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Ding, Yi
2025-05-28 03:35:33 +00:00
parent cf5b4c11a2
commit b99c50a5d5
4 changed files with 78 additions and 100 deletions
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85
example/67_gemm_microscaling/gemm_mx_common.hpp
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@@ -110,7 +110,7 @@ bool parse_cmd_args(int argc,
#if 1
template <bool KLast>
void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int MN_dst, int K)
void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int K)
{
int MNXdlPack = 2;
int KXdlPack = 2;
@@ -128,40 +128,32 @@ void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, i
// To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
// Then, MNRepeat->KRepeat
const auto fn = [&](auto IS_PADDED, int n, int k) {
constexpr auto IS_PADDED_V = decltype(IS_PADDED)::value;
int n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
int tempn = n % (XdlMNThread * MNXdlPack);
int n1 = tempn % XdlMNThread; // i XdlMNThread
int n2 = tempn / XdlMNThread; // i MNXdlPack
int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
int tempk = k % (XdlKThread * KXdlPack);
int k1 = tempk % XdlKThread; // i XdlKThread
int k2 = tempk / XdlKThread; // i KXdlPack
int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
k2 * MNXdlPack + n2;
// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
// k2 * MNXdlPack)));
if constexpr(IS_PADDED_V)
dst[outputIndex] = 0;
else if constexpr(KLast)
dst[outputIndex] = src[n * K + k];
else
dst[outputIndex] = src[k * MN + n];
};
int n = 0;
for(; n < MN; ++n)
for(int n = 0; n < MN; ++n)
{
for(int k = 0; k < K; ++k)
fn(ck::false_type{}, n, k);
for(; n < MN_dst; ++n)
for(int k = 0; k < K; ++k)
fn(ck::true_type{}, n, k);
{
int n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
int tempn = n % (XdlMNThread * MNXdlPack);
int n1 = tempn % XdlMNThread; // i XdlMNThread
int n2 = tempn / XdlMNThread; // i MNXdlPack
int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
int tempk = k % (XdlKThread * KXdlPack);
int k1 = tempk % XdlKThread; // i XdlKThread
int k2 = tempk / XdlKThread; // i KXdlPack
int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
k2 * MNXdlPack + n2;
// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
// k2 * MNXdlPack)));
if constexpr(KLast)
dst[outputIndex] = src[n * K + k];
else
dst[outputIndex] = src[k * MN + n];
}
}
}
void preShuffleBuffer(const ck::f4x2_pk_t* src, ck::f4x2_pk_t* dst, int N, int K, int NXdl)
@@ -258,11 +250,10 @@ bool run_mx_gemm(const ProblemSizeSplitK& problem_size, const ExecutionConfig& c
using AScaleLayout = Row;
using BScaleLayout = Col;
auto Scale_Stride_AM = f_get_default_stride(M, K / ScaleBlockSize, -1, AScaleLayout{});
auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
auto Scale_Padded_M = (M + 32 - 1) / 32 * 32;
auto Scale_Padded_Stride_AM =
auto Scale_Padded_M = (M + 32 - 1) / 32 * 32;
auto Scale_Stride_AM =
f_get_default_stride(Scale_Padded_M, K / ScaleBlockSize, -1, AScaleLayout{});
auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
auto b_k_n =
@@ -273,14 +264,14 @@ bool run_mx_gemm(const ProblemSizeSplitK& problem_size, const ExecutionConfig& c
f_host_tensor_descriptor(K, N, StrideB, BLayout{})); // use layout only for size
// scales for A and B
Tensor<XDataType> a_m_k_scale(
f_host_tensor_descriptor(M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> a_m_k_scale(f_host_tensor_descriptor(
Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> b_k_n_scale(
f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
// shuffled scales for A and B
Tensor<XDataType> a_shuffled_scale(f_host_tensor_descriptor(
Scale_Padded_M, K / ScaleBlockSize, Scale_Padded_Stride_AM, AScaleLayout{}));
Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> b_shuffled_scale(
f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
@@ -355,14 +346,12 @@ bool run_mx_gemm(const ProblemSizeSplitK& problem_size, const ExecutionConfig& c
}
#if 1
preShuffleScaleBuffer<ck::is_same_v<ALayout, Row>>( //
a_m_k_scale.mData.data(),
a_shuffled_scale.mData.data(),
M,
Scale_Padded_M,
K / ScaleBlockSize);
preShuffleScaleBuffer<ck::is_same_v<ALayout, Row>>(a_m_k_scale.mData.data(),
a_shuffled_scale.mData.data(),
Scale_Padded_M,
K / ScaleBlockSize);
preShuffleScaleBuffer<ck::is_same_v<BLayout, Col>>(
b_k_n_scale.mData.data(), b_shuffled_scale.mData.data(), N, N, K / ScaleBlockSize);
b_k_n_scale.mData.data(), b_shuffled_scale.mData.data(), N, K / ScaleBlockSize);
if constexpr(BPreShuffle)
{
int NPerXdl = 16; // Fixed 16

4
example/67_gemm_microscaling/gemm_mx_fp4.cpp
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@@ -50,14 +50,14 @@ using DeviceOpInstance = ck::tensor_operation::device::DeviceGemmMX_Xdl_CShuffle
GemmSpec, // GemmSpec
ScaleBlockSize, // ScaleBlockSize: Scaling block size
256, // BlockSize: Thread block size
256, // MPerBlock
128, // MPerBlock
256, // NPerBlock
KPerBlock, // KPerBlock
16, // AK1
16, // BK1
16, // MPerXDL
16, // NPerXDL
8, // MXdlPerWave
4, // MXdlPerWave
8, // NXdlPerWave
S<8, 32, 1>, // ABlockTransferThreadClusterLengths_AK0_M_AK1
S<1, 0, 2>, // ABlockTransferThreadClusterArrangeOrder

4
library/src/tensor_operation_instance/gpu/gemm_mx/device_gemm_mx_xdl_f4_f4_f16/device_gemm_mx_xdl_f4_f4_f16_mk_nk_mn.hpp
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@@ -49,8 +49,8 @@ using device_gemm_mx_xdl_f4_f4_f16_mk_nk_mn_instances = std::tuple<
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 32, 256, 128, 16, 16, 16, 16, 2, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 64, 128, 128, 16, 16, 16, 16, 4, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 64, 256, 128, 16, 16, 16, 16, 4, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 96, 128, 128, 16, 16, 16, 16, 6, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 96, 256, 128, 16, 16, 16, 16, 6, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
// DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 96, 128, 128, 16, 16, 16, 16, 6, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
// DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 96, 256, 128, 16, 16, 16, 16, 6, 4, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v1>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 256, 256, 128, 16, 16, 16, 16, 8, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v3>,
DeviceGemmMX_Xdl_CShuffleV3< Row, Col, Row, F4, E8M0PK, F4, E8M0PK, F16, F32, F16, PassThrough, PassThrough, PassThrough, GemmSpec, ScaleBlockSize, 256, 128, 256, 128, 16, 16, 16, 16, 4, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, true, 2, 2, S<1, 32, 1, 8>, 8, BlkGemmPipeSched, BlockGemmPipelineVersion::v3>,

85
profiler/include/profiler/profile_gemm_mx_impl.hpp
View File

@@ -26,7 +26,7 @@ namespace profiler {
#if 1
template <bool KLast>
void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int MN_dst, int K)
void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, int K)
{
int MNXdlPack = 2;
int KXdlPack = 2;
@@ -44,40 +44,32 @@ void preShuffleScaleBuffer(ck::e8m0_bexp_t* src, ck::e8m0_bexp_t* dst, int MN, i
// To XdlKThread-> XdlMNThread -> KXdlPack -> MNXdlPack
// Then, MNRepeat->KRepeat
const auto fn = [&](auto IS_PADDED, int n, int k) {
constexpr auto IS_PADDED_V = decltype(IS_PADDED)::value;
int n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
int tempn = n % (XdlMNThread * MNXdlPack);
int n1 = tempn % XdlMNThread; // i XdlMNThread
int n2 = tempn / XdlMNThread; // i MNXdlPack
int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
int tempk = k % (XdlKThread * KXdlPack);
int k1 = tempk % XdlKThread; // i XdlKThread
int k2 = tempk / XdlKThread; // i KXdlPack
int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
k2 * MNXdlPack + n2;
// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
// k2 * MNXdlPack)));
if constexpr(IS_PADDED_V)
dst[outputIndex] = 0;
else if constexpr(KLast)
dst[outputIndex] = src[n * K + k];
else
dst[outputIndex] = src[k * MN + n];
};
int n = 0;
for(; n < MN; ++n)
for(int n = 0; n < MN; ++n)
{
for(int k = 0; k < K; ++k)
fn(ck::false_type{}, n, k);
for(; n < MN_dst; ++n)
for(int k = 0; k < K; ++k)
fn(ck::true_type{}, n, k);
{
int n0 = n / (XdlMNThread * MNXdlPack); // i MNRepeat
int tempn = n % (XdlMNThread * MNXdlPack);
int n1 = tempn % XdlMNThread; // i XdlMNThread
int n2 = tempn / XdlMNThread; // i MNXdlPack
int k0 = k / (XdlKThread * KXdlPack); // i KRepeat
int tempk = k % (XdlKThread * KXdlPack);
int k1 = tempk % XdlKThread; // i XdlKThread
int k2 = tempk / XdlKThread; // i KXdlPack
int outputIndex = n0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread * K0 +
k0 * MNXdlPack * KXdlPack * XdlMNThread * XdlKThread +
k1 * MNXdlPack * KXdlPack * XdlMNThread + n1 * MNXdlPack * KXdlPack +
k2 * MNXdlPack + n2;
// src[n * K + k] = ck::type_convert<ck::e8m0_bexp_t>(static_cast<float>(powf(2.0f, n2 +
// k2 * MNXdlPack)));
if constexpr(KLast)
dst[outputIndex] = src[n * K + k];
else
dst[outputIndex] = src[k * MN + n];
}
}
}
#endif
@@ -140,24 +132,23 @@ bool profile_gemm_mx_impl(int do_verification,
return static_cast<ck::index_t>(stride);
};
auto Scale_Stride_AM = f_get_default_stride(M, K / ScaleBlockSize, -1, AScaleLayout{});
auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
auto Scale_Padded_M = (M + 32 - 1) / 32 * 32;
auto Scale_Padded_Stride_AM =
auto Scale_Padded_M = (M + 32 - 1) / 32 * 32;
auto Scale_Stride_AM =
f_get_default_stride(Scale_Padded_M, K / ScaleBlockSize, -1, AScaleLayout{});
auto Scale_Stride_BN = f_get_default_stride(K / ScaleBlockSize, N, -1, BScaleLayout{});
Tensor<ADataType> a_m_k(f_host_tensor_descriptor(M, K, StrideA, ALayout{}));
Tensor<BDataType> b_k_n(f_host_tensor_descriptor(K, N, StrideB, BLayout{}));
// scales for A and B
Tensor<XDataType> a_m_k_scale(
f_host_tensor_descriptor(M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> a_m_k_scale(f_host_tensor_descriptor(
Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> b_k_n_scale(
f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
// shuffled scales for A and B
Tensor<XDataType> a_shuffled_scale(f_host_tensor_descriptor(
Scale_Padded_M, K / ScaleBlockSize, Scale_Padded_Stride_AM, AScaleLayout{}));
Scale_Padded_M, K / ScaleBlockSize, Scale_Stride_AM, AScaleLayout{}));
Tensor<XDataType> b_shuffled_scale(
f_host_tensor_descriptor(K / ScaleBlockSize, N, Scale_Stride_BN, BScaleLayout{}));
@@ -232,14 +223,12 @@ bool profile_gemm_mx_impl(int do_verification,
}
#if 1
preShuffleScaleBuffer<ck::is_same_v<ALayout, Row>>( //
a_m_k_scale.mData.data(),
a_shuffled_scale.mData.data(),
M,
Scale_Padded_M,
K / ScaleBlockSize);
preShuffleScaleBuffer<ck::is_same_v<ALayout, Row>>(a_m_k_scale.mData.data(),
a_shuffled_scale.mData.data(),
Scale_Padded_M,
K / ScaleBlockSize);
preShuffleScaleBuffer<ck::is_same_v<BLayout, Col>>(
b_k_n_scale.mData.data(), b_shuffled_scale.mData.data(), N, N, K / ScaleBlockSize);
b_k_n_scale.mData.data(), b_shuffled_scale.mData.data(), N, K / ScaleBlockSize);
#endif
using AElementOp = ck::tensor_operation::element_wise::PassThrough;