Skip to content

GitLab

Commit 9ba504b6 authored by ThomasNing's avatar ThomasNing
Browse files

merge with the develop support the fp8 with computev4

parents e3402c93 f49de496
Expand all Hide whitespace changes
Inline Side-by-side
Showing with 5218 additions and 70 deletions
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_gemm_xdl_cshuffle_v1.hpp
View file @ 9ba504b6
......@@ -607,6 +607,7 @@ struct GridwiseBatchedGemmGemm_Xdl_CShuffle
// with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
// cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
// therefore we may just as well assign Gemm1KPack = group_size
constexpr index_t Gemm1KPack =
MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.group_size;
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_gemm_multiple_d_xdl_cshuffle_v1.hpp
View file @ 9ba504b6
......@@ -856,11 +856,18 @@ struct GridwiseBatchedGemmMultipleDGemmMultipleD_Xdl_CShuffle
static_cast<A0B0B1DataType*>(p_shared) + SharedMemTrait::b1_block_space_offset,
b1_block_desc_bk0_n_bk1.GetElementSpaceSize());
constexpr index_t Gemm1KPack = math::max(
math::lcm(
MfmaSelector<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl>::selected_mfma.group_size,
B1K1),
MfmaSelector<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl>::selected_mfma.k_per_blk);
// selected_mfma.group_size or B1K1 <= Gemm1KPack <= selected_mfma.group_size
// selected_mfma.k_per_blk <= Gemm1KPack
//
// Following similar rationale behind Gemm0KPack, let Gemm1KPack be the lowest common
// multiples of A1K1 (predetermined by selected_mfma.group_size) and B1K1. But in this case
// Gemm1KPack can't be higher than A1K1 itself because A1 matrix is distributed in VGPRs
// with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
// cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
// therefore we may just as well assign Gemm1KPack = group_size
constexpr index_t Gemm1KPack =
MfmaSelector<A0B0B1DataType, Gemm0MPerXdl, Gemm0NPerXdl>::selected_mfma.group_size;
auto blockwise_gemm1 = BlockwiseGemmXdlops_v2<
BlockSize,
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_multiple_d_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ 9ba504b6
......@@ -773,6 +773,7 @@ struct GridwiseBatchedGemmMultipleDSoftmaxGemm_Xdl_CShuffle
// with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
// cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
// therefore we may just as well assign Gemm1KPack = group_size
constexpr index_t Gemm1KPack =
MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.group_size;
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_gemm_softmax_gemm_xdl_cshuffle_v1.hpp
View file @ 9ba504b6
......@@ -628,6 +628,7 @@ struct GridwiseBatchedGemmSoftmaxGemm_Xdl_CShuffle
// with 'group_size' amount of contiguous elements. Having Gemm1KPack greater than A1K1 will
// cause mismatch in summation index for example c[0:7] = a1[[0:3, 8:11]] * b1[0:7].
// therefore we may just as well assign Gemm1KPack = group_size
constexpr index_t Gemm1KPack =
MfmaSelector<FloatAB, MPerXdl, NPerXdl>::selected_mfma.group_size;
......
include/ck/tensor_operation/gpu/warp/xdlops_gemm.hpp
View file @ 9ba504b6
......@@ -37,7 +37,17 @@ enum struct MfmaInstr
mfma_f32_32x32x16f8bf8,
mfma_f32_16x16x32f8bf8,
mfma_f32_32x32x16bf8f8,
mfma_f32_16x16x32bf8f8
mfma_f32_16x16x32bf8f8,
mfma_f32_32x32x16f16,
mfma_f32_16x16x32f16,
mfma_f32_32x32x16bf16,
mfma_f32_16x16x32bf16,
mfma_i32_32x32x32i8,
mfma_i32_16x16x64i8,
mfma_f32_32x32x64f8f6f4,
mfma_f32_16x16x128f8f6f4,
mfma_scale_f32_32x32x64f8f6f4,
mfma_scale_f32_16x16x128f8f6f4
};
template <MfmaInstr instr>
......@@ -198,6 +208,50 @@ struct mfma_type<MfmaInstr::mfma_f32_32x32x8f16>
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16f16>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 4;
static constexpr index_t num_regs_per_blk = 16;
static constexpr index_t num_threads_per_blk = 32;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 2;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 32;
static constexpr index_t n_per_blk = 32;
static constexpr index_t k_per_blk = 8;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_32x32x16f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_16x16x32f16>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 1;
static constexpr index_t num_regs_per_blk = 4;
static constexpr index_t num_threads_per_blk = 16;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 4;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 16;
static constexpr index_t n_per_blk = 16;
static constexpr index_t k_per_blk = 8;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_16x16x32f16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_16x16x16f16>
{
......@@ -264,6 +318,28 @@ struct mfma_type<MfmaInstr::mfma_f32_4x4x4f16>
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x16bf16>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 4;
static constexpr index_t num_regs_per_blk = 16;
static constexpr index_t num_threads_per_blk = 32;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 2;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 32;
static constexpr index_t n_per_blk = 32;
static constexpr index_t k_per_blk = 8;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_32x32x16bf16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x8bf16_1k>
{
......@@ -286,6 +362,28 @@ struct mfma_type<MfmaInstr::mfma_f32_32x32x8bf16_1k>
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf16>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 1;
static constexpr index_t num_regs_per_blk = 4;
static constexpr index_t num_threads_per_blk = 16;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 4;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 16;
static constexpr index_t n_per_blk = 16;
static constexpr index_t k_per_blk = 8;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_16x16x32bf16<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_16x16x16bf16_1k>
{
......@@ -440,6 +538,50 @@ struct mfma_type<MfmaInstr::mfma_i32_16x16x32i8>
}
};
template <>
struct mfma_type<MfmaInstr::mfma_i32_32x32x32i8>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 4;
static constexpr index_t num_regs_per_blk = 16;
static constexpr index_t num_threads_per_blk = 32;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 2;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 32;
static constexpr index_t n_per_blk = 32;
static constexpr index_t k_per_blk = 16;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_i32_32x32x32i8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_i32_16x16x64i8>
{
static constexpr index_t group_size = 4;
static constexpr index_t num_groups_per_blk = 1;
static constexpr index_t num_regs_per_blk = 4;
static constexpr index_t num_threads_per_blk = 16;
static constexpr index_t wave_size = 64;
static constexpr index_t num_input_blks = 4;
static constexpr index_t num_output_blks = 1;
static constexpr index_t m_per_blk = 16;
static constexpr index_t n_per_blk = 16;
static constexpr index_t k_per_blk = 16;
static constexpr bool is_k_reduction = true;
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_i32_16x16x64i8<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f64_16x16x4f64>
{
......@@ -638,16 +780,115 @@ struct mfma_type<MfmaInstr::mfma_f32_16x16x32bf8f8>
}
};
// TODO: fix mfma...f8f6f4 instructions
template <>
struct mfma_type<MfmaInstr::mfma_f32_32x32x64f8f6f4>
{
// clang-format off
static constexpr index_t group_size = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_groups_per_blk = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_regs_per_blk = 16; // m_per_blk * n_per_blk / wave_size
static constexpr index_t num_threads_per_blk = 32; // n_per_blk
static constexpr index_t wave_size = 64; // fixed
static constexpr index_t num_input_blks = 2; // m_per_blk / num_regs_per_blk
static constexpr index_t num_output_blks = 1; // (is_k_reduction == true) ???
static constexpr index_t m_per_blk = 32; // from the instruction
static constexpr index_t n_per_blk = 32; // from the instruction
static constexpr index_t k_per_blk = 32; // (is_k_reduction == true) ? 64 / num_input_blks
static constexpr bool is_k_reduction = true; // ???
// clang-format on
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_32x32x64f8f6f4<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_f32_16x16x128f8f6f4>
{
// clang-format off
static constexpr index_t group_size = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_groups_per_blk = 1; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_regs_per_blk = 4; // m_per_blk * n_per_blk / wave_size
static constexpr index_t num_threads_per_blk = 16; // == n_per_blk
static constexpr index_t wave_size = 64; // fixed
static constexpr index_t num_input_blks = 4; // m_per_blk / num_regs_per_blk
static constexpr index_t num_output_blks = 1; // (is_k_reduction == true) ???
static constexpr index_t m_per_blk = 16; // from the instruction
static constexpr index_t n_per_blk = 16; // from the instruction
static constexpr index_t k_per_blk = 32; // (is_k_reduction == true) ? 128 / num_input_blks
static constexpr bool is_k_reduction = true; // ???
// clang-format on
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_f32_16x16x128f8f6f4<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_scale_f32_32x32x64f8f6f4>
{
// clang-format off
static constexpr index_t group_size = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_groups_per_blk = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_regs_per_blk = 16; // m_per_blk * n_per_blk / wave_size
static constexpr index_t num_threads_per_blk = 32; // n_per_blk
static constexpr index_t wave_size = 64; // fixed
static constexpr index_t num_input_blks = 2; // m_per_blk / num_regs_per_blk
static constexpr index_t num_output_blks = 1; // (is_k_reduction == true) ???
static constexpr index_t m_per_blk = 32; // from the instruction
static constexpr index_t n_per_blk = 32; // from the instruction
static constexpr index_t k_per_blk = 32; // (is_k_reduction == true) ? 64 / num_input_blks
static constexpr bool is_k_reduction = true; // ???
// clang-format on
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_scale_f32_32x32x64f8f6f4<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <>
struct mfma_type<MfmaInstr::mfma_scale_f32_16x16x128f8f6f4>
{
// clang-format off
static constexpr index_t group_size = 4; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_groups_per_blk = 1; // ??? group_size * num_groups_per_blk == num_regs_per_blk
static constexpr index_t num_regs_per_blk = 4; // m_per_blk * n_per_blk / wave_size
static constexpr index_t num_threads_per_blk = 16; // == n_per_blk
static constexpr index_t wave_size = 64; // fixed
static constexpr index_t num_input_blks = 4; // m_per_blk / num_regs_per_blk
static constexpr index_t num_output_blks = 1; // (is_k_reduction == true) ???
static constexpr index_t m_per_blk = 16; // from the instruction
static constexpr index_t n_per_blk = 16; // from the instruction
static constexpr index_t k_per_blk = 32; // (is_k_reduction == true) ? 128 / num_input_blks
static constexpr bool is_k_reduction = true; // ???
// clang-format on
template <index_t MPerXdlops, index_t NPerXdlops, class FloatA, class FloatB, class FloatC>
__device__ void run(const FloatA& a, const FloatB& b, FloatC& reg_c) const
{
intrin_mfma_scale_f32_16x16x128f8f6f4<MPerXdlops, NPerXdlops>::Run(a, b, reg_c);
}
};
template <typename base_type,
index_t MPerXdlops,
index_t NPerXdlops,
typename additional_type = base_type>
typename additional_type = base_type,
bool is_single_rate_mfma = false>
struct MfmaSelector
{
template <typename base_type_,
index_t MPerXdlops_,
index_t NPerXdlops_,
typename additional_type_ = base_type_>
typename additional_type_ = base_type_,
bool is_single_rate_mfma_ = false>
static constexpr auto GetMfma();
template <>
......@@ -711,13 +952,32 @@ struct MfmaSelector
}
template <>
constexpr auto GetMfma<half_t, 32, 32>()
constexpr auto GetMfma<half_t, 32, 32, half_t, false>()
{
#if defined(__gfx950__)
return MfmaInstr::mfma_f32_32x32x16f16;
#else
return MfmaInstr::mfma_f32_32x32x8f16;
#endif
}
template <>
constexpr auto GetMfma<half_t, 32, 32, half_t, true>()
{
return MfmaInstr::mfma_f32_32x32x8f16;
}
template <>
constexpr auto GetMfma<half_t, 16, 16>()
constexpr auto GetMfma<half_t, 16, 16, half_t, false>()
{
#if defined(__gfx950__)
return MfmaInstr::mfma_f32_16x16x32f16;
#else
return MfmaInstr::mfma_f32_16x16x16f16;
#endif
}
template <>
constexpr auto GetMfma<half_t, 16, 16, half_t, true>()
{
return MfmaInstr::mfma_f32_16x16x16f16;
}
......@@ -741,7 +1001,19 @@ struct MfmaSelector
}
template <>
constexpr auto GetMfma<bhalf_t, 32, 32>()
constexpr auto GetMfma<bhalf_t, 32, 32, bhalf_t, false>()
{
#if defined(__gfx950__)
return MfmaInstr::mfma_f32_32x32x16bf16;
#elif defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_32x32x8bf16_1k;
#else
return MfmaInstr::mfma_f32_32x32x4bf16;
#endif
}
template <>
constexpr auto GetMfma<bhalf_t, 32, 32, bhalf_t, true>()
{
#if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_32x32x8bf16_1k;
......@@ -751,7 +1023,19 @@ struct MfmaSelector
}
template <>
constexpr auto GetMfma<bhalf_t, 16, 16>()
constexpr auto GetMfma<bhalf_t, 16, 16, bhalf_t, false>()
{
#if defined(__gfx950__)
return MfmaInstr::mfma_f32_16x16x32bf16;
#elif defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_16x16x16bf16_1k;
#else
return MfmaInstr::mfma_f32_16x16x8bf16;
#endif
}
template <>
constexpr auto GetMfma<bhalf_t, 16, 16, bhalf_t, true>()
{
#if defined(CK_USE_AMD_MFMA_BF16_1K_OP)
return MfmaInstr::mfma_f32_16x16x16bf16_1k;
......@@ -760,7 +1044,18 @@ struct MfmaSelector
#endif
}
#if defined(CK_USE_AMD_MFMA_GFX940)
#if defined(__gfx950__)
template <>
constexpr auto GetMfma<int8_t, 32, 32>()
{
return MfmaInstr::mfma_i32_32x32x32i8;
}
template <>
constexpr auto GetMfma<int8_t, 16, 16>()
{
return MfmaInstr::mfma_i32_16x16x64i8;
}
#elif defined(__gfx942__)
template <>
constexpr auto GetMfma<int8_t, 32, 32>()
{
......@@ -832,8 +1127,8 @@ struct MfmaSelector
return MfmaInstr::mfma_f32_16x16x32bf8f8;
}
static constexpr auto selected_mfma =
mfma_type<GetMfma<base_type, MPerXdlops, NPerXdlops, additional_type>()>{};
static constexpr auto selected_mfma = mfma_type<
GetMfma<base_type, MPerXdlops, NPerXdlops, additional_type, is_single_rate_mfma>()>{};
__host__ __device__ constexpr MfmaSelector()
{
......@@ -1135,7 +1430,13 @@ struct XdlopsGemm
return TransposeC ? CIndex4D{blk_td, I0, blk_id, I0} : CIndex4D{I0, blk_id, I0, blk_td};
}
static constexpr auto mfma = MfmaSelector<base_type, MPerXdlops, NPerXdlops, additional_type>{};
// Falls back to single rate instruction on gfx950 if KPack <= 4; no change on gfx942-
static constexpr auto
mfma = MfmaSelector < base_type,
MPerXdlops, NPerXdlops, additional_type,
((is_same<base_type, half_t>::value || is_same<base_type, bhalf_t>::value) && KPack <= 4)
? true
: false > {};
static constexpr auto mfma_instr = mfma.selected_mfma;
......
include/ck/utility/amd_buffer_addressing.hpp
View file @ 9ba504b6
......@@ -581,7 +581,7 @@ __device__ void amd_global_atomic_add_impl(const typename vector_type<T, N>::typ
tmp.template AsType<half2_t>()[i]);
});
}
#if defined(__gfx942__)
#if defined(__gfx942__) || defined(__gfx950__)
else if constexpr(is_same<T, bhalf_t>::value)
{
vector_type<bhalf_t, N> tmp{src_thread_data};
......
include/ck/utility/amd_ck_fp8.hpp
View file @ 9ba504b6
......@@ -20,39 +20,25 @@
#define CK_USE_OCP_FP8 0
#endif
namespace {
// https://en.cppreference.com/w/cpp/types/conditional
template <bool B, class T, class F>
struct conditional
{
using type = T;
};
template <class T, class F>
struct conditional<false, T, F>
{
using type = F;
};
} // namespace
namespace ck {
using f8_fnuz_t = _BitInt(8);
using bf8_fnuz_t = unsigned _BitInt(8);
#if(defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__) || defined(__gfx1200__) || \
defined(__gfx1201__)) && \
defined(__gfx1201__) || defined(__gfx950__)) && \
__HIP_DEVICE_COMPILE__
#define CK_FP8_CVT_FAST_PATH 1
#else
#define CK_FP8_CVT_FAST_PATH 0
#endif
#if(defined(__gfx1200__) || defined(__gfx1201__)) && __HIP_DEVICE_COMPILE__
#if(defined(__gfx1200__) || defined(__gfx1201__) || defined(__gfx950__)) && __HIP_DEVICE_COMPILE__
#define CK_OCP_FP8_CVT_FAST_PATH 1
#else
#define CK_OCP_FP8_CVT_FAST_PATH 0
#endif
namespace ck {
using f8_fnuz_t = _BitInt(8);
using bf8_fnuz_t = unsigned _BitInt(8);
typedef unsigned char fp8_storage_t;
/**
......@@ -207,10 +193,11 @@ __host__ __device__ static inline T cast_from_f8(fp8_storage_t x)
}
}
typename conditional<
typename std::conditional<
sizeof(T) == 2,
unsigned short int,
typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type retval;
typename std::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type
retval;
if constexpr(we == 5 && is_half && !is_fnuz)
{
......@@ -303,7 +290,6 @@ static __device__ float2_t cast_to_f32x2_from_f8x2(fp8x2_storage_t v)
return __builtin_amdgcn_cvt_pk_f32_bf8(i16val, false);
}
}
#endif
} // namespace fp8_impl
......@@ -378,7 +364,7 @@ struct bf8_ocp_t
__host__ explicit operator float() const
#endif
{
#if defined(__gfx1200__) || defined(__gfx1201__)
#if defined(__gfx950__) || defined(__gfx1200__) || defined(__gfx1201__)
return fp8_impl::cast_to_f32_from_f8<default_interpret>(this->data);
#else
return fp8_impl::cast_from_f8<float, wm, we, false>(
......@@ -392,7 +378,7 @@ struct bf8_ocp_t
__host__ explicit operator _Float16() const
#endif
{
#if defined(__gfx1200__) || defined(__gfx1201__)
#if defined(__gfx950__) || defined(__gfx1200__) || defined(__gfx1201__)
return static_cast<_Float16>(fp8_impl::cast_to_f32_from_f8<default_interpret>(this->data));
#else
return fp8_impl::cast_from_f8<_Float16, wm, we, false>(
......@@ -553,10 +539,10 @@ __host__ __device__ static inline fp8_storage_t cast_to_f8(T _x, unsigned int rn
constexpr int mfmt = (sizeof(T) == 8) ? 52 : ((sizeof(T) == 4) ? 23 : 10);
using T_bitwise = typename conditional<
using T_bitwise = typename std::conditional<
sizeof(T) == 2,
unsigned short int,
typename conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type;
typename std::conditional<sizeof(T) == 4, unsigned int, unsigned long long>::type>::type;
T_bitwise x_bitwise = bit_cast<T_bitwise>(_x);
unsigned long long x{x_bitwise};
......
include/ck/utility/amd_xdlops.hpp
View file @ 9ba504b6
......@@ -5,7 +5,7 @@
namespace ck {
// Define the common macro for MI300 models
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__)
#if defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__) || defined(__gfx950__)
#define __gfx94__
#endif
......@@ -134,6 +134,46 @@ struct intrin_mfma_f32_32x32x4f16<32, 64>
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16f16;
template <>
struct intrin_mfma_f32_32x32x16f16<32, 32>
{
template <class FloatC>
__device__ static void Run(const half8_t& reg_a, const half8_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<float16_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_32x32x16_f16(
reg_a, reg_b, reg_c.template AsType<float16_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_16x16x32f16;
template <>
struct intrin_mfma_f32_16x16x32f16<16, 16>
{
template <class FloatC>
__device__ static void Run(const half8_t& reg_a, const half8_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<float4_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_16x16x32_f16(
reg_a, reg_b, reg_c.template AsType<float4_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x8f16;
......@@ -204,6 +244,46 @@ struct intrin_mfma_f32_4x4x4f16<8, 64>
};
// bfp16
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16bf16;
template <>
struct intrin_mfma_f32_32x32x16bf16<32, 32>
{
template <class FloatC>
__device__ static void Run(const bhalf8_t& reg_a, const bhalf8_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<float16_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_32x32x16_bf16(
reg_a, reg_b, reg_c.template AsType<float16_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_16x16x32bf16;
template <>
struct intrin_mfma_f32_16x16x32bf16<16, 16>
{
template <class FloatC>
__device__ static void Run(const bhalf8_t& reg_a, const bhalf8_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<float4_t>()(Number<0>{}) = __builtin_amdgcn_mfma_f32_16x16x32_bf16(
reg_a, reg_b, reg_c.template AsType<float4_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x8bf16_1k;
......@@ -298,6 +378,46 @@ struct intrin_mfma_i32_16x16x16i8<16, 16>
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_i32_32x32x32i8;
template <>
struct intrin_mfma_i32_32x32x32i8<32, 32>
{
template <class FloatC>
__device__ static void Run(const int8x16_t& reg_a, const int8x16_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<int32x16_t>()(Number<0>{}) = __builtin_amdgcn_mfma_i32_32x32x32_i8(
reg_a, reg_b, reg_c.template AsType<int32x16_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_i32_16x16x64i8;
template <>
struct intrin_mfma_i32_16x16x64i8<16, 16>
{
template <class FloatC>
__device__ static void Run(const int8x16_t& reg_a, const int8x16_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<int32x4_t>()(Number<0>{}) = __builtin_amdgcn_mfma_i32_16x16x64_i8(
reg_a, reg_b, reg_c.template AsType<int32x4_t>()[Number<0>{}], 0, 0, 0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif // defined(__gfx950__)
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_i32_32x32x16i8;
......@@ -356,6 +476,149 @@ struct intrin_mfma_f64_16x16x4f64<16, 16>
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x64f8f6f4;
/// @brief Performs a matrix fused multiply-accumulate operation on 32x32x64 submatrices for f8, f6,
/// and f4 data types.
///
/// @note Calls scaled version of the instruction as the original instruction is not supported in
/// the backend. That is the intended use. There is a backend optimization to select the unscaled
/// operation if the scale is 0.
template <>
struct intrin_mfma_f32_32x32x64f8f6f4<32, 32>
{
template <class FloatC>
__device__ static void Run(const f8x32_t& reg_a, const f8x32_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
reg_c.template AsType<float16_t>()(Number<0>{}) =
__builtin_amdgcn_mfma_scale_f32_32x32x64_f8f6f4(
reg_a,
reg_b,
reg_c.template AsType<float16_t>()[Number<0>{}],
0, // cbsz
0, // blgp
0,
0,
0,
0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_scale_f32_32x32x64f8f6f4;
template <>
struct intrin_mfma_scale_f32_32x32x64f8f6f4<32, 32>
{
template <class FloatC>
__device__ static void Run(const f8x32_t& reg_a,
const int32_t scale_a,
const f8x32_t& reg_b,
const int32_t scale_b,
FloatC& reg_c)
{
#if defined(__gfx950__)
// https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/llvm/test/Verifier/AMDGPU/mfma-scale.ll#L10
reg_c.template AsType<float16_t>()(Number<0>{}) =
__builtin_amdgcn_mfma_scale_f32_32x32x64_f8f6f4(
reg_a,
reg_b,
reg_c.template AsType<float16_t>()[Number<0>{}],
0, // cbsz
0, // blgp
0, // { OPSEL_HI[0], OPSEL[0] }?
scale_a,
0, // { OPSEL_HI[1], OPSEL[1] }?
scale_b);
#else
ignore = reg_a;
ignore = scale_a;
ignore = reg_b;
ignore = scale_b;
ignore = reg_c;
#endif
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_scale_f32_16x16x128f8f6f4;
template <>
struct intrin_mfma_scale_f32_16x16x128f8f6f4<16, 16>
{
template <class FloatC>
__device__ static void Run(const f8x32_t& reg_a,
const int32_t scale_a,
const f8x32_t& reg_b,
const int32_t scale_b,
FloatC& reg_c)
{
#if defined(__gfx950__)
// https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/llvm/test/Verifier/AMDGPU/mfma-scale.ll#L10
reg_c.template AsType<float4_t>()(Number<0>{}) =
__builtin_amdgcn_mfma_scale_f32_16x16x128_f8f6f4(
reg_a,
reg_b,
reg_c.template AsType<float4_t>()[Number<0>{}],
0, // cbsz
0, // blgp
0, // { OPSEL_HI[0], OPSEL[0] }?
scale_a,
0, // { OPSEL_HI[1], OPSEL[1] }?
scale_b);
#else
ignore = reg_a;
ignore = scale_a;
ignore = reg_b;
ignore = scale_b;
ignore = reg_c;
#endif
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_16x16x128f8f6f4;
/// @brief Performs a matrix fused multiply-accumulate operation on 16x16x128 submatrices for f8f6f4
/// data types.
///
/// @note Calls scaled version of the instruction as the original instruction is not supported in
/// the backend. That is the intended use. There is a backend optimization to select the unscaled
/// operation if the scale is 0.
template <>
struct intrin_mfma_f32_16x16x128f8f6f4<16, 16>
{
template <class FloatC>
__device__ static void Run(const f8x32_t& reg_a, const f8x32_t& reg_b, FloatC& reg_c)
{
#if defined(__gfx950__)
// https://github.com/ROCm/llvm-project/blob/656552edc693e2bb4abc9258399c39d190fce2b3/llvm/test/Verifier/AMDGPU/mfma-scale.ll#L10
reg_c.template AsType<float4_t>()(Number<0>{}) =
__builtin_amdgcn_mfma_scale_f32_16x16x128_f8f6f4(
reg_a,
reg_b,
reg_c.template AsType<float4_t>()[Number<0>{}],
0, // cbsz
0, // blgp
0,
0,
0,
0);
#else
ignore = reg_a;
ignore = reg_b;
ignore = reg_c;
#endif
}
};
template <index_t MPerWave, index_t NPerWave>
struct intrin_mfma_f32_32x32x16f8f8;
......
include/ck/utility/blkgemmpipe_scheduler.hpp
View file @ 9ba504b6
......@@ -90,14 +90,22 @@ struct BlockwiseGemmXdlops_pipeline_hotloop_inst
KPerXDL);
printf(" A/B buffer load inst: %d, %d\n A/B LDS write inst: %d, %d\n A/B LDS read inst: "
"%d, %d\n C MFMA inst: %d\n",
"%d, %d\n C MFMA inst: %d\n"
"A/B LDS read width: %d, %d, A/B LDS write width: %d, %d, A/B buffer load width: "
"%d/ %d\n",
A_Buffer_Load_Inst_Num,
B_Buffer_Load_Inst_Num,
A_LDS_Write_Inst_Num,
B_LDS_Write_Inst_Num,
A_LDS_Read_Inst_Num,
B_LDS_Read_Inst_Num,
C_MFMA_Inst_Num);
C_MFMA_Inst_Num,
A_LDS_Read_Width,
B_LDS_Read_Width,
ALDSWriteWidth,
BLDSWriteWidth,
ABufferLoadWidth,
BBufferLoadWidth);
}
};
......
include/ck/utility/data_type.hpp
View file @ 9ba504b6
This diff is collapsed.
include/ck/utility/e8m0.hpp 0 → 100644
View file @ 9ba504b6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/type.hpp"
namespace ck {
/**
* @brief Unsigned representation of a conventional biased Float32 exponent.
*
* bias = 127;
*
* E8M0_1 = 0b01111111; => 2^(127-127) = 1
* E8M0_2 = 0b10000000; => 2^(128-127) = 2^1 = 2
* E8M0_3 = 0b10000010; => 2^(130-127) = 2^3 = 8
* E8M0_135 = 0b10000111; => 2^(135-127) = 2^8 = 256
* E8M0_142 = 0b10001110; => 2^(142-127) = 2^15 = 32768
* E8M0_MIN = 0b00000000; => 2^-127
* E8M0_MAX = 0b11111110; => 2^127
* E8M0_NAN = 0b11111111; => NaN
*/
struct e8m0_bexp_t
{
using type = uint8_t;
type data;
constexpr static type bias = 127;
constexpr static type nan_mask = 0xFF;
__host__ __device__ constexpr e8m0_bexp_t() : data{type{}} {}
__host__ __device__ constexpr e8m0_bexp_t(type init) : data{init} {}
__host__ __device__ constexpr e8m0_bexp_t(int init) : data{static_cast<type>(init & nan_mask)}
{
}
__host__ __device__ explicit constexpr e8m0_bexp_t(float scale)
: data{static_cast<type>((bit_cast<uint32_t>(scale) & (nan_mask << 23)) >> 23)}
{
}
__host__ __device__ explicit constexpr operator float() const
{
if(data == nan_mask || data == 0)
{
uint32_t bits = data << 1;
bits |= 1;
bits <<= 22;
return bit_cast<float>(bits);
}
else
{
uint32_t bits = data << 23;
return bit_cast<float>(bits);
}
}
__host__ __device__ constexpr bool operator==(const e8m0_bexp_t& other) const
{
// strict IEEE compliance for NaN
return data == other.data && data != nan_mask;
}
__host__ __device__ constexpr bool is_nan() const { return data == nan_mask; }
};
namespace utils {
template <typename T>
__host__ __device__ inline int get_exponent_value(T x);
template <>
__host__ __device__ inline int get_exponent_value<e8m0_bexp_t>(e8m0_bexp_t x)
{
return x.data;
}
} // namespace utils
} // namespace ck
include/ck/utility/mxf4_utils.hpp 0 → 100644
View file @ 9ba504b6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/mxfp_utils.hpp"
namespace ck::utils {
template <>
__host__ __device__ inline bool is_nan<f4_t>(e8m0_bexp_t const scale,
f4_t const dataBytes [[maybe_unused]])
{
// no need to check for data as it does not have NaN representation
return scale == NumericLimits<e8m0_bexp_t>::QuietNaN();
}
// no infinity representation in ocp_e2m1_mxfp4 will always return false
template <>
__host__ __device__ inline bool is_inf<f4_t>(e8m0_bexp_t const scale [[maybe_unused]],
f4_t const data [[maybe_unused]])
{
// no inf representation for ocp_e2m1_mxfp4
return false;
}
template <>
__host__ __device__ inline bool is_zero<f4_t>(e8m0_bexp_t const scale, f4_t const data)
{
if(is_nan<f4_t>(scale, data))
return false;
// no need to check for scale as it does not have a 0 representation
f4_t result = (data & 0b00001111) & NumericUtils<f4_t>::set_sign_mask;
return result == 0b0;
}
template <>
__host__ __device__ inline float to_float<f4_t>(e8m0_bexp_t const scale, f4_t const data)
{
if(is_nan<f4_t>(scale, data))
return std::numeric_limits<float>::quiet_NaN();
if(is_zero<f4_t>(scale, data))
return 0.0f;
f4_t prepared_data = data & 0b00001111;
int scale_exp = get_exponent_value<e8m0_bexp_t>(scale);
return convert_to_float<f4_t>(prepared_data, scale_exp);
}
template <>
__host__ __device__ inline f4_t sat_convert_to_type<f4_t>(float value)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
{
return sign ? NumericUtils<f4_t>::data_max_negative_normal_mask
: NumericUtils<f4_t>::data_max_positive_normal_mask;
}
if(std::abs(value) > NumericLimits<f4_t>::Max()) // covers inf case as well
return sign ? NumericUtils<f4_t>::data_max_negative_normal_mask
: NumericUtils<f4_t>::data_max_positive_normal_mask;
f4_t res = convert_to_type<f4_t>(value);
if(std::abs(to_float<f4_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<f4_t>::DataMinSubnorm())
return value < 0 ? NumericUtils<f4_t>::negative_zero_mask
: NumericUtils<f4_t>::positive_zero_mask;
return res;
}
template <>
__host__ __device__ inline f4_t sat_convert_to_type_sr<f4_t>(float value, uint32_t seed)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
return sign ? NumericUtils<f4_t>::data_max_negative_normal_mask
: NumericUtils<f4_t>::data_max_positive_normal_mask;
if(std::abs(value) > NumericLimits<f4_t>::Max()) // covers inf case as well
return sign ? NumericUtils<f4_t>::data_max_negative_normal_mask
: NumericUtils<f4_t>::data_max_positive_normal_mask;
f4_t res = convert_to_type_sr<f4_t>(value, seed);
if(std::abs(to_float<f4_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<f4_t>::DataMinSubnorm())
return value < 0 ? NumericUtils<f4_t>::negative_zero_mask
: NumericUtils<f4_t>::positive_zero_mask;
return res;
}
} // namespace ck::utils
include/ck/utility/mxf6_utils.hpp 0 → 100644
View file @ 9ba504b6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/utility/data_type.hpp"
#include "ck/utility/mxfp_utils.hpp"
namespace ck::utils {
/**
* @brief Checks if an f6_t value is NaN based on the provided scale.
*
* For f6_t data, NaN cannot be represented directly. Instead, this function
* determines NaN by checking if the scale is set to a quiet NaN.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for f6_t.
* @param dataBytes The f6_t value to check (unused in this implementation).
* @return true if the scale indicates a NaN value, false otherwise.
*/
template <>
__host__ __device__ inline bool is_nan<f6_t>(e8m0_bexp_t const scale,
f6_t const dataBytes [[maybe_unused]])
{
// no need to check for data as it does not have NaN representation
return scale.is_nan();
}
/**
* @brief Checks if an bf6_t value is NaN based on the provided scale.
*
* For bf6_t data, NaN cannot be represented directly. Instead, this function
* determines NaN by checking if the scale is set to a quiet NaN.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for bf6_t.
* @param dataBytes The bf6_t value to check (unused in this implementation).
* @return true if the scale indicates a NaN value, false otherwise.
*/
template <>
__host__ __device__ inline bool is_nan<bf6_t>(e8m0_bexp_t const scale,
bf6_t const dataBytes [[maybe_unused]])
{
// no need to check for data as it does not have NaN representation
return scale.is_nan();
}
/**
* @brief Checks if an f6_t value is infinite.
*
* Because f6_t does not support infinite values, this function always returns false.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for f6_t.
* @param data The f6_t value to check.
* @return Always false, as infinity is not represented in f6_t.
*/
template <>
__host__ __device__ inline bool is_inf<f6_t>(e8m0_bexp_t const scale [[maybe_unused]],
f6_t const data [[maybe_unused]])
{
// no inf representation for fp6
return false;
}
/**
* @brief Checks if an bf6_t value is infinite.
*
* Because bf6_t does not support infinite values, this function always returns false.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for bf6_t.
* @param data The bf6_t value to check.
* @return Always false, as infinity is not represented in bf6_t.
*/
template <>
__host__ __device__ inline bool is_inf<bf6_t>(e8m0_bexp_t const scale [[maybe_unused]],
bf6_t const data [[maybe_unused]])
{
// no inf representation for bf6
return false;
}
/**
* @brief Checks whether an f6_t value is zero.
*
* If the specified f6_t is NaN, this function returns false.
* Otherwise, it masks out the sign bits and checks if the remaining bits
* are zero.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for f6_t.
* @param data The f6_t value to check.
* @return true if the value is zero; otherwise false.
*/
template <>
__host__ __device__ inline bool is_zero<f6_t>(e8m0_bexp_t const scale, f6_t const data)
{
if(is_nan<f6_t>(scale, data))
return false;
// no need to check for scale as it does not have a 0 representation
f6_t result = (data & 0b00111111) & NumericUtils<f6_t>::set_sign_mask;
return result == 0b0;
}
/**
* @brief Checks whether an bf6_t value is zero.
*
* If the specified bf6_t is NaN, this function returns false.
* Otherwise, it masks out the sign bits and checks if the remaining bits
* are zero.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for bf6_t.
* @param data The bf6_t value to check.
* @return true if the value is zero; otherwise false.
*/
template <>
__host__ __device__ inline bool is_zero<bf6_t>(e8m0_bexp_t const scale, bf6_t const data)
{
if(is_nan<bf6_t>(scale, data))
return false;
// no need to check for scale as it does not have a 0 representation
bf6_t result = (data & 0b00111111) & NumericUtils<bf6_t>::set_sign_mask;
return result == 0b0;
}
/**
* @brief Converts an f6_t value to a float based on an e8m0_bexp_t scale factor.
*
* Checks if the f6_t value is NaN or zero before performing the conversion.
* Applies the exponent from the scale to compute the final float result.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for f6_t.
* @param data The f6_t value to convert.
* @return The converted float value.
*/
template <>
__host__ __device__ inline float to_float<f6_t>(e8m0_bexp_t const scale, f6_t const data)
{
if(is_nan<f6_t>(scale, data))
return std::numeric_limits<float>::quiet_NaN();
if(is_zero<f6_t>(scale, data))
return 0.0f;
f6_t prepared_data = data & 0b00111111;
int scale_exp = get_exponent_value<e8m0_bexp_t>(scale);
return convert_to_float<f6_t>(prepared_data, scale_exp);
}
/**
* @brief Converts an bf6_t value to a float based on an e8m0_bexp_t scale factor.
*
* Checks if the bf6_t value is NaN or zero before performing the conversion.
* Applies the exponent from the scale to compute the final float result.
*
* @param scale The exponent scale factor (e8m0_bexp_t) used for bf6_t.
* @param data The bf6_t value to convert.
* @return The converted float value.
*/
template <>
__host__ __device__ inline float to_float<bf6_t>(e8m0_bexp_t const scale, bf6_t const data)
{
if(is_nan<bf6_t>(scale, data))
return std::numeric_limits<float>::quiet_NaN();
if(is_zero<bf6_t>(scale, data))
return 0.0f;
bf6_t prepared_data = data & 0b00111111;
int scale_exp = get_exponent_value<e8m0_bexp_t>(scale);
return convert_to_float<bf6_t>(prepared_data, scale_exp);
}
/**
* @brief Converts a float to f6_t with saturation.
*
* If the input is NaN or exceeds the representable range for f6_t, returns
* the corresponding max normal mask. Handles subnormal cases by returning
* zero with the appropriate sign.
*
* @param value The float value to be converted.
* @return The saturated f6_t value.
*/
template <>
__host__ __device__ inline f6_t sat_convert_to_type<f6_t>(float value)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
{
return sign ? NumericUtils<f6_t>::data_max_negative_normal_mask
: NumericUtils<f6_t>::data_max_positive_normal_mask;
}
if(std::abs(value) > NumericLimits<f6_t>::Max()) // covers inf case as well
return sign ? NumericUtils<f6_t>::data_max_negative_normal_mask
: NumericUtils<f6_t>::data_max_positive_normal_mask;
f6_t res = convert_to_type<f6_t>(value);
if(std::abs(to_float<f6_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<f6_t>::DataMinSubnorm())
return sign ? NumericUtils<f6_t>::negative_zero_mask
: NumericUtils<f6_t>::positive_zero_mask;
return res;
}
/**
* @brief Converts a float to bf6_t with saturation.
*
* If the input is NaN or exceeds the representable range for bf6_t, returns
* the corresponding max normal mask. Handles subnormal cases by returning
* zero with the appropriate sign.
*
* @param value The float value to be converted.
* @return The saturated bf6_t value.
*/
template <>
__host__ __device__ inline bf6_t sat_convert_to_type<bf6_t>(float value)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
{
return sign ? NumericUtils<bf6_t>::data_max_negative_normal_mask
: NumericUtils<bf6_t>::data_max_positive_normal_mask;
}
if(std::abs(value) > NumericLimits<bf6_t>::Max()) // covers inf case as well
return sign ? NumericUtils<bf6_t>::data_max_negative_normal_mask
: NumericUtils<bf6_t>::data_max_positive_normal_mask;
bf6_t res = convert_to_type<bf6_t>(value);
if(std::abs(to_float<bf6_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<bf6_t>::DataMinSubnorm())
return sign ? NumericUtils<bf6_t>::negative_zero_mask
: NumericUtils<bf6_t>::positive_zero_mask;
return res;
}
/**
* @brief Converts a float to f6_t with saturation and stochastic rounding.
*
* If the input is NaN or exceeds the representable range for f6_t, returns
* the corresponding max normal mask. Handles subnormal cases by returning
* zero with the appropriate sign.
*
* @param value The float value to be converted.
* @return The saturated f6_t value.
*/
template <>
__host__ __device__ inline f6_t sat_convert_to_type_sr<f6_t>(float value, uint32_t seed)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
return sign ? NumericUtils<f6_t>::data_max_negative_normal_mask
: NumericUtils<f6_t>::data_max_positive_normal_mask;
if(std::abs(value) > NumericLimits<f6_t>::Max()) // covers inf case as well
return sign ? NumericUtils<f6_t>::data_max_negative_normal_mask
: NumericUtils<f6_t>::data_max_positive_normal_mask;
f6_t res = convert_to_type_sr<f6_t>(value, seed);
if(std::abs(to_float<f6_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<f6_t>::DataMinSubnorm())
return sign ? NumericUtils<f6_t>::negative_zero_mask
: NumericUtils<f6_t>::positive_zero_mask;
return res;
}
/**
* @brief Converts a float to f6_t with saturation and stochastic rounding.
*
* If the input is NaN or exceeds the representable range for f6_t, returns
* the corresponding max normal mask. Handles subnormal cases by returning
* zero with the appropriate sign.
*
* @param value The float value to be converted.
* @return The saturated f6_t value.
*/
template <>
__host__ __device__ inline bf6_t sat_convert_to_type_sr<bf6_t>(float value, uint32_t seed)
{
cvt t;
t.value_float = value;
uint32_t sign = t.value_bitwise >> 31;
if(std::isnan(value))
return sign ? NumericUtils<bf6_t>::data_max_negative_normal_mask
: NumericUtils<bf6_t>::data_max_positive_normal_mask;
if(std::abs(value) > NumericLimits<bf6_t>::Max()) // covers inf case as well
return sign ? NumericUtils<bf6_t>::data_max_negative_normal_mask
: NumericUtils<bf6_t>::data_max_positive_normal_mask;
bf6_t res = convert_to_type_sr<bf6_t>(value, seed);
if(std::abs(to_float<bf6_t>(NumericLimits<e8m0_bexp_t>::Binary_1(), res)) <
NumericLimits<bf6_t>::DataMinSubnorm())
return sign ? NumericUtils<bf6_t>::negative_zero_mask
: NumericUtils<bf6_t>::positive_zero_mask;
return res;
}
} // namespace ck::utils
include/ck/utility/mxf8_utils.hpp 0 → 100644
View file @ 9ba504b6
This diff is collapsed.
include/ck/utility/mxfp_utils.hpp 0 → 100644
View file @ 9ba504b6
// SPDX-License-Identifier: MIT
// Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
namespace ck::utils {
union cvt
{
float value_float;
uint32_t value_bitwise;
};
template <typename DTYPE>
inline bool getDataHasInf()
{
return DTYPE::dataInfo.hasInf;
}
template <typename T>
__host__ __device__ inline bool is_zero(e8m0_bexp_t const scale, T const data);
template <typename T>
__host__ __device__ inline bool is_nan(e8m0_bexp_t const scale, T const data);
template <typename T>
__host__ __device__ inline bool is_inf(e8m0_bexp_t const scale, T const data);
template <typename T>
__host__ __device__ inline int get_exponent_value(T x)
{
x >>= NumericUtils<T>::mant;
x &= ((1 << NumericUtils<T>::exp) - 1);
return static_cast<int>(x);
}
template <typename T>
__host__ __device__ inline bool is_subnormal(T x)
{
return get_exponent_value<T>(x) == 0;
}
template <typename T>
__host__ __device__ inline double get_mantissa_value(T x)
{
double mantissa = is_subnormal<T>(x) ? 0.0f : 1.0f;
for(uint i = 0; i < NumericUtils<T>::mant; i++)
{
mantissa += std::pow(2, -int32_t((NumericUtils<T>::mant - i))) * (x & 0b1);
x >>= 1;
}
return mantissa;
}
template <typename T>
__host__ __device__ inline bool get_data_has_inf()
{
return NumericUtils<T>::has_inf;
}
template <typename T>
__host__ __device__ float convert_to_float(T data, int scale_exp)
{
float d_sign =
std::pow(-1, static_cast<float>(data >> (NumericUtils<T>::exp + NumericUtils<T>::mant)));
float d_exp;
if(is_subnormal<T>(data))
d_exp = std::pow(2, 1 - static_cast<int>(NumericUtils<T>::bias));
else
d_exp = std::pow(2, get_exponent_value<T>(data) - static_cast<int>(NumericUtils<T>::bias));
float d_mant = get_mantissa_value<T>(data);
float data_value = d_sign * d_exp * d_mant;
float scale_value = std::pow(
2, static_cast<float>((scale_exp - static_cast<int>(NumericUtils<e8m0_bexp_t>::bias))));
return data_value * scale_value;
}
template <typename T>
__host__ __device__ inline float to_float(e8m0_bexp_t const scale, T const data);
template <typename T>
__host__ __device__ T sat_convert_to_type(float value);
template <typename T>
__host__ __device__ T sat_convert_to_type_sr(float value, uint32_t seed);
template <typename T>
inline T convert_to_type(float value)
{
using bitwise_type = typename NumericUtils<T>::bitwise_type;
if(std::abs(value) > NumericLimits<T>::Max())
{
float max_value = NumericLimits<T>::Max();
cvt t;
// cppcheck-suppress redundantAssignment
t.value_float = max_value;
uint32_t max_bitwise = t.value_bitwise;
// cppcheck-suppress redundantAssignment
t.value_float = value;
bitwise_type sign =
t.value_bitwise >> (NumericUtils<float>::exp + NumericUtils<float>::mant);
bitwise_type exp =
((max_bitwise >> NumericUtils<float>::mant) & NumericUtils<float>::exp_mask) -
(NumericUtils<float>::bias - NumericUtils<T>::bias);
bitwise_type mantissa = max_bitwise >> (NumericUtils<float>::mant - NumericUtils<T>::mant);
uint32_t mant_prev = max_bitwise >> (NumericUtils<float>::mant - NumericUtils<T>::mant);
mant_prev &= ((1 << NumericUtils<T>::mant) - 1);
mant_prev--;
mant_prev <<= (NumericUtils<float>::mant - NumericUtils<T>::mant);
uint32_t prev_bit =
((max_bitwise >> NumericUtils<float>::mant) << NumericUtils<float>::mant) | mant_prev;
t.value_bitwise = prev_bit;
float prev_val = t.value_float;
float diff = max_value - prev_val;
float actual_max = max_value + (diff / 2);
if(std::abs(value) < actual_max)
{
return sign << ((NumericUtils<T>::exp + NumericUtils<T>::mant)) |
(exp << NumericUtils<T>::mant) | mantissa;
}
else
{
if(!get_data_has_inf<T>())
{
return (1 << (NumericUtils<T>::mant + NumericUtils<T>::exp)) - 1;
}
else
{
exp++;
return sign << ((NumericUtils<T>::exp + NumericUtils<T>::mant)) |
(exp << NumericUtils<T>::mant);
}
}
}
const int mfmt = NumericUtils<float>::mant;
uint32_t x;
x = bit_cast<uint32_t>(value);
uint32_t head, mantissa;
int32_t exponent, bias;
uint32_t sign;
head = x & NumericUtils<float>::head_mask;
mantissa = x & NumericUtils<float>::mant_mask;
exponent = (head >> NumericUtils<float>::mant) & NumericUtils<float>::exp_mask;
sign = head >> (NumericUtils<float>::mant + NumericUtils<float>::exp);
bias = NumericUtils<float>::bias;
if(x == 0)
{
return 0b0;
}
const int mini_bias = NumericUtils<T>::bias;
const int mini_denormal_act_exponent = 1 - mini_bias;
int act_exponent, out_exponent, exponent_diff;
bool is_subnorm = false;
if(exponent == 0)
{
act_exponent = exponent - bias + 1;
exponent_diff = mini_denormal_act_exponent - act_exponent;
is_subnorm = true;
}
else
{
act_exponent = exponent - bias;
if(act_exponent <= mini_denormal_act_exponent)
{
exponent_diff = mini_denormal_act_exponent - act_exponent;
is_subnorm = true;
}
else
{
exponent_diff = 0;
}
mantissa += (1UL << mfmt);
}
auto shift_amount = (mfmt - NumericUtils<T>::mant + exponent_diff);
shift_amount = (shift_amount >= 64) ? 63 : shift_amount;
bool midpoint = (mantissa & ((1UL << shift_amount) - 1)) == (1UL << (shift_amount - 1));
float min_subnorm = NumericLimits<T>::DataMinSubnorm() * (sign ? -1 : 1);
if(is_subnorm && std::abs(value) < std::abs(min_subnorm))
{
// closer to 0
if(std::abs(value) <= std::abs(min_subnorm - value))
return 0;
else
return 1 | (sign << (NumericUtils<T>::exp + NumericUtils<T>::mant));
}
if(exponent_diff > 0)
mantissa >>= exponent_diff;
else if(exponent_diff == -1)
mantissa <<= -exponent_diff;
bool implicit_one = mantissa & (1 << mfmt);
out_exponent = (act_exponent + exponent_diff) + mini_bias - (implicit_one ? 0 : 1);
uint32_t drop_mask = (1UL << (mfmt - NumericUtils<T>::mant)) - 1;
bool odd = mantissa & (1UL << (mfmt - NumericUtils<T>::mant));
mantissa += (midpoint ? (odd ? mantissa : mantissa - 1) : mantissa) & drop_mask;
if(out_exponent == 0)
{
if((1UL << mfmt) & mantissa)
{
out_exponent = 1;
}
}
else
{
if((1UL << (mfmt + 1)) & mantissa)
{
mantissa >>= 1;
out_exponent++;
}
}
mantissa >>= (mfmt - NumericUtils<T>::mant);
if(out_exponent == 0 && mantissa == 0)
{
return 0;
}
mantissa &= (1UL << NumericUtils<T>::mant) - 1;
return (sign << (NumericUtils<T>::exp + NumericUtils<T>::mant)) |
(out_exponent << NumericUtils<T>::mant) | mantissa;
}
template <typename T>
inline T convert_to_type_sr(float value, uint32_t seed)
{
if(std::abs(value) > NumericLimits<T>::Max())
{
float max_value = NumericLimits<T>::Max();
cvt t;
// cppcheck-suppress redundantAssignment
t.value_float = max_value;
uint max_bitwise = t.value_bitwise;
// cppcheck-suppress redundantAssignment
t.value_float = value;
T sign = t.value_bitwise >> (NumericUtils<float>::exp + NumericUtils<float>::mant);
T exp = ((max_bitwise >> NumericUtils<float>::mant) & NumericUtils<float>::exp_mask) -
(NumericUtils<float>::bias - NumericUtils<T>::bias);
uint32_t mant_prev = max_bitwise >> (NumericUtils<float>::mant - NumericUtils<T>::mant);
mant_prev &= ((1UL << NumericUtils<T>::mant) - 1);
mant_prev--;
mant_prev <<= (NumericUtils<float>::mant - NumericUtils<T>::mant);
uint32_t prev_bit =
((max_bitwise >> NumericUtils<float>::mant) << NumericUtils<float>::mant) | mant_prev;
t.value_bitwise = prev_bit;
float prev_val = t.value_float;
float diff = max_value - prev_val;
float actual_max = max_value + (diff / 2);
if(std::abs(value) < actual_max)
{
double d_max_value = static_cast<double>(max_value);
double d_actual_max = static_cast<double>(actual_max);
double d_value = static_cast<double>(value);
double d_is = std::abs(d_max_value - d_actual_max);
double d_seed = static_cast<double>(seed);
double d_prob = 1.0f - (std::abs(d_value - d_max_value) / d_is); // prob to round down
double thresh = UINT_MAX * d_prob;
if(!get_data_has_inf<T>() || d_seed <= thresh)
// return static_cast<T>(satConvertToType(getDataMax<DTYPE>())); //round down time
return sign == 0 ? NumericUtils<f4_t>::data_max_positive_normal_mask
: NumericUtils<f4_t>::data_max_negative_normal_mask;
else
{
exp++;
return sign << ((NumericUtils<T>::exp + NumericUtils<T>::mant)) // inf
| (exp << NumericUtils<T>::mant);
}
}
else
{
if(!get_data_has_inf<T>())
return (1 << (NumericUtils<T>::mant + NumericUtils<T>::exp)) - 1;
else
{
exp++;
return sign << ((NumericUtils<T>::exp + NumericUtils<T>::mant)) // inf
| (exp << NumericUtils<T>::mant);
}
}
}
uint32_t f32 = bit_cast<uint32_t>(value);
auto f32_mant = f32 & NumericUtils<float>::mant_mask;
auto head = f32 & NumericUtils<float>::head_mask;
auto f32_exp = (head >> NumericUtils<float>::mant) & NumericUtils<float>::exp_mask;
auto sign_bit = head >> (NumericUtils<float>::mant + NumericUtils<float>::exp);
auto sign = sign_bit << (NumericUtils<T>::exp + NumericUtils<T>::mant);
f32_exp = static_cast<int32_t>(f32_exp) - NumericUtils<float>::bias;
int32_t exp = f32_exp;
auto mant = f32_mant;
bool subnorm = false;
if(f32 == 0)
return 0b0;
if(exp >= NumericUtils<T>::unbiased_exp_min)
{
mant = f32_mant;
}
// if the exponent bit is 8, then the subnormal is exactly the same as f32
else if(exp < NumericUtils<T>::unbiased_exp_min &&
NumericUtils<T>::exp < NumericUtils<float>::exp)
{
subnorm = true;
auto diff = static_cast<uint32_t>(NumericUtils<T>::unbiased_exp_min - exp);
if(diff >= 32)
{
mant = 0;
f32_mant = 0;
}
else
{
f32_mant |= static_cast<uint32_t>(1) << NumericUtils<float>::mant;
f32_mant >>= diff;
}
exp = 0;
mant = f32_mant;
}
uint32_t sr_shift = NumericUtils<T>::sr_shift;
// For stochastic-rounding we add the aligned random value to the
// mantissa and then truncate (RTZ).
mant += seed >> sr_shift;
// Increment exponent when mantissa overflows due to rounding
if(mant >= static_cast<uint32_t>(1) << NumericUtils<float>::mant)
++exp;
mant >>= (NumericUtils<float>::mant - NumericUtils<T>::mant);
mant &= ((1 << NumericUtils<T>::mant) - 1);
auto biased_exp = static_cast<uint32_t>(exp);
if(!subnorm)
biased_exp = static_cast<uint32_t>(exp + NumericUtils<T>::bias);
biased_exp &= ((1 << NumericUtils<T>::exp) - 1);
auto val = sign | biased_exp << NumericUtils<T>::mant | mant;
return val;
}
} // namespace ck::utils
include/ck/utility/scaled_type_convert.hpp 0 → 100644
View file @ 9ba504b6
This diff is collapsed.
include/ck/utility/type_convert.hpp
View file @ 9ba504b6
This diff is collapsed.
include/ck_tile/core.hpp
View file @ 9ba504b6
......@@ -32,6 +32,7 @@
#include "ck_tile/core/numeric/math.hpp"
#include "ck_tile/core/numeric/null_type.hpp"
#include "ck_tile/core/numeric/numeric.hpp"
#include "ck_tile/core/numeric/pk_int4.hpp"
#include "ck_tile/core/numeric/type_convert.hpp"
#include "ck_tile/core/numeric/vector_type.hpp"
#include "ck_tile/core/tensor/buffer_view.hpp"
......
include/ck_tile/core/config.hpp
View file @ 9ba504b6
......@@ -144,6 +144,10 @@
#define CK_TILE_USE_AMD_BUFFER_ATOMIC_ADD_INTEGER 1
#endif
#ifndef CK_TILE_USE_PK4_LAYOUT_SHUFFLE
#define CK_TILE_USE_PK4_LAYOUT_SHUFFLE 1
#endif
// buffer atomic add: floating point
#ifndef __HIP_DEVICE_COMPILE__ // for host code
#define CK_TILE_USE_AMD_BUFFER_ATOMIC_ADD_FLOAT 1
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment