mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-12 17:26:00 +00:00
9908a87c31
* Add placeholder test. * Initial conv bwd weight factory. * Conv builder test refactoring. * Add missing pieces to bwd weight factory. * Improve compile time erros message when no matching factory is found. * Use amcro to ensure automatic macthing between concepts are their string representations. * Improve compile time diagnostics. * Small improvements. * Improve missing member/wrong type compile-time errors. * Improve compile time diagnostics. * Concept bug fixes. * Remove debug assert. * Update algorithm signature diagnostics. * Factory bug fixes. * First functional version of bwd weight conv factory. * Refactor handing of GEMM-K batch template parameter in conv bwd weight factory. * Concept improvements. * Improve concept diagnostics. * Introduve a common size type for concepts. * Update compiletime diagnostics to use the size type. * Update conv specialization enum. * Fix fwd conv builder tests. * Fix smoke tests. * Separate bwd weigth and bwd data tests into separate targets. * Clean-up CK Tile builder tests. * Add bwd weight XDL CShuffle V3 factory. * Build conv bwd weigth v3 instances successfully. * Add instance traits for DeviceGroupedConvBwdWeight_Xdl_CShuffleV3. * Test fix. * Add instance traits for bwd weight algorithms. * Add unit tests for instance strings. * Build new instance traits unit tests but exclude WMMA for now. * Added factory for DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle. * Conv bwd weight DL factory. * Final implementation for bwd weight DL factory. * Add test for creating DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle instance. * Add factory for DeviceGroupedConvBwdWeightMultipleD_Xdl_CShuffle * Treat ref algorithm the same way as real algorithms in the dispatcher. * Refactor large tensor support and WMMA configuration. * Add factory and tests for DeviceGroupedConvBwdWeight_Wmma_CShuffleV3. * Update Readme. * Fix WMMA bwd weight tests. * Added factory and tests for DeviceGroupedConvBwdWeightTwoStage_Wmma_CShuffleV3. * Factory and tests for DeviceGroupedConvBwdWeight_Wmma_CShuffle. * Dispatching for DeviceGroupedConvBwdWeightMultipleD_Wmma_CShuffle. * Add factory for DeviceGroupedConvBwdWeightMultipleD_Wmma_CShuffleV3 * Fix DeviceGroupedConvBwdWeightMultipleD_Wmma_CShuffleV3 factory and compute types for input and output tensor in bwd weigth convs. * Fix fwd factories after refactoring. * clang-format * Move compile-time diagnostics to a separate branch. * Fix ref algorithm dispatching. * Fix smoke tests. * clang-format * Fix factory for regular WMMA conv bwd weight. * Clarify builder Readme. * Remove obsolete test file. * Fix test after merge. * clang-format * Remove the C++26 extensions. * Unify conv elementwise ops and layout definitions for fwd and bwd directions. * Remove old layout and elementwise ops. * Unify handling of conv tensor types between fwd and bwd directions. * Unify block transfer for fwd and bwd directions. Rename ThreadSliceDim to ThreadClusterRank. * Make BlockTransferDescriptor concept parametrized. Introduce a common TileTransferParameters concept for conv algorithms. * clang-format --------- Co-authored-by: Ville Pietilä <>
327 lines
19 KiB
C++
327 lines
19 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
|
// SPDX-License-Identifier: MIT
|
|
|
|
#include <gtest/gtest.h>
|
|
#include <gmock/gmock.h>
|
|
#include <concepts>
|
|
|
|
#include <ck/tensor_operation/gpu/element/element_wise_operation.hpp>
|
|
#include <ck_tile/builder/reflect/conv_traits.hpp>
|
|
#include <ck_tile/builder/reflect/instance_traits_device_grouped_conv_fwd_multiple_abd_xdl_cshuffle_v3.hpp>
|
|
#include <ck_tile/builder/reflect/instance_traits_device_grouped_conv_fwd_multiple_abd_xdl_cshuffle.hpp>
|
|
#include <ck_tile/builder/reflect/instance_traits_device_grouped_conv_fwd_multiple_d_xdl_large_tensor_cshuffle.hpp>
|
|
|
|
namespace {
|
|
|
|
using ck_tile::builder::ConvDirection;
|
|
using ck_tile::builder::DataType;
|
|
using ck_tile::builder::ElementwiseOperation;
|
|
using ck_tile::builder::PipelineScheduler;
|
|
using ck_tile::builder::PipelineVersion;
|
|
using ck_tile::builder::TensorLayout;
|
|
using ::testing::ElementsAre;
|
|
|
|
// Test fixture for ConvTraits tests
|
|
class ConvTraitsTest : public ::testing::Test
|
|
{
|
|
};
|
|
|
|
// Test ConvTraits with DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3
|
|
TEST_F(ConvTraitsTest, ConvFwdTraitsExtraction)
|
|
{
|
|
// Define a concrete instance type with specific template parameters
|
|
using DeviceInstance =
|
|
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle_V3<
|
|
2, // NDimSpatial
|
|
ck::tensor_layout::convolution::GNHWC, // ALayout
|
|
ck::tensor_layout::convolution::GKYXC, // BLayout
|
|
ck::Tuple<>, // DsLayout
|
|
ck::tensor_layout::convolution::GNHWK, // ELayout
|
|
ck::half_t, // ADataType
|
|
ck::half_t, // BDataType
|
|
float, // AccDataType
|
|
ck::half_t, // CShuffleDataType
|
|
ck::Tuple<>, // DsDataType
|
|
ck::half_t, // EDataType
|
|
ck::tensor_operation::element_wise::PassThrough, // AElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // BElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // CDEElementwiseOperation
|
|
ck::tensor_operation::device::ConvolutionForwardSpecialization::
|
|
Default, // ConvForwardSpecialization
|
|
ck::tensor_operation::device::GemmSpecialization::Default, // GemmSpec
|
|
256, // BlockSize
|
|
128, // MPerBlock
|
|
128, // NPerBlock
|
|
16, // KPerBlock
|
|
8, // AK1
|
|
8, // BK1
|
|
32, // MPerXDL
|
|
32, // NPerXDL
|
|
4, // MXdlPerWave
|
|
4, // NXdlPerWave
|
|
ck::Sequence<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder
|
|
2, // ABlockTransferSrcVectorDim
|
|
8, // ABlockTransferSrcScalarPerVector
|
|
8, // ABlockTransferDstScalarPerVector_AK1
|
|
1, // ABlockLdsExtraM
|
|
ck::Sequence<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder
|
|
2, // BBlockTransferSrcVectorDim
|
|
8, // BBlockTransferSrcScalarPerVector
|
|
8, // BBlockTransferDstScalarPerVector_BK1
|
|
1, // BBlockLdsExtraN
|
|
1, // CShuffleMXdlPerWavePerShuffle
|
|
1, // CShuffleNXdlPerWavePerShuffle
|
|
ck::Sequence<1,
|
|
32,
|
|
1,
|
|
8>, // CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
|
|
8, // CDEBlockTransferScalarPerVector_NPerBlock
|
|
ck::BlockGemmPipelineScheduler::Intrawave, // BlkGemmPipeSched
|
|
ck::BlockGemmPipelineVersion::v1, // BlkGemmPipelineVer
|
|
ck::half_t, // AComputeDataType
|
|
ck::half_t, // BComputeDataType
|
|
false>; // DirectLoad
|
|
|
|
// Use ConvTraits to extract compile-time information
|
|
using Traits = ck_tile::reflect::conv::ConvTraits<DeviceInstance>;
|
|
|
|
// Verify signature information
|
|
EXPECT_EQ(Traits::spatial_dim, 2);
|
|
EXPECT_EQ(Traits::direction, ConvDirection::FORWARD);
|
|
EXPECT_THAT(Traits::layout,
|
|
ElementsAre(TensorLayout::GNHWC, TensorLayout::GKYXC, TensorLayout::GNHWK));
|
|
EXPECT_EQ(Traits::data_type, DataType::FP16);
|
|
EXPECT_EQ(Traits::input_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::weight_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::output_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
|
|
// Verify specializations
|
|
EXPECT_EQ(Traits::gemm_padding, ck_tile::builder::GemmPadding::DEFAULT);
|
|
EXPECT_EQ(Traits::conv_specialization, ck_tile::builder::ConvSpecialization::DEFAULT);
|
|
|
|
// Verify algorithm information
|
|
EXPECT_EQ(Traits::thread_block_size, 256);
|
|
|
|
// Verify tile dimensions
|
|
EXPECT_EQ(Traits::tile_dims.m, 128);
|
|
EXPECT_EQ(Traits::tile_dims.n, 128);
|
|
EXPECT_EQ(Traits::tile_dims.k, 16);
|
|
|
|
// Verify A tile transfer info
|
|
EXPECT_EQ(Traits::a_tile_transfer.tile_dimensions.k0, 2);
|
|
EXPECT_EQ(Traits::a_tile_transfer.tile_dimensions.m_or_n, 128);
|
|
EXPECT_EQ(Traits::a_tile_transfer.tile_dimensions.k1, 8);
|
|
EXPECT_EQ(Traits::a_tile_transfer.transfer_params.k1, 8);
|
|
EXPECT_THAT(Traits::a_tile_transfer.transfer_params.thread_cluster_dims, ElementsAre(4, 64, 1));
|
|
EXPECT_THAT(Traits::a_tile_transfer.transfer_params.thread_cluster_order, ElementsAre(1, 0, 2));
|
|
EXPECT_THAT(Traits::a_tile_transfer.transfer_params.src_access_order, ElementsAre(1, 0, 2));
|
|
EXPECT_EQ(Traits::a_tile_transfer.transfer_params.src_vector_dim, 2);
|
|
EXPECT_EQ(Traits::a_tile_transfer.transfer_params.src_scalar_per_vector, 8);
|
|
EXPECT_EQ(Traits::a_tile_transfer.transfer_params.dst_scalar_per_vector_k1, 8);
|
|
EXPECT_TRUE(Traits::a_tile_transfer.transfer_params.lds_padding);
|
|
|
|
// Verify B tile transfer info
|
|
EXPECT_EQ(Traits::b_tile_transfer.tile_dimensions.k0, 2);
|
|
EXPECT_EQ(Traits::b_tile_transfer.tile_dimensions.m_or_n, 128);
|
|
EXPECT_EQ(Traits::b_tile_transfer.tile_dimensions.k1, 8);
|
|
EXPECT_EQ(Traits::b_tile_transfer.transfer_params.k1, 8);
|
|
EXPECT_THAT(Traits::b_tile_transfer.transfer_params.thread_cluster_dims, ElementsAre(4, 64, 1));
|
|
EXPECT_THAT(Traits::b_tile_transfer.transfer_params.thread_cluster_order, ElementsAre(1, 0, 2));
|
|
EXPECT_THAT(Traits::b_tile_transfer.transfer_params.src_access_order, ElementsAre(1, 0, 2));
|
|
EXPECT_EQ(Traits::b_tile_transfer.transfer_params.src_vector_dim, 2);
|
|
EXPECT_EQ(Traits::b_tile_transfer.transfer_params.src_scalar_per_vector, 8);
|
|
EXPECT_EQ(Traits::b_tile_transfer.transfer_params.dst_scalar_per_vector_k1, 8);
|
|
EXPECT_TRUE(Traits::b_tile_transfer.transfer_params.lds_padding);
|
|
|
|
// Verify warp GEMM params
|
|
EXPECT_EQ(Traits::warp_gemm.gemm_m, 32);
|
|
EXPECT_EQ(Traits::warp_gemm.gemm_n, 32);
|
|
EXPECT_EQ(Traits::warp_gemm.m_iter, 4);
|
|
EXPECT_EQ(Traits::warp_gemm.n_iter, 4);
|
|
|
|
// Verify output tile transfer info
|
|
EXPECT_EQ(Traits::c_tile_transfer.shuffle_params.m_gemms_per_shuffle, 1);
|
|
EXPECT_EQ(Traits::c_tile_transfer.shuffle_params.n_gemms_per_shuffle, 1);
|
|
EXPECT_THAT(Traits::c_tile_transfer.thread_cluster_dims, ElementsAre(1, 32, 1, 8));
|
|
EXPECT_EQ(Traits::c_tile_transfer.scalar_per_vector, 8);
|
|
|
|
// Verify pipeline configuration
|
|
EXPECT_EQ(Traits::pipeline_scheduler, PipelineScheduler::INTRAWAVE);
|
|
EXPECT_EQ(Traits::pipeline_version, PipelineVersion::V1);
|
|
}
|
|
|
|
// Test ConvTraits with DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle
|
|
TEST_F(ConvTraitsTest, ConvFwdBaseTraitsExtraction)
|
|
{
|
|
// Define a concrete instance type with specific template parameters
|
|
using DeviceInstance =
|
|
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleABD_Xdl_CShuffle<
|
|
2, // NDimSpatial
|
|
ck::tensor_layout::convolution::GNHWC, // ALayout
|
|
ck::tensor_layout::convolution::GKYXC, // BLayout
|
|
ck::Tuple<>, // DsLayout
|
|
ck::tensor_layout::convolution::GNHWK, // ELayout
|
|
ck::half_t, // ADataType
|
|
ck::half_t, // BDataType
|
|
float, // AccDataType
|
|
ck::half_t, // CShuffleDataType
|
|
ck::Tuple<>, // DsDataType
|
|
ck::half_t, // EDataType
|
|
ck::tensor_operation::element_wise::PassThrough, // AElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // BElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // CDEElementwiseOperation
|
|
ck::tensor_operation::device::ConvolutionForwardSpecialization::
|
|
Default, // ConvForwardSpecialization
|
|
ck::tensor_operation::device::GemmSpecialization::Default, // GemmSpec
|
|
1, // NumGemmKPrefetchStage
|
|
256, // BlockSize
|
|
128, // MPerBlock
|
|
128, // NPerBlock
|
|
16, // KPerBlock
|
|
8, // AK1
|
|
8, // BK1
|
|
32, // MPerXDL
|
|
32, // NPerXDL
|
|
4, // MXdlPerWave
|
|
4, // NXdlPerWave
|
|
ck::Sequence<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder
|
|
2, // ABlockTransferSrcVectorDim
|
|
8, // ABlockTransferSrcScalarPerVector
|
|
8, // ABlockTransferDstScalarPerVector_AK1
|
|
1, // ABlockLdsExtraM
|
|
ck::Sequence<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder
|
|
2, // BBlockTransferSrcVectorDim
|
|
8, // BBlockTransferSrcScalarPerVector
|
|
8, // BBlockTransferDstScalarPerVector_BK1
|
|
1, // BBlockLdsExtraN
|
|
1, // CShuffleMXdlPerWavePerShuffle
|
|
1, // CShuffleNXdlPerWavePerShuffle
|
|
ck::Sequence<1,
|
|
32,
|
|
1,
|
|
8>, // CDEBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
|
|
8, // CDEBlockTransferScalarPerVector_NPerBlock
|
|
ck::half_t, // AComputeDataType
|
|
ck::half_t, // BComputeDataType
|
|
ck::LoopScheduler::Default, // LoopSched
|
|
1>; // NumGroupsToMerge
|
|
|
|
// Use ConvTraits to extract compile-time information
|
|
using Traits = ck_tile::reflect::conv::ConvTraits<DeviceInstance>;
|
|
|
|
// Verify signature information
|
|
EXPECT_EQ(Traits::spatial_dim, 2);
|
|
EXPECT_EQ(Traits::direction, ConvDirection::FORWARD);
|
|
EXPECT_THAT(Traits::layout,
|
|
ElementsAre(TensorLayout::GNHWC, TensorLayout::GKYXC, TensorLayout::GNHWK));
|
|
EXPECT_EQ(Traits::data_type, DataType::FP16);
|
|
EXPECT_EQ(Traits::input_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::weight_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::output_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
|
|
// Verify specializations
|
|
EXPECT_EQ(Traits::gemm_padding, ck_tile::builder::GemmPadding::DEFAULT);
|
|
EXPECT_EQ(Traits::conv_specialization, ck_tile::builder::ConvSpecialization::DEFAULT);
|
|
|
|
// Verify algorithm information
|
|
EXPECT_EQ(Traits::thread_block_size, 256);
|
|
|
|
// Verify tile dimensions
|
|
EXPECT_EQ(Traits::tile_dims.m, 128);
|
|
EXPECT_EQ(Traits::tile_dims.n, 128);
|
|
EXPECT_EQ(Traits::tile_dims.k, 16);
|
|
}
|
|
// Test ConvTraits with DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor
|
|
TEST_F(ConvTraitsTest, ConvFwdLargeTensorTraitsExtraction)
|
|
{
|
|
// Define a concrete instance type with specific template parameters
|
|
using DeviceInstance =
|
|
ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD_Xdl_CShuffle_Large_Tensor<
|
|
2, // NDimSpatial
|
|
ck::tensor_layout::convolution::GNHWC, // ALayout
|
|
ck::tensor_layout::convolution::GKYXC, // BLayout
|
|
ck::Tuple<>, // DsLayout
|
|
ck::tensor_layout::convolution::GNHWK, // ELayout
|
|
ck::half_t, // ADataType
|
|
ck::half_t, // BDataType
|
|
float, // AccDataType
|
|
ck::half_t, // CShuffleDataType
|
|
ck::Tuple<>, // DsDataType
|
|
ck::half_t, // EDataType
|
|
ck::tensor_operation::element_wise::PassThrough, // AElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // BElementwiseOperation
|
|
ck::tensor_operation::element_wise::PassThrough, // CDEElementwiseOperation
|
|
ck::tensor_operation::device::ConvolutionForwardSpecialization::
|
|
Default, // ConvForwardSpecialization
|
|
ck::tensor_operation::device::GemmSpecialization::Default, // GemmSpec
|
|
1, // NumGemmKPrefetchStage
|
|
256, // BlockSize
|
|
128, // MPerBlock
|
|
128, // NPerBlock
|
|
16, // KPerBlock
|
|
8, // AK1
|
|
8, // BK1
|
|
32, // MPerXDL
|
|
32, // NPerXDL
|
|
4, // MXdlPerWave
|
|
4, // NXdlPerWave
|
|
ck::Sequence<4, 64, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // ABlockTransferSrcAccessOrder
|
|
2, // ABlockTransferSrcVectorDim
|
|
8, // ABlockTransferSrcScalarPerVector
|
|
8, // ABlockTransferDstScalarPerVector_AK1
|
|
1, // ABlockLdsExtraM
|
|
ck::Sequence<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
|
|
ck::Sequence<1, 0, 2>, // BBlockTransferSrcAccessOrder
|
|
2, // BBlockTransferSrcVectorDim
|
|
8, // BBlockTransferSrcScalarPerVector
|
|
8, // BBlockTransferDstScalarPerVector_BK1
|
|
1, // BBlockLdsExtraN
|
|
1, // CShuffleMXdlPerWavePerShuffle
|
|
1, // CShuffleNXdlPerWavePerShuffle
|
|
ck::Sequence<1,
|
|
32,
|
|
1,
|
|
8>, // CDEBlockTransferClusterLengths
|
|
8, // CDEBlockTransferScalarPerVector_NPerBlock
|
|
ck::half_t, // AComputeDataType
|
|
ck::half_t, // BComputeDataType
|
|
ck::LoopScheduler::Default>; // LoopSched
|
|
|
|
// Use ConvTraits to extract compile-time information
|
|
using Traits = ck_tile::reflect::conv::ConvTraits<DeviceInstance>;
|
|
|
|
// Verify signature information
|
|
EXPECT_EQ(Traits::spatial_dim, 2);
|
|
EXPECT_EQ(Traits::direction, ConvDirection::FORWARD);
|
|
EXPECT_THAT(Traits::layout,
|
|
ElementsAre(TensorLayout::GNHWC, TensorLayout::GKYXC, TensorLayout::GNHWK));
|
|
EXPECT_EQ(Traits::data_type, DataType::FP16);
|
|
EXPECT_EQ(Traits::input_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::weight_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
EXPECT_EQ(Traits::output_element_op, ElementwiseOperation::PASS_THROUGH);
|
|
|
|
// Verify specializations
|
|
EXPECT_EQ(Traits::gemm_padding, ck_tile::builder::GemmPadding::DEFAULT);
|
|
EXPECT_EQ(Traits::conv_specialization, ck_tile::builder::ConvSpecialization::DEFAULT);
|
|
|
|
// Verify algorithm information
|
|
EXPECT_EQ(Traits::thread_block_size, 256);
|
|
|
|
// Verify tile dimensions
|
|
EXPECT_EQ(Traits::tile_dims.m, 128);
|
|
EXPECT_EQ(Traits::tile_dims.n, 128);
|
|
EXPECT_EQ(Traits::tile_dims.k, 16);
|
|
}
|
|
} // anonymous namespace
|