Files
composable_kernel/experimental/builder/test/test_conv_description.cpp
Ville Pietilä 9cb1f421bc [CK_BUILDER] Refactor convolution signature to provide data type/layout/elementwise op per tensor (#3331)
* Separate layouts into separate entities for input, weight, and output tensors.

* Add test for handling bias tensor layouts.

* Use instance string in builder tests.

* Add handling of output bias data types and layouts.

* Generalize handling of the elementwise ops.

* Test fix.

* Create builder for layouts.

* Layout builder improvements.

* Improve layout builder.

* Simplify bias layout handling.

* Code clean-up.

* Move layout utils into separate file.

* Remove hard-coded layout combinations.

* Small code clean-up.

* Move data type utils into a separate file.

* Add data types, layouts, and elementwise ops per conv tensor.

* Builder bug fixes after refactoring.

* Working baseline.

* Make signature definition look nice in the test code.

* Move TensorConfig into test implementations.

* Fix all fwd conv builder tests.

* Fix conv traits and descriptors tests.

* More factory assets under a separate directory.

* Fix building conv traits.

* Fix clang-format.

* Add Readme doc to describe the design.

* Add link to main Readme. Fix links in the builder design doc.

* Clean-up data type/layout/elementwise op conversions.

* Switch from dimension and tensor type specific layouts to a flat list of tensor layouts.

* Fix clang-formatting.

* Fix clang-format for test code.

* Simplify fwd conv signature definitions in the test code.

* Remove accidental edits.

* Fix comment string.

* Fix instance factory after rebase.

* Fix tests after rebase.

* Unify layout handling.

* Add more conv layout unit tests.

* Clang-format.

* Fix merge conflicts.

* Improve elementwise op handling.

---------

Co-authored-by: Ville Pietilä <>
2025-12-04 12:58:31 +02:00

258 lines
12 KiB
C++
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier: MIT
#include <gtest/gtest.h>
#include <gmock/gmock.h>
#include <ck_tile/builder/conv_builder.hpp>
#include <ck_tile/builder/reflect/conv_description.hpp>
#include "testing_utils.hpp"
#include "impl/conv_signature_types.hpp"
#include "impl/conv_algorithm_types.hpp"
namespace {
namespace ckb = ck_tile::builder;
namespace ckr = ck_tile::reflect;
namespace ckt = ck_tile::test;
struct TensorOp
{
ckb::ElementwiseOperation elementwise_operation{ckb::ElementwiseOperation::PASS_THROUGH};
};
struct InvalidTensorOp
{
int elementwise_operation = 7; // invalid value
};
static_assert(!ckb::TensorOperatorDescriptor<InvalidTensorOp>);
struct TensorConfig
{
ckb::TensorLayout layout;
ckb::DataType data_type{ckb::DataType::UNDEFINDED};
ckb::DataType compute_type{ckb::DataType::UNDEFINDED};
};
struct ConvTensorSimple
{
TensorConfig config;
};
struct ConvTensorWithOp
{
TensorConfig config;
TensorOp operation{};
};
struct ConvTensorWithInvalidOp
{
TensorConfig config;
InvalidTensorOp operation{};
};
// Defines the signature of the convolution operation to be tested.
// This includes dimensionality, direction, data layout, and data type.
struct ConvSignature
{
int spatial_dim = 2;
ckb::DataType data_type = ckb::DataType::FP16;
ckb::DataType accumulation_data_type = ckb::DataType::FP32;
ConvTensorSimple input = {.config = {ckb::TensorLayout::GNHWC}};
ConvTensorSimple weight = {.config = {ckb::TensorLayout::GKYXC}};
ConvTensorSimple output = {.config = {ckb::TensorLayout::GNHWK}};
};
static_assert(ckb::ConvSignatureDescriptor<ConvSignature>);
// Compile time tests for concepts
struct ConvSignatureWithOptionalParams
{
int spatial_dim = 2;
ckb::DataType data_type = ckb::DataType::FP16;
ckb::DataType accumulation_data_type = ckb::DataType::FP32;
ckb::ConvDirection direction = ckb::ConvDirection::FORWARD;
ConvTensorWithOp input = {
.config = {ckb::TensorLayout::GNHWC, ckb::DataType::FP16},
};
ConvTensorWithOp weight = {.config = {ckb::TensorLayout::GKYXC, ckb::DataType::FP16}};
ConvTensorWithOp output = {.config = {ckb::TensorLayout::GNHWK, ckb::DataType::FP16},
.operation = {ckb::ElementwiseOperation::SCALE}};
};
static_assert(ckb::ConvSignatureDescriptor<ConvSignatureWithOptionalParams>);
struct ConvSignatureWithInvalidOptionalParams
{
int spatial_dim = 2;
ckb::DataType data_type = ckb::DataType::FP16;
ckb::DataType accumulation_data_type = ckb::DataType::FP32;
ConvTensorWithInvalidOp input = {.config = {ckb::TensorLayout::GNHWC}};
ConvTensorWithInvalidOp weight = {.config = {ckb::TensorLayout::GKYXC}};
ConvTensorWithInvalidOp output = {.config = {ckb::TensorLayout::GNHWK}};
};
static_assert(!ckb::ConvSignatureDescriptor<ConvSignatureWithInvalidOptionalParams>);
struct DefaultAlgorithm
{
ckb::test::ThreadBlock thread_block{.block_size = 256,
.tile_size = {.m = 256, .n = 256, .k = 32}};
ckb::test::GridwiseXdlGemm gridwise_gemm{.ak1 = 8,
.bk1 = 8,
.m_per_xdl = 16,
.n_per_xdl = 16,
.m_xdl_per_wave = 4,
.n_xdl_per_wave = 4};
ckb::test::TransferABC transfer{
.a =
{
.block_transfer = {.k0 = 1, .m_n = 128, .k1 = 2},
.lds_transfer = {.src_vector_dim = 2,
.src_scalar_per_vector = 2,
.lds_dst_scalar_per_vector = 2,
.is_direct_load = false,
.lds_padding = false},
.block_transfer_access_order = {.order = {0, 1, 2}},
.src_access_order = {.order = {0, 1, 2}},
},
.b =
{
.block_transfer = {.k0 = 1, .m_n = 128, .k1 = 2},
.lds_transfer = {.src_vector_dim = 2,
.src_scalar_per_vector = 2,
.lds_dst_scalar_per_vector = 2,
.is_direct_load = false,
.lds_padding = false},
.block_transfer_access_order = {.order = {0, 1, 2}},
.src_access_order = {.order = {0, 1, 2}},
},
.c =
{
.thread_cluster_dims =
{.m_block = 1, .m_wave_per_xdl = 32, .n_block = 1, .n_wave_per_xdl = 8},
.epilogue = {.m_xdl_per_wave_per_shuffle = 1,
.n_per_wave_per_shuffle = 1,
.scalar_per_vector = 2},
},
};
ckb::ConvFwdSpecialization fwd_specialization = ckb::ConvFwdSpecialization::DEFAULT;
ckb::GemmSpecialization gemm_specialization = ckb::GemmSpecialization::Default;
ckb::test::BlockGemm block_gemm{.pipeline_version = ckb::PipelineVersion::V4,
.scheduler = ckb::PipelineScheduler::INTRAWAVE};
};
static_assert(ckb::ConvAlgorithmDescriptor<DefaultAlgorithm>);
TEST(ConvDescriptionTest, DefaultInstanceHasBriefDescription)
{
static constexpr const ConvSignature SIGNATURE;
static constexpr const DefaultAlgorithm ALGORITHM;
using Instance = ckb::ConvBuilder<SIGNATURE, ALGORITHM>::Instance;
EXPECT_THAT(ckr::describe<Instance>().brief(), ckt::StringEqWithDiff("2D Forward convolution"));
}
TEST(ConvDescriptionTest, DefaultInstanceHasDetailedDescription)
{
static constexpr const ConvSignature SIGNATURE;
static constexpr const DefaultAlgorithm ALGORITHM;
using Instance = ckb::ConvBuilder<SIGNATURE, ALGORITHM>::Instance;
EXPECT_THAT(ckr::describe<Instance>().detailed(),
ckt::StringEqWithDiff( //
"2D Forward Convolution Kernel\n"
"├─ Signature\n"
"│ ├─ Tensor Type: FP16\n"
"│ ├─ Input Layout: GNHWC\n"
"│ ├─ Weight Layout: GKYXC\n"
"│ ├─ Output Layout: GNHWK\n"
"│ ├─ Input elementwise operation: PASS_THROUGH\n"
"│ ├─ Weights elementwise operation: PASS_THROUGH\n"
"│ └─ Output elementwise operation: PASS_THROUGH\n"
"└─ Algorithm\n"
" ├─ Thread block size: 256\n"
" ├─ Data tile size: 256×256×32\n"
" ├─ Gemm padding: DEFAULT\n"
" ├─ Convolution specialization: DEFAULT\n"
" ├─ Pipeline version: V4\n"
" ├─ Pipeline scheduler: INTRAWAVE\n"
" ├─ Warp Gemm parameters: \n"
" │ ├─ subtile size: 16×16\n"
" │ └─ Number of warp gemm iterations: 4×4\n"
" └─ Memory access:\n"
" ├─ A Tile transfer: \n"
" │ ├─ Tile dimensions: 4×256×8×\n"
" │ ├─ The innermost K subdimension size: 8\n"
" │ ├─ Spatial thread distribution over the data tile: 0×1×2\n"
" │ ├─ The order of accessing data tile axes: 0×1×2\n"
" │ ├─ Vectorized memory access axis index (with contiguous memory): 2\n"
" │ ├─ Vector access (GMEM read) instruction size: 2\n"
" │ ├─ Vector access (LDS write) instruction size: 2\n"
" │ └─ LDS data layout padding (to prevent bank conflicts): 2\n"
" ├─ B Tile transfer: \n"
" │ ├─ Tile dimensions: 4×256×8×\n"
" │ ├─ The innermost K subdimension size: 8\n"
" │ ├─ Spatial thread distribution over the data tile: 0×1×2\n"
" │ ├─ The order of accessing data tile axes: 0×1×2\n"
" │ ├─ Vectorized memory access axis index (with contiguous memory): 2\n"
" │ ├─ Vector access (GMEM read) instruction size: 2\n"
" │ ├─ Vector access (LDS write) instruction size: 2\n"
" │ └─ LDS data layout padding (to prevent bank conflicts): 2\n"
" └─ C Tile transfer: \n"
" ├─ Data shuffle (number of gemm instructions per iteration): 1×1\n"
" ├─ Spatial thread distribution used to store data: 1×32×1×8\n"
" └─ Vector access (GMEM write) instruction size: 2"));
}
TEST(ConvDescriptionTest, DefaultInstanceHasInstanceString)
{
static constexpr const ConvSignature SIGNATURE;
static constexpr const DefaultAlgorithm ALGORITHM;
using Instance = ckb::ConvBuilder<SIGNATURE, ALGORITHM>::Instance;
// Get the instance string from the description
std::string instance_str = ckr::describe<Instance>().instance_string();
// Verify that the instance string is not empty
EXPECT_FALSE(instance_str.empty());
// Verify that it contains the device operation name
// The exact format depends on the InstanceTraits implementation
EXPECT_THAT(instance_str, ::testing::HasSubstr("DeviceGroupedConvFwdMultipleABD"));
}
// NOTE: BackwardDataInstanceHasDetailedDescription test is disabled because ConvFactory
// does not have a specialization for backward data convolutions. The test fails with:
// "implicit instantiation of undefined template 'ck_tile::builder::ConvFactory<...>'"
//
// To enable this test, a ConvFactory specialization for backward data operations must be
// implemented first.
//
// TEST(ConvDescriptionTest, BackwardDataInstanceHasDetailedDescription)
// {
// struct BackwardDataSignature
// {
// int spatial_dim = 2;
// ckb::ConvDirection direction = ckb::ConvDirection::BACKWARD_DATA;
// ckb::GroupConvLayout layout = ckb::GroupConvLayout2D::GNHWC_GKYXC_GNHWK;
// ckb::DataType data_type = ckb::DataType::FP16;
// ckb::ElementwiseOperation elementwise_operation =
// ckb::ElementwiseOperation::PASS_THROUGH; ckb::GroupConvDeviceOp device_operation =
// ckb::BwdDataGroupConvDeviceOperation::DeviceGroupedConvBwdDataMultipleD_Xdl_CShuffle_v1;
// };
// static_assert(ckb::ConvSignatureDescriptor<BackwardDataSignature>);
//
// static constexpr const BackwardDataSignature SIGNATURE;
// static constexpr const DefaultAlgorithm ALGORITHM;
// using Builder = ckb::ConvBuilder<SIGNATURE, ALGORITHM>;
//
// // Verify Brief works
// EXPECT_THAT(ckr::Describe<Builder>().brief(),
// ckt::StringEqWithDiff("2D Backward Data convolution"));
//
// // Verify detailed works - to be updated once ConvFactory is implemented
// EXPECT_THAT(ckr::Describe<Builder>().detailed(),
// ckt::StringEqWithDiff("PLACEHOLDER"));
// }
} // namespace