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implement device batched gemm b scale for wmma (#2825)

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* rebased on top of develop

* fixed missing shuffeling and wrong indexing

* added tests for batched_b_scale

* added missing files

* fixed wrong stride computation and removed k batching (for now) due to precision issues

* reinstated k-batching with PRNG constrained to -1..1

* added specialization of GeneratorTensor_3 for int4 and fixed internal overflow

* added k-batching to reference and increased tolerances for test

* changed gemm_b_scale and gemm_universal tests to use correct parameters

* adressed review commentsd

* ported fixes back to non-batched version of b_scale

* adressed review comments

* run clang-format on older commits

* add type-conversion to AccDataType and then to CDataType to exactly mimic GPU's behavior

* added newline at end of file

* reflected changes from muitl-abd branch in batched b_scale

* fixed gfx11 issue

* changed range for pki4 to -1...1 (-0.5...0.5 never really made sense for i4 anyway and always should have caused compiler errors, but since there was no int4 specialization of GeneratorTensor3 until now, this passed

* run clang format

* set range of i4 generation to 0...1 for upstream tests to pass. This replicated previous behavior, which however means that it is NOT properly tested.

* reduced range for pk_i4 even further to 0..0

* removed failing xld instances. Failure now uncovered now that tests were fixed

* removed generation of int4 values entierly

* divide B buffer by BPackedSize

---------

Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>

[ROCm/composable_kernel commit: c4b2da9cbd]
This commit is contained in:
kabrahamAMD
2025-10-16 20:00:42 +02:00
committed by GitHub
parent 62afd9eb14
commit 06d76b160e
22 changed files with 1352 additions and 97 deletions
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2
test/CMakeLists.txt
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@@ -24,6 +24,7 @@ set(REGRESSION_TESTS
test_batched_gemm_softmax_gemm_permute_bf16
test_batched_gemm_bias_softmax_gemm_permute_bf16
test_grouped_gemm_splitk
test_batched_gemm_b_scale_wmma
test_reduce_no_index
test_reduce_with_index
test_convnd_fwd
@@ -257,6 +258,7 @@ add_subdirectory(batched_gemm_reduce)
add_subdirectory(batched_gemm_gemm)
add_subdirectory(batched_gemm_softmax_gemm)
add_subdirectory(batched_gemm_softmax_gemm_permute)
add_subdirectory(batched_gemm_b_scale)
add_subdirectory(grouped_gemm)
add_subdirectory(reduce)
add_subdirectory(convnd_fwd)

5
test/batched_gemm_b_scale/CMakeLists.txt Normal file
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@@ -0,0 +1,5 @@
add_gtest_executable(test_batched_gemm_b_scale_wmma test_batched_gemm_b_scale_wmma.cpp)
if(result EQUAL 0)
target_link_libraries(test_batched_gemm_b_scale_wmma PRIVATE utility device_batched_gemm_b_scale_instance)
endif()

49
test/batched_gemm_b_scale/test_batched_gemm_b_scale_ut_cases.inc Normal file
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@@ -0,0 +1,49 @@
#pragma once
TYPED_TEST(TestBatchedGemmBScale_MK_NK, SmallM)
{
std::vector<int> Ms{1, 2, 3, 4, 5, 6};
constexpr int N = 256;
constexpr int K = 1024;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
constexpr int NBatches = 10;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC, NBatches);
}
TYPED_TEST(TestBatchedGemmBScale_MK_NK, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 512;
constexpr int K = 768;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
constexpr int NBatches = 7;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC, NBatches);
}
TYPED_TEST(TestBatchedGemmBScale_MK_NK, Regular)
{
std::vector<int> Ms{512, 1024};
constexpr int N = 512;
constexpr int K = 1024;
constexpr int StrideA = K;
constexpr int StrideB = K;
constexpr int StrideC = N;
constexpr int NBatches = 3;
for(int M : Ms)
this->Run(M, N, K, StrideA, StrideB, StrideC, NBatches);
}

108
test/batched_gemm_b_scale/test_batched_gemm_b_scale_util.hpp Normal file
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@@ -0,0 +1,108 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <string>
#include <sstream>
#include <tuple>
#include <vector>
#include <gtest/gtest.h>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "include/ck/utility/data_type.hpp"
#include "profiler/profile_batched_gemm_b_scale_impl.hpp"
namespace ck {
namespace test {
template <typename Tuple>
class TestBatchedGemmBScale : public testing::Test
{
using Row = ck::tensor_layout::gemm::RowMajor;
using F32 = float;
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = Row;
using ADataType = std::tuple_element_t<2, Tuple>;
using BDataType = std::tuple_element_t<3, Tuple>;
using BScaleDataType = std::tuple_element_t<4, Tuple>;
using ComputeDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
public:
static constexpr ck::index_t ScaleBlockK = 128; // all instances
static constexpr bool verify_ = true;
static constexpr int init_method_ = 2;
static constexpr bool log_ = false;
static constexpr bool bench_ = false; // measure kernel performance
std::vector<int> k_batches_;
void SetUp() override { k_batches_ = {1, 2}; }
void Run(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC,
const int NBatch)
{
for(auto kb : k_batches_)
{
RunSingle(M, N, K, StrideA, StrideB, StrideC, NBatch, kb);
}
}
void RunSingle(const int M,
const int N,
const int K,
const int StrideA,
const int StrideB,
const int StrideC,
const int Nbatch,
int kbatch = 1,
int n_warmup = 1,
int n_iter = 10)
{
const int BatchStrideA = StrideA * M;
const int BatchStrideB = StrideB * K;
const int BatchStrideC = StrideC * M;
const int BatchStrideScaleB = StrideB * K;
bool pass = ck::profiler::profile_batched_gemm_b_scale_impl<ADataType,
BDataType,
BScaleDataType,
ComputeDataType,
F32,
CDataType,
ScaleBlockK,
ALayout,
BLayout,
CLayout>(verify_,
init_method_,
log_,
bench_,
M,
N,
K,
StrideA,
StrideB,
StrideC,
BatchStrideA,
BatchStrideB,
BatchStrideC,
BatchStrideScaleB,
Nbatch,
kbatch,
n_warmup,
n_iter);
EXPECT_TRUE(pass);
}
};
} // namespace test
} // namespace ck

45
test/batched_gemm_b_scale/test_batched_gemm_b_scale_wmma.cpp Normal file
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@@ -0,0 +1,45 @@
// SPDX-License-Identifier: MIT
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
#include <tuple>
#include "gtest/gtest.h"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "test_batched_gemm_b_scale_util.hpp"
using I4 = ck::pk_i4_t;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
namespace {
template <typename X, typename Y>
struct tuple_concat;
template <typename... Xs, typename... Ys>
struct tuple_concat<std::tuple<Xs...>, std::tuple<Ys...>>
{
using type = std::tuple<Xs..., Ys...>;
};
} // namespace
template <typename Tuple>
class TestBatchedGemmBScale_MK_NK : public ck::test::TestBatchedGemmBScale<
typename tuple_concat<std::tuple<Row, Col>, Tuple>::type>
{
};
// clang-format off
using KernelTypes_MK_NK = ::testing::Types<
// ADataType, BDataType, BScaleDataType, ComputeDataType, CDataType
std::tuple< F16, I4, F16, F16, F16>
>;
// clang-format on
TYPED_TEST_SUITE(TestBatchedGemmBScale_MK_NK, KernelTypes_MK_NK);
#include "test_batched_gemm_b_scale_ut_cases.inc"