1
this->conv_param = {
3, 2, 4, 192, 192, {3, 3, 3}, {28, 28, 28}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
bool is_supported = this->template Run<1>();
EXPECT_FALSE(is_supported);
// Check strides 2x2x2 instead of 1x1x1
this->conv_param = {
3, 2, 4, 192, 192, {1, 1, 1}, {28, 28, 28}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
is_supported = this->template Run<1>();
EXPECT_FALSE(is_supported);
// Check with pad
this->conv_param = {
3, 2, 4, 192, 192, {1, 1, 1}, {28, 28, 28}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1}};
is_supported = this->template Run<1>();
EXPECT_FALSE(is_supported);
// Supported version
this->conv_param = {
3, 2, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
is_supported = this->template Run<1>();
EXPECT_TRUE(is_supported);
}
TYPED_TEST(TestGroupedConvndBwdWeightDefault3d, VectorLoadCheck)
{
// vector load for A
this->conv_param = {
3, 2, 128, 129, 256, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
bool is_supported = this->template Run<1>();
EXPECT_FALSE(is_supported);
// vector load for B, E, Ds
this->conv_param = {
3, 2, 128, 128, 257, {1, 1, 1}, {7, 7, 7}, {2, 2, 2}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
is_supported = this->template Run<1>();
EXPECT_FALSE(is_supported);
}
TYPED_TEST(TestGroupedConvndBwdWeightDefault3d, SplitKCheck)
{
// SplitK=1
this->conv_param = {
3, 2, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
bool is_supported = this->template Run<1>();
EXPECT_TRUE(is_supported);
// SplitK=2
this->conv_param = {
3, 2, 128, 128, 256, {1, 1, 1}, {3, 3, 3}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0}};
is_supported = this->template Run<2>();
EXPECT_FALSE(is_supported);
}
test/normalization/test_groupnorm_fp16.cpp
View file @ ce72f286
......@@ -12,11 +12,12 @@ template <typename Tuple>
class TestGroupnorm : 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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using SaveMeanInvStdDataType = std::tuple_element_t<5, Tuple>;
void Run()
{
......@@ -37,7 +38,9 @@ class TestGroupnorm : public ::testing::Test
GammaDataType,
BetaDataType,
ComputeDataType,
YDataType>(true, 2, false, false, length);
YDataType,
SaveMeanInvStdDataType,
true>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
}
......@@ -45,7 +48,7 @@ class TestGroupnorm : public ::testing::Test
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F16, F16, F16, F32, F16>>;
std::tuple<F16, F16, F16, F32, F16, F32>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
TYPED_TEST(TestGroupnorm, Test_FP16) { this->Run(); }
test/normalization/test_groupnorm_fp32.cpp
View file @ ce72f286
......@@ -12,11 +12,12 @@ template <typename Tuple>
class TestGroupnorm : 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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using SaveMeanInvStdDataType = std::tuple_element_t<5, Tuple>;
void Run()
{
......@@ -35,7 +36,9 @@ class TestGroupnorm : public ::testing::Test
GammaDataType,
BetaDataType,
ComputeDataType,
YDataType>(true, 2, false, false, length);
YDataType,
SaveMeanInvStdDataType,
true>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
}
......@@ -43,7 +46,7 @@ class TestGroupnorm : public ::testing::Test
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F32, F32, F32, F32, F32>>;
std::tuple<F32, F32, F32, F32, F32, F32>>;
TYPED_TEST_SUITE(TestGroupnorm, KernelTypes);
TYPED_TEST(TestGroupnorm, Test_FP32) { this->Run(); }
test/normalization/test_layernorm2d_fp16.cpp
View file @ ce72f286
......@@ -12,11 +12,12 @@ template <typename Tuple>
class TestLayernorm2d : 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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using SaveMeanInvStdDataType = std::tuple_element_t<5, Tuple>;
void Run()
{
......@@ -31,6 +32,8 @@ class TestLayernorm2d : public ::testing::Test
BetaDataType,
ComputeDataType,
YDataType,
SaveMeanInvStdDataType,
true,
2>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
......@@ -39,7 +42,7 @@ class TestLayernorm2d : public ::testing::Test
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F16, F16, F16, F32, F16>>;
std::tuple<F16, F16, F16, F32, F16, F32>>;
TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
TYPED_TEST(TestLayernorm2d, Test_FP16) { this->Run(); }
test/normalization/test_layernorm2d_fp32.cpp
View file @ ce72f286
......@@ -12,11 +12,12 @@ template <typename Tuple>
class TestLayernorm2d : 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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
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 ComputeDataType = std::tuple_element_t<3, Tuple>;
using YDataType = std::tuple_element_t<4, Tuple>;
using SaveMeanInvStdDataType = std::tuple_element_t<5, Tuple>;
void Run()
{
......@@ -31,6 +32,8 @@ class TestLayernorm2d : public ::testing::Test
BetaDataType,
ComputeDataType,
YDataType,
SaveMeanInvStdDataType,
true,
2>(true, 2, false, false, length);
EXPECT_TRUE(success);
}
......@@ -39,7 +42,7 @@ class TestLayernorm2d : public ::testing::Test
using KernelTypes = ::testing::Types<
// XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType>
std::tuple<F32, F32, F32, F32, F32>>;
std::tuple<F32, F32, F32, F32, F32, F32>>;
TYPED_TEST_SUITE(TestLayernorm2d, KernelTypes);
TYPED_TEST(TestLayernorm2d, Test_FP32) { this->Run(); }
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