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MX FP GEMM - Test MX FP8 MFMA Instructions (#1902)

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* Refactored `load_A_row_major` to follow scale mapping

* Refactored `load_A_col_major` to follow scale mapping

* Refactored `load_B_col_major` to follow scale mapping

* Verified non-scaled test

* Verified scaled tests

* Used ReferenceMXGemm for verification

* Updated license headers
This commit is contained in:
Andriy Roshchenko
2025-02-21 13:35:54 -07:00
committed by GitHub
parent 3ace125c30
commit ffa13455a2
4 changed files with 897 additions and 127 deletions
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69
test/mx_mfma_op/mx_mfma_op.cpp
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@@ -30,11 +30,11 @@ bool run_mfma_test(ck::index_t init)
constexpr auto BLOCK_N = mfma_instr.n_per_blk;
constexpr auto BLOCK_K = mfma_instr.num_input_blks * mfma_instr.k_per_blk;
const auto mx_mfma_kernel = ck::matmul<AType, BType, CType, AccType, BLOCK_M, BLOCK_N, BLOCK_K>;
const auto mfma_kernel = ck::matmul<AType, BType, CType, AccType, BLOCK_M, BLOCK_N, BLOCK_K>;
bool pass = true;
pass = ck::mfma_test::TestMFMA<decltype(mx_mfma_kernel),
pass = ck::mfma_test::TestMFMA<decltype(mfma_kernel),
AType,
BType,
CType,
@@ -45,21 +45,80 @@ bool run_mfma_test(ck::index_t init)
CLayout,
BLOCK_M,
BLOCK_N,
BLOCK_K>{}(mx_mfma_kernel, init);
BLOCK_K>{}(mfma_kernel, init);
return pass;
}
TEST(MFMA, FP8MFMA16x16x128)
{
auto AB_init = 0;
auto AB_init = 4;
auto pass = run_mfma_test<f8_t, f8_t, half_t, ck::MFMA_F8F6F4::F32_16x16x128>(AB_init);
EXPECT_TRUE(pass);
}
TEST(MFMA, FP8MFMA32x32x64)
{
auto AB_init = 0;
auto AB_init = 4;
auto pass = run_mfma_test<f8_t, f8_t, float, ck::MFMA_F8F6F4::F32_32x32x64>(AB_init);
EXPECT_TRUE(pass);
}
/**
* @brief Run the test for the given MX MFMA instruction
*
* @param init - selects initialization algorithm for A and B tensors
*/
template <typename AType, typename BType, typename CType, ck::MFMA_F8F6F4 mfma>
bool run_mxmfma_test(ck::index_t init)
{
static_assert(mfma == ck::MFMA_F8F6F4::SCALE_F32_16x16x128 ||
mfma == ck::MFMA_F8F6F4::SCALE_F32_32x32x64,
"Only SCALE_F32_16x16x128 and SCALE_F32_32x32x64 are supported");
using ALayout = ck::tensor_layout::gemm::RowMajor;
using BLayout = ck::tensor_layout::gemm::ColumnMajor;
using CLayout = ck::tensor_layout::gemm::RowMajor;
using AccType = float; // only MFMA_F32 instructions supported
using ScaleType = ck::e8m0_bexp_t; // biased exponent type
ck::mfma_type<static_cast<ck::MfmaInstr>(mfma)> mfma_instr;
constexpr auto BLOCK_M = mfma_instr.m_per_blk;
constexpr auto BLOCK_N = mfma_instr.n_per_blk;
constexpr auto BLOCK_K = mfma_instr.num_input_blks * mfma_instr.k_per_blk;
constexpr auto BLOCK_X = 32; // scaling vector size
const auto mx_mfma_kernel =
ck::matmul<AType, BType, ScaleType, CType, AccType, BLOCK_M, BLOCK_N, BLOCK_K, BLOCK_X>;
bool pass = true;
pass = ck::mxmfma_test::TestMXMFMA<decltype(mx_mfma_kernel),
AType,
BType,
ScaleType,
CType,
ALayout,
BLayout,
CLayout,
BLOCK_M,
BLOCK_N,
BLOCK_K,
BLOCK_X>{}(mx_mfma_kernel, init);
return pass;
}
TEST(MXMFMA, MXFP8MFMA16x16x128)
{
auto AB_init = 7;
auto pass = run_mxmfma_test<f8_t, f8_t, float, ck::MFMA_F8F6F4::SCALE_F32_16x16x128>(AB_init);
EXPECT_TRUE(pass);
}
TEST(MXMFMA, MXFP8MFMA32x32x64)
{
auto AB_init = 7;
auto pass = run_mxmfma_test<f8_t, f8_t, half_t, ck::MFMA_F8F6F4::SCALE_F32_32x32x64>(AB_init);
EXPECT_TRUE(pass);
}

924
test/mx_mfma_op/mx_mfma_op.hpp
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