mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-03-27 02:27:37 +00:00
d460ab35b6
[CK_TILE] add tf32 support MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Proposed changes TF32 is added in CK on gfx942 and gfx950. This PR is to initiate tf32 in CK_TILE on gfx942 and gfx950. ## Checklist Please put an into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask. - [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally - [ ] I have added the test to REGRESSION_TESTS list defined at the top of CMakeLists.txt in tests/CMakeLists.txt, **IF** the test takes more than 30 seconds to run. - [ ] I have added inline documentation which enables the maintainers with understanding the motivation - [ ] I have removed the stale documentation which is no longer relevant after this pull request - [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request - [x] I have run on all changed files - [ ] Any dependent changes have been merged ## Discussion
1200 lines
49 KiB
C++
1200 lines
49 KiB
C++
// Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
|
|
// SPDX-License-Identifier: MIT
|
|
|
|
#pragma once
|
|
|
|
#include <cstdlib>
|
|
#include <thread>
|
|
|
|
#include "ck_tile/core.hpp"
|
|
#include "ck_tile/host/host_tensor.hpp"
|
|
#include "ck_tile/host/device_prop.hpp"
|
|
|
|
namespace ck_tile {
|
|
|
|
template <typename ADataType,
|
|
typename QDataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename QuantGroupSize,
|
|
bool aquant,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_gemm_quant(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<QDataType>& q,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
|
|
constexpr std::size_t kGroupK = QuantGroupSize::kK;
|
|
|
|
// ---- A loader: dequant A(m,k) into AccDataType ----
|
|
auto load_a = [&](std::size_t k) -> AccDataType {
|
|
if constexpr(std::is_same_v<ADataType, pk_int4_t>)
|
|
{
|
|
const pk_int4_t pk_val = a_element_op(a_m_k(m, k));
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
|
|
return (k & 1) ? fp32_val.hi : fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
return ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
|
|
}
|
|
};
|
|
|
|
// ---- B loader: dequant B(k,n) into AccDataType ----
|
|
auto load_b = [&](std::size_t k) -> AccDataType {
|
|
if constexpr(std::is_same_v<BDataType, pk_int4_t>)
|
|
{
|
|
const pk_int4_t pk_val = b_element_op(b_k_n(k, n));
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
|
|
return (k & 1) ? fp32_val.hi : fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<BDataType, fp8_t>)
|
|
{
|
|
return fp8_to_float_raw(b_element_op(b_k_n(k, n)));
|
|
}
|
|
else
|
|
{
|
|
return ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
|
|
}
|
|
};
|
|
|
|
// ---- scale loader for a given K-group index ----
|
|
auto load_scale = [&](ck_tile::index_t k_group) -> float {
|
|
const ck_tile::index_t outer_dim = aquant ? (m / QuantGroupSize::kM) : k_group;
|
|
const ck_tile::index_t inner_dim = aquant ? k_group : (n / QuantGroupSize::kN);
|
|
|
|
if constexpr(std::is_same_v<QDataType, float>)
|
|
{
|
|
return q(outer_dim, inner_dim);
|
|
}
|
|
else if constexpr(std::is_same_v<QDataType, ck_tile::fp8_t>)
|
|
{
|
|
return fp8_to_float_raw(q(outer_dim, inner_dim));
|
|
}
|
|
else // QDataType == bf8_t by static_assert above
|
|
{
|
|
return bf8_to_float_raw(q(outer_dim, inner_dim));
|
|
}
|
|
};
|
|
|
|
// ---- Loop over K by groups (full and tail) ----
|
|
for(std::size_t k_begin = 0; k_begin < K; k_begin += kGroupK)
|
|
{
|
|
const std::size_t k_end = std::min<std::size_t>(k_begin + kGroupK, K);
|
|
|
|
AccDataType v_block_acc = 0;
|
|
|
|
// unscaled accumulation within this K-group
|
|
for(std::size_t k = k_begin; k < k_end; ++k)
|
|
{
|
|
const AccDataType v_a = load_a(k);
|
|
const AccDataType v_b = load_b(k);
|
|
v_block_acc += v_a * v_b;
|
|
}
|
|
|
|
const ck_tile::index_t k_group = static_cast<ck_tile::index_t>(k_begin / kGroupK);
|
|
const float scale = load_scale(k_group);
|
|
|
|
v_acc += v_block_acc * scale;
|
|
}
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
std::cout << std::endl;
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename AQDataType,
|
|
typename BDataType,
|
|
typename BQDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AQuantGroupSize,
|
|
typename BQuantGroupSize,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_gemm_abquant(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<AQDataType>& a_q,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
const HostTensor<BQDataType>& b_q,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
constexpr auto A_TENSOR_M_DIM = 0;
|
|
constexpr auto A_TENSOR_K_DIM = 1;
|
|
constexpr auto B_TENSOR_K_DIM = 0;
|
|
constexpr auto B_TENSOR_N_DIM = 1;
|
|
|
|
const std::size_t M = a_m_k.get_length(A_TENSOR_M_DIM);
|
|
const std::size_t N = b_k_n.get_length(B_TENSOR_N_DIM);
|
|
const std::size_t K = a_m_k.get_length(A_TENSOR_K_DIM);
|
|
|
|
// Pre-convert A/B tensors to AccData type
|
|
// This prevents doing slow reconversions for each row/column
|
|
HostTensor<AccDataType> a_acc(a_m_k.mDesc);
|
|
HostTensor<AccDataType> b_acc(b_k_n.mDesc);
|
|
|
|
a_acc.ForEach([&](auto& self, auto index) {
|
|
if constexpr(std::is_same_v<ADataType, pk_int4_t> || std::is_same_v<ADataType, pk_fp4_t>)
|
|
{
|
|
const ADataType pk_val = a_element_op(a_m_k(index));
|
|
const fp32x2_t fp32_val = pk_val.to_fp32x2();
|
|
self(index) = (index[A_TENSOR_K_DIM] & 1) ? fp32_val.hi : fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
self(index) = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(index)));
|
|
}
|
|
});
|
|
|
|
b_acc.ForEach([&](auto& self, auto index) {
|
|
if constexpr(std::is_same_v<BDataType, pk_int4_t> || std::is_same_v<BDataType, pk_fp4_t>)
|
|
{
|
|
const BDataType pk_val = b_element_op(b_k_n(index));
|
|
const fp32x2_t fp32_val = pk_val.to_fp32x2();
|
|
self(index) = (index[B_TENSOR_K_DIM] & 1) ? fp32_val.hi : fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<BDataType, fp8_t>)
|
|
{
|
|
self(index) = fp8_to_float_raw(b_element_op(b_k_n(index)));
|
|
}
|
|
else
|
|
{
|
|
self(index) = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(index)));
|
|
}
|
|
});
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
|
|
constexpr std::size_t kGroupK = BQuantGroupSize::kK;
|
|
|
|
// ---- a scale loader for a given K-group index ----
|
|
auto load_scale_a = [&](ck_tile::index_t k_group) -> float {
|
|
const ck_tile::index_t outer_dim = m / AQuantGroupSize::kM;
|
|
const ck_tile::index_t inner_dim = k_group;
|
|
|
|
if constexpr(std::is_same_v<AQDataType, float>)
|
|
{
|
|
return a_q(outer_dim, inner_dim);
|
|
}
|
|
else if constexpr(std::is_same_v<AQDataType, ck_tile::fp8_t>)
|
|
{
|
|
return fp8_to_float_raw(a_q(outer_dim, inner_dim));
|
|
}
|
|
else // QDataType == bf8_t by static_assert above
|
|
{
|
|
return bf8_to_float_raw(a_q(outer_dim, inner_dim));
|
|
}
|
|
};
|
|
// ---- b scale loader for a given K-group index ----
|
|
auto load_scale_b = [&](ck_tile::index_t k_group) -> float {
|
|
const ck_tile::index_t outer_dim = k_group;
|
|
const ck_tile::index_t inner_dim = n / BQuantGroupSize::kN;
|
|
|
|
if constexpr(std::is_same_v<BQDataType, float>)
|
|
{
|
|
return b_q(outer_dim, inner_dim);
|
|
}
|
|
else if constexpr(std::is_same_v<BQDataType, ck_tile::fp8_t>)
|
|
{
|
|
return fp8_to_float_raw(b_q(outer_dim, inner_dim));
|
|
}
|
|
else // QDataType == bf8_t by static_assert above
|
|
{
|
|
return bf8_to_float_raw(b_q(outer_dim, inner_dim));
|
|
}
|
|
};
|
|
// ---- Loop over K by groups (full and tail) ----
|
|
for(std::size_t k_begin = 0; k_begin < K; k_begin += kGroupK)
|
|
{
|
|
const std::size_t k_end = std::min<std::size_t>(k_begin + kGroupK, K);
|
|
|
|
AccDataType v_block_acc = 0;
|
|
|
|
// unscaled accumulation within this K-group
|
|
for(std::size_t k = k_begin; k < k_end; ++k)
|
|
{
|
|
const AccDataType v_a = a_acc(m, k);
|
|
const AccDataType v_b = b_acc(k, n);
|
|
v_block_acc += v_a * v_b;
|
|
}
|
|
|
|
const ck_tile::index_t k_group = static_cast<ck_tile::index_t>(k_begin / kGroupK);
|
|
const float scale_a = load_scale_a(k_group);
|
|
const float scale_b = load_scale_b(k_group);
|
|
|
|
v_acc += v_block_acc * scale_a * scale_b;
|
|
}
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename AQDataType,
|
|
typename BDataType,
|
|
typename BQDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_gemm_rowcol_quant(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<AQDataType>& aq_m_1,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
const HostTensor<BQDataType>& bq_1_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
static_assert(std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>);
|
|
static_assert(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t>);
|
|
static_assert(std::is_same_v<AccDataType, float>);
|
|
static_assert(std::is_same_v<CDataType, float> || std::is_same_v<CDataType, ck_tile::half_t>);
|
|
static_assert(std::is_same_v<AQDataType, float> && std::is_same_v<BQDataType, float>);
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
// Init accumulator
|
|
AccDataType v_acc = 0;
|
|
// Get row scale for A and column scale for B
|
|
float a_scale = aq_m_1(m, 0);
|
|
float b_scale = bq_1_n(0, n);
|
|
|
|
// Compute the dot product
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
AccDataType v_a;
|
|
AccDataType v_b;
|
|
|
|
// Process A data
|
|
if constexpr(std::is_same_v<ADataType, pk_int4_t>)
|
|
{
|
|
const pk_int4_t pk_val = a_element_op(a_m_k(m, k));
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
|
|
if(k % 2 == 1)
|
|
v_a = fp32_val.hi;
|
|
else
|
|
v_a = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
|
|
}
|
|
|
|
// Process B data
|
|
if constexpr(std::is_same_v<BDataType, pk_int4_t>)
|
|
{
|
|
const pk_int4_t pk_val = b_element_op(b_k_n(k, n));
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t_signed_conversion(pk_val);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
|
|
}
|
|
|
|
v_acc += v_a * v_b;
|
|
}
|
|
|
|
v_acc = v_acc * a_scale * b_scale;
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename AQDataType,
|
|
typename BDataType,
|
|
typename BQDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_gemm_tensor_quant(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<AQDataType>& aq_1_1,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
const HostTensor<BQDataType>& bq_1_1,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
static_assert(std::is_same_v<ADataType, fp8_t> || std::is_same_v<ADataType, bf8_t>);
|
|
static_assert(std::is_same_v<BDataType, fp8_t> || std::is_same_v<BDataType, bf8_t>);
|
|
static_assert(std::is_same_v<AccDataType, float>);
|
|
static_assert(std::is_same_v<CDataType, float> || std::is_same_v<CDataType, ck_tile::half_t>);
|
|
static_assert(std::is_same_v<AQDataType, float> && std::is_same_v<BQDataType, float>);
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
// Init accumulator
|
|
AccDataType v_acc = 0;
|
|
// Get scale for A and scale for B
|
|
const AccDataType a_scale = ck_tile::type_convert<AccDataType>(aq_1_1(0, 0));
|
|
const AccDataType b_scale = ck_tile::type_convert<AccDataType>(bq_1_1(0, 0));
|
|
|
|
// Compute the dot product
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
AccDataType v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
|
|
AccDataType v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
|
|
|
|
v_acc += v_a * v_b;
|
|
}
|
|
|
|
v_acc = v_acc * a_scale * b_scale;
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename QDataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename QuantGroupSize,
|
|
typename BLayout,
|
|
bool aquant,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_mx_gemm_bquant(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<QDataType>& q,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
using ComputeType = float;
|
|
ComputeType v_a;
|
|
ComputeType v_b;
|
|
|
|
auto load_b = [&](std::size_t k) -> AccDataType {
|
|
if constexpr(std::is_same_v<BDataType, pk_fp4_t>)
|
|
{
|
|
const auto b_pack = type_convert<pk_fp4_t>(b_element_op(b_k_n(k, n)));
|
|
if constexpr(std::is_same_v<BLayout, ck_tile::tensor_layout::gemm::RowMajor>)
|
|
{
|
|
return (n & 1) ? type_convert<ComputeType>(b_pack.unpack(number<1>{}))
|
|
: type_convert<ComputeType>(b_pack.unpack(number<0>{}));
|
|
}
|
|
else
|
|
{
|
|
return (k & 1) ? type_convert<ComputeType>(b_pack.unpack(number<1>{}))
|
|
: type_convert<ComputeType>(b_pack.unpack(number<0>{}));
|
|
}
|
|
}
|
|
else
|
|
{
|
|
return ck_tile::type_convert<ComputeType>(b_element_op(b_k_n(k, n)));
|
|
}
|
|
};
|
|
|
|
for(std::size_t k = 0; k < K; k++)
|
|
{
|
|
const auto b_scale = type_convert<float>(q(k / QuantGroupSize::kK, n));
|
|
v_a = ck_tile::type_convert<ComputeType>(a_element_op(a_m_k(m, k)));
|
|
v_b = load_b(k) * b_scale;
|
|
v_acc += v_a * v_b;
|
|
}
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
std::cout << std::endl;
|
|
}
|
|
|
|
template <typename ADataType_,
|
|
typename BDataType_,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void
|
|
reference_gemm(const HostTensor<if_select_t<ADataType_, tf32_t, float, ADataType_>>& a_m_k,
|
|
const HostTensor<if_select_t<BDataType_, tf32_t, float, BDataType_>>& b_k_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
if constexpr(std::is_same_v<ADataType_, tf32_t> || std::is_same_v<BDataType_, tf32_t>)
|
|
static_assert(std::is_same_v<ADataType_, BDataType_>,
|
|
"ADataType and BDataType must be the same");
|
|
using ADataTypeCompute = ADataType_;
|
|
using ADataTypeBuf = if_select_t<ADataType_, tf32_t, float, ADataType_>;
|
|
using BDataTypeBuf = if_select_t<BDataType_, tf32_t, float, BDataType_>;
|
|
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
const bool is_gfx950 = (ck_tile::get_device_name() == "gfx950");
|
|
|
|
auto f_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
AccDataType v_a;
|
|
AccDataType v_b;
|
|
if constexpr(std::is_same_v<ADataTypeBuf, pk_fp4_t>)
|
|
{
|
|
// HostTensor automatically handles packed indexing: a_m_k(m,k) divides offset by
|
|
// PackedSize So a_m_k(m,0) and a_m_k(m,1) return the same packed byte
|
|
const pk_fp4_t pk_val = a_m_k(m, k);
|
|
const fp32x2_t fp32_val = pk_val.to_fp32x2(1.0f);
|
|
const float unpacked = (k % 2 == 1) ? fp32_val.hi : fp32_val.lo;
|
|
v_a = ck_tile::type_convert<AccDataType>(a_element_op(unpacked));
|
|
}
|
|
else if constexpr(std::is_same_v<ADataTypeBuf, pk_int4_t>)
|
|
{
|
|
// HostTensor automatically handles packed indexing
|
|
const pk_int4_t pk_val = a_m_k(m, k);
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
|
|
const float unpacked = (k % 2 == 1) ? fp32_val.hi : fp32_val.lo;
|
|
v_a = ck_tile::type_convert<AccDataType>(a_element_op(unpacked));
|
|
}
|
|
else
|
|
{
|
|
v_a = ck_tile::type_convert<AccDataType>(a_element_op(a_m_k(m, k)));
|
|
}
|
|
if constexpr(std::is_same_v<BDataTypeBuf, pk_fp4_t>)
|
|
{
|
|
// HostTensor automatically handles packed indexing
|
|
const pk_fp4_t pk_val = b_k_n(k, n);
|
|
const fp32x2_t fp32_val = pk_val.to_fp32x2(1.0f);
|
|
const float unpacked = (k % 2 == 1) ? fp32_val.hi : fp32_val.lo;
|
|
v_b = ck_tile::type_convert<AccDataType>(b_element_op(unpacked));
|
|
}
|
|
else if constexpr(std::is_same_v<BDataTypeBuf, pk_int4_t>)
|
|
{
|
|
// HostTensor automatically handles packed indexing
|
|
const pk_int4_t pk_val = b_k_n(k, n);
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(pk_val);
|
|
const float unpacked = (k % 2 == 1) ? fp32_val.hi : fp32_val.lo;
|
|
v_b = ck_tile::type_convert<AccDataType>(b_element_op(unpacked));
|
|
}
|
|
else
|
|
{
|
|
v_b = ck_tile::type_convert<AccDataType>(b_element_op(b_k_n(k, n)));
|
|
}
|
|
|
|
if constexpr(std::is_same_v<ADataTypeCompute, tf32_t>)
|
|
{
|
|
if(is_gfx950)
|
|
{
|
|
// gfx950: use 3x bf16 emulation
|
|
bf16_t v_a_bf16_big = ck_tile::type_convert<bf16_t>(v_a);
|
|
bf16_t v_a_bf16_small = ck_tile::type_convert<bf16_t>(
|
|
v_a - type_convert<AccDataType>(v_a_bf16_big));
|
|
bf16_t v_b_bf16_big = ck_tile::type_convert<bf16_t>(v_b);
|
|
bf16_t v_b_bf16_small = ck_tile::type_convert<bf16_t>(
|
|
v_b - type_convert<AccDataType>(v_b_bf16_big));
|
|
|
|
v_acc += ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_small) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_small) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big);
|
|
}
|
|
else
|
|
{
|
|
// Other architectures: tf32 not supported or handled via fp32 fallback
|
|
v_acc += v_a * v_b;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
v_acc += v_a * v_b;
|
|
}
|
|
}
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(acc_element_op(v_acc));
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename AsDataType,
|
|
typename BsDataType,
|
|
typename DsDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AElementOp,
|
|
typename BElementOp,
|
|
typename CDElementOp,
|
|
typename ADataType = remove_cvref_t<std::tuple_element_t<0, AsDataType>>,
|
|
typename BDataType = remove_cvref_t<std::tuple_element_t<0, BsDataType>>,
|
|
typename DDataType = remove_cvref_t<std::tuple_element_t<0, DsDataType>>>
|
|
CK_TILE_HOST void
|
|
reference_gemm_multiple_abd(const std::array<HostTensor<ADataType>, AsDataType::size()>& as_m_k,
|
|
const std::array<HostTensor<BDataType>, BsDataType::size()>& bs_k_n,
|
|
const std::array<HostTensor<DDataType>, DsDataType::size()>& ds_m_n,
|
|
HostTensor<ADataType>& a_m_k,
|
|
HostTensor<BDataType>& b_k_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const AElementOp& a_element_op = {},
|
|
const BElementOp& b_element_op = {},
|
|
const CDElementOp& acc_element_op = {})
|
|
{
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto as_m_k_tuple =
|
|
generate_tie([&](auto idx) -> auto& { return as_m_k[idx]; }, number<AsDataType::size()>{});
|
|
|
|
auto bs_k_n_tuple =
|
|
generate_tie([&](auto idx) -> auto& { return bs_k_n[idx]; }, number<BsDataType::size()>{});
|
|
|
|
auto ds_m_n_tuple =
|
|
generate_tie([&](auto idx) -> auto& { return ds_m_n[idx]; }, number<DsDataType::size()>{});
|
|
|
|
// Apply elementwise function to A
|
|
auto a_elementwise_fn = [&](auto i, auto j) {
|
|
ck_tile::apply([&](auto&&... t) { a_element_op(a_m_k(i, j), t(i, j)...); }, as_m_k_tuple);
|
|
};
|
|
|
|
make_ParallelTensorFunctor(a_elementwise_fn, M, K)(std::thread::hardware_concurrency());
|
|
|
|
// Apply elementwise function to B
|
|
auto b_elementwise_fn = [&](auto i, auto j) {
|
|
ck_tile::apply([&](auto&&... t) { b_element_op(b_k_n(i, j), t(i, j)...); }, bs_k_n_tuple);
|
|
};
|
|
|
|
make_ParallelTensorFunctor(b_elementwise_fn, K, N)(std::thread::hardware_concurrency());
|
|
|
|
auto f_mk_kn_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
ADataType v_a = a_m_k(m, k);
|
|
BDataType v_b = b_k_n(k, n);
|
|
v_acc +=
|
|
ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
|
|
}
|
|
|
|
CDataType v_c = 0;
|
|
|
|
ck_tile::apply(
|
|
[&](auto&&... t) {
|
|
acc_element_op(v_c,
|
|
ck_tile::type_convert<float>(v_acc),
|
|
ck_tile::type_convert<float>(t(m, n))...);
|
|
},
|
|
ds_m_n_tuple);
|
|
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_c);
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mk_kn_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename ScaleDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename AElementOp = ck_tile::identity,
|
|
typename BElementOp = ck_tile::identity,
|
|
typename ACCElementOp = ck_tile::identity>
|
|
CK_TILE_HOST void reference_mx_gemm(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const HostTensor<ScaleDataType>& scale_a,
|
|
const HostTensor<ScaleDataType>& scale_b,
|
|
const AElementOp& = {},
|
|
const BElementOp& = {},
|
|
const ACCElementOp& = {})
|
|
{
|
|
static_assert(std::is_same_v<AElementOp, ck_tile::identity>);
|
|
static_assert(std::is_same_v<BElementOp, ck_tile::identity>);
|
|
static_assert(std::is_same_v<ACCElementOp, ck_tile::identity>);
|
|
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
const std::size_t ScaleBlockSize = K / scale_a.get_length(1);
|
|
|
|
HostTensor<AccDataType> a_m_k_scaled({std::size_t(M), std::size_t(K)},
|
|
{std::size_t(K), std::size_t(1)});
|
|
HostTensor<AccDataType> b_k_n_scaled({std::size_t(K), std::size_t(N)},
|
|
{std::size_t(1), std::size_t(K)});
|
|
|
|
for(std::size_t m = 0; m < M; ++m)
|
|
{
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
if constexpr(std::is_same_v<ADataType, pk_fp4_t>)
|
|
{
|
|
if(k % 2 == 1)
|
|
continue; // skip odd k
|
|
|
|
auto a_f4x2 = a_m_k(m, k);
|
|
auto a_scale = ck_tile::type_convert<AccDataType>(scale_a(m, k / ScaleBlockSize));
|
|
auto a_f4_lo =
|
|
ck_tile::type_convert<AccDataType>(a_f4x2.template unpack<>(number<0>{}));
|
|
auto a_f4_hi =
|
|
ck_tile::type_convert<AccDataType>(a_f4x2.template unpack<>(number<1>{}));
|
|
|
|
a_m_k_scaled(m, k) = a_f4_lo * a_scale;
|
|
a_m_k_scaled(m, k + 1) = a_f4_hi * a_scale;
|
|
}
|
|
else if constexpr(std::is_same_v<ADataType, pk_fp6x16_t>)
|
|
{
|
|
if(k % pk_fp6x16_t::packed_size != 0)
|
|
continue;
|
|
auto a_scale = ck_tile::type_convert<AccDataType>(scale_a(m, k / ScaleBlockSize));
|
|
for(std::size_t k_ = 0; k_ < pk_fp6x16_t::packed_size; k_++)
|
|
{
|
|
a_m_k_scaled(m, k + k_) =
|
|
pk_fp6x16_t::fp6_e2m3_to_float(a_m_k(m, k).unpack(k_)) * a_scale;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
a_m_k_scaled(m, k) =
|
|
ck_tile::type_convert<AccDataType>((a_m_k(m, k))) *
|
|
ck_tile::type_convert<AccDataType>(scale_a(m, k / ScaleBlockSize));
|
|
}
|
|
}
|
|
}
|
|
|
|
for(std::size_t n = 0; n < N; n++)
|
|
{
|
|
for(std::size_t k = 0; k < K; k++)
|
|
{
|
|
if constexpr(std::is_same_v<BDataType, pk_fp4_t>)
|
|
{
|
|
if(k % 2 == 1)
|
|
continue; // skip odd k
|
|
|
|
auto b_f4x2 = b_k_n(k, n);
|
|
auto b_scale = ck_tile::type_convert<AccDataType>(scale_b(k / ScaleBlockSize, n));
|
|
auto b_f4_lo =
|
|
ck_tile::type_convert<AccDataType>(b_f4x2.template unpack<>(number<0>{}));
|
|
auto b_f4_hi =
|
|
ck_tile::type_convert<AccDataType>(b_f4x2.template unpack<>(number<1>{}));
|
|
|
|
b_k_n_scaled(k, n) = b_f4_lo * b_scale;
|
|
b_k_n_scaled(k + 1, n) = b_f4_hi * b_scale;
|
|
}
|
|
else if constexpr(std::is_same_v<BDataType, pk_fp6x16_t>)
|
|
{
|
|
if(k % pk_fp6x16_t::packed_size != 0)
|
|
continue;
|
|
auto b_scale = ck_tile::type_convert<AccDataType>(scale_b(k / ScaleBlockSize, n));
|
|
for(std::size_t k_ = 0; k_ < pk_fp6x16_t::packed_size; k_++)
|
|
{
|
|
b_k_n_scaled(k + k_, n) =
|
|
pk_fp6x16_t::fp6_e2m3_to_float(b_k_n(k, n).unpack(k_)) * b_scale;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
b_k_n_scaled(k, n) =
|
|
ck_tile::type_convert<AccDataType>((b_k_n(k, n))) *
|
|
ck_tile::type_convert<AccDataType>(scale_b(k / ScaleBlockSize, n));
|
|
}
|
|
}
|
|
}
|
|
|
|
// call reference gemm
|
|
reference_gemm<AccDataType, AccDataType, AccDataType, CDataType>(
|
|
a_m_k_scaled, b_k_n_scaled, c_m_n);
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename DsDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename ACCElementOp,
|
|
typename DDataType = remove_cvref_t<std::tuple_element_t<0, DsDataType>>>
|
|
CK_TILE_HOST void
|
|
reference_gemm_multiple_d(const HostTensor<ADataType>& a_m_k,
|
|
const HostTensor<BDataType>& b_k_n,
|
|
const std::array<HostTensor<DDataType>, DsDataType::size()>& ds_m_n,
|
|
HostTensor<CDataType>& c_m_n,
|
|
const ACCElementOp& acc_element_op = {})
|
|
{
|
|
const std::size_t M = a_m_k.get_length(0);
|
|
const std::size_t N = b_k_n.get_length(1);
|
|
const std::size_t K = a_m_k.get_length(1);
|
|
|
|
auto f_mk_kn_mn = [&](auto m, auto n) {
|
|
AccDataType v_acc = 0;
|
|
for(std::size_t k = 0; k < K; ++k)
|
|
{
|
|
ADataType v_a = a_m_k(m, k);
|
|
BDataType v_b = b_k_n(k, n);
|
|
v_acc +=
|
|
ck_tile::type_convert<AccDataType>(v_a) * ck_tile::type_convert<AccDataType>(v_b);
|
|
}
|
|
|
|
CDataType v_c = 0;
|
|
if constexpr(DsDataType::size() == 0)
|
|
{
|
|
acc_element_op(v_c, ck_tile::type_convert<float>(v_acc));
|
|
}
|
|
else if constexpr(DsDataType::size() == 1)
|
|
{
|
|
acc_element_op(v_c,
|
|
ck_tile::type_convert<float>(v_acc),
|
|
ck_tile::type_convert<float>(ds_m_n[0](m, n)));
|
|
}
|
|
else if constexpr(DsDataType::size() == 2)
|
|
{
|
|
acc_element_op(v_c,
|
|
ck_tile::type_convert<float>(v_acc),
|
|
ck_tile::type_convert<float>(ds_m_n[0](m, n)),
|
|
ck_tile::type_convert<float>(ds_m_n[1](m, n)));
|
|
}
|
|
c_m_n(m, n) = ck_tile::type_convert<CDataType>(v_c);
|
|
};
|
|
|
|
make_ParallelTensorFunctor(f_mk_kn_mn, M, N)(std::thread::hardware_concurrency());
|
|
}
|
|
|
|
template <typename ADataType_,
|
|
typename BDataType_,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
__global__ void naive_gemm_kernel(if_select_t<ADataType_, tf32_t, float, ADataType_>* A,
|
|
if_select_t<BDataType_, tf32_t, float, BDataType_>* B,
|
|
CDataType* C,
|
|
ck_tile::index_t M,
|
|
ck_tile::index_t N,
|
|
ck_tile::index_t K,
|
|
ck_tile::index_t strideA,
|
|
ck_tile::index_t strideB,
|
|
ck_tile::index_t strideC)
|
|
{
|
|
if constexpr(std::is_same_v<ADataType_, tf32_t> || std::is_same_v<BDataType_, tf32_t>)
|
|
static_assert(std::is_same_v<ADataType_, BDataType_>,
|
|
"ADataType and BDataType must be the same");
|
|
using ADataTypeCompute = ADataType_;
|
|
// ADataTypeBuf: buffer/storage type (fp32 when tf32)
|
|
using ADataTypeBuf = if_select_t<ADataType_, tf32_t, float, ADataType_>;
|
|
using BDataTypeBuf = if_select_t<BDataType_, tf32_t, float, BDataType_>;
|
|
|
|
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
|
int row = idx / N; // Compute row index
|
|
int col = idx % N; // Compute column index
|
|
|
|
if(row < M && col < N)
|
|
{
|
|
AccDataType acc = 0.0;
|
|
for(int k = 0; k < K; ++k)
|
|
{
|
|
constexpr index_t packed_size_a = ck_tile::numeric_traits<ADataTypeBuf>::PackedSize;
|
|
constexpr index_t packed_size_b = ck_tile::numeric_traits<BDataTypeBuf>::PackedSize;
|
|
// Adjust indexing based on matrix layout
|
|
int a_index = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
|
|
? row * strideA + k
|
|
: k * strideA + row;
|
|
int b_index = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
|
|
? col * strideB + k
|
|
: k * strideB + col;
|
|
|
|
AccDataType v_a;
|
|
AccDataType v_b;
|
|
if constexpr(std::is_same_v<ADataTypeBuf, pk_int4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(A[a_index / packed_size_a]);
|
|
if(k % 2 == 1)
|
|
v_a = fp32_val.hi;
|
|
else
|
|
v_a = fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<ADataTypeBuf, pk_fp4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_fp4_to_fp32x2(A[a_index / packed_size_a], 1.0f);
|
|
if(k % 2 == 1)
|
|
v_a = fp32_val.hi;
|
|
else
|
|
v_a = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_a = ck_tile::type_convert<AccDataType>(A[a_index]);
|
|
}
|
|
if constexpr(std::is_same_v<BDataTypeBuf, pk_int4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(B[b_index / packed_size_b]);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<BDataTypeBuf, pk_fp4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_fp4_to_fp32x2(B[b_index / packed_size_b], 1.0f);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_b = ck_tile::type_convert<AccDataType>(B[b_index]);
|
|
}
|
|
|
|
if constexpr(std::is_same_v<ADataTypeCompute, tf32_t>)
|
|
{
|
|
#ifdef CK_GFX950_SUPPORT
|
|
// gfx950: use 3x bf16 emulation
|
|
bf16_t v_a_bf16_big = ck_tile::type_convert<bf16_t>(v_a);
|
|
bf16_t v_a_bf16_small =
|
|
ck_tile::type_convert<bf16_t>(v_a - type_convert<AccDataType>(v_a_bf16_big));
|
|
bf16_t v_b_bf16_big = ck_tile::type_convert<bf16_t>(v_b);
|
|
bf16_t v_b_bf16_small =
|
|
ck_tile::type_convert<bf16_t>(v_b - type_convert<AccDataType>(v_b_bf16_big));
|
|
|
|
acc += ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_small) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_small) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big);
|
|
#else
|
|
// Other architectures: use fp32 fallback
|
|
acc += v_a * v_b;
|
|
#endif
|
|
}
|
|
else
|
|
{
|
|
acc += v_a * v_b;
|
|
}
|
|
}
|
|
|
|
int c_index = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
|
|
? row * strideC + col
|
|
: col * strideC + row;
|
|
C[c_index] = ck_tile::type_convert<CDataType>(acc);
|
|
}
|
|
}
|
|
|
|
template <typename ADataType_,
|
|
typename BDataType_,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
__global__ void blockwise_gemm_kernel(if_select_t<ADataType_, tf32_t, float, ADataType_>* A,
|
|
if_select_t<BDataType_, tf32_t, float, BDataType_>* B,
|
|
CDataType* C,
|
|
ck_tile::index_t M,
|
|
ck_tile::index_t N,
|
|
ck_tile::index_t K,
|
|
ck_tile::index_t strideA,
|
|
ck_tile::index_t strideB,
|
|
ck_tile::index_t strideC,
|
|
ck_tile::index_t scale_granularity_m,
|
|
ck_tile::index_t scale_granularity_n,
|
|
ck_tile::index_t scale_granularity_k,
|
|
float* scale_A_ptr,
|
|
float* scale_B_ptr)
|
|
{
|
|
if constexpr(std::is_same_v<ADataType_, tf32_t> || std::is_same_v<BDataType_, tf32_t>)
|
|
static_assert(std::is_same_v<ADataType_, BDataType_>,
|
|
"ADataType and BDataType must be the same");
|
|
using ADataTypeCompute = ADataType_;
|
|
// ADataTypeBuf: buffer/storage type (fp32 when tf32)
|
|
using ADataTypeBuf = if_select_t<ADataType_, tf32_t, float, ADataType_>;
|
|
using BDataTypeBuf = if_select_t<BDataType_, tf32_t, float, BDataType_>;
|
|
|
|
int idx = blockIdx.x * blockDim.x + threadIdx.x;
|
|
int row = idx / N; // Compute row index
|
|
int col = idx % N; // Compute column index
|
|
|
|
if(row < M && col < N)
|
|
{
|
|
AccDataType acc = 0.0, acc_temp = 0.0;
|
|
|
|
index_t scale_A_stride = (M + scale_granularity_m - 1) / scale_granularity_m;
|
|
index_t scale_B_stride = (N + scale_granularity_n - 1) / scale_granularity_n;
|
|
|
|
float scale_A = 0;
|
|
float scale_B = 0;
|
|
|
|
for(int k = 0; k < K; ++k)
|
|
{
|
|
if(k % scale_granularity_k == 0)
|
|
{
|
|
// update acc
|
|
acc += acc_temp * scale_A * scale_B;
|
|
acc_temp = 0.0;
|
|
// update scale factors
|
|
scale_A = scale_A_ptr[(row / scale_granularity_m) +
|
|
(k / scale_granularity_k) * scale_A_stride];
|
|
scale_B = scale_B_ptr[(col / scale_granularity_n) +
|
|
(k / scale_granularity_k) * scale_B_stride];
|
|
}
|
|
|
|
constexpr index_t packed_size_a = ck_tile::numeric_traits<ADataTypeBuf>::PackedSize;
|
|
constexpr index_t packed_size_b = ck_tile::numeric_traits<BDataTypeBuf>::PackedSize;
|
|
// Adjust indexing based on matrix layout
|
|
int a_index = (std::is_same_v<LayoutA, tensor_layout::gemm::RowMajor>)
|
|
? row * strideA + k
|
|
: k * strideA + row;
|
|
int b_index = (std::is_same_v<LayoutB, tensor_layout::gemm::ColumnMajor>)
|
|
? col * strideB + k
|
|
: k * strideB + col;
|
|
|
|
AccDataType v_a;
|
|
AccDataType v_b;
|
|
if constexpr(std::is_same_v<ADataTypeBuf, pk_int4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(A[a_index / packed_size_a]);
|
|
if(k % 2 == 1)
|
|
v_a = fp32_val.hi;
|
|
else
|
|
v_a = fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<ADataTypeBuf, pk_fp4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_fp4_to_fp32x2(A[a_index / packed_size_a], 1.0f);
|
|
if(k % 2 == 1)
|
|
v_a = fp32_val.hi;
|
|
else
|
|
v_a = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_a = ck_tile::type_convert<AccDataType>(A[a_index]);
|
|
}
|
|
|
|
if constexpr(std::is_same_v<BDataTypeBuf, pk_int4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_int4_t_to_fp32x2_t(B[b_index / packed_size_b]);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else if constexpr(std::is_same_v<BDataTypeBuf, pk_fp4_t>)
|
|
{
|
|
const fp32x2_t fp32_val = pk_fp4_to_fp32x2(B[b_index / packed_size_b], 1.0f);
|
|
if(k % 2 == 1)
|
|
v_b = fp32_val.hi;
|
|
else
|
|
v_b = fp32_val.lo;
|
|
}
|
|
else
|
|
{
|
|
v_b = ck_tile::type_convert<AccDataType>(B[b_index]);
|
|
}
|
|
|
|
if constexpr(std::is_same_v<ADataTypeCompute, tf32_t>)
|
|
{
|
|
#ifdef CK_GFX950_SUPPORT
|
|
// gfx950: use 3x bf16 emulation
|
|
bf16_t v_a_bf16_big = ck_tile::type_convert<bf16_t>(v_a);
|
|
bf16_t v_a_bf16_small =
|
|
ck_tile::type_convert<bf16_t>(v_a - type_convert<AccDataType>(v_a_bf16_big));
|
|
bf16_t v_b_bf16_big = ck_tile::type_convert<bf16_t>(v_b);
|
|
bf16_t v_b_bf16_small =
|
|
ck_tile::type_convert<bf16_t>(v_b - type_convert<AccDataType>(v_b_bf16_big));
|
|
|
|
acc_temp += ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_small) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_small) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big) +
|
|
ck_tile::type_convert<AccDataType>(v_a_bf16_big) *
|
|
ck_tile::type_convert<AccDataType>(v_b_bf16_big);
|
|
#else
|
|
// Other architectures: use fp32 fallback
|
|
acc_temp += v_a * v_b;
|
|
#endif
|
|
}
|
|
else
|
|
{
|
|
acc_temp += v_a * v_b;
|
|
}
|
|
}
|
|
// final accumulation
|
|
acc += acc_temp * scale_A * scale_B;
|
|
|
|
int c_index = (std::is_same_v<LayoutC, tensor_layout::gemm::RowMajor>)
|
|
? row * strideC + col
|
|
: col * strideC + row;
|
|
C[c_index] = ck_tile::type_convert<CDataType>(acc);
|
|
}
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
void reference_gemm_gpu(if_select_t<ADataType, tf32_t, float, ADataType>* a_ptr,
|
|
if_select_t<BDataType, tf32_t, float, BDataType>* b_ptr,
|
|
CDataType* c_ptr,
|
|
index_t M,
|
|
index_t N,
|
|
index_t K,
|
|
index_t stride_a,
|
|
index_t stride_b,
|
|
index_t stride_c)
|
|
{
|
|
int totalElements = M * N;
|
|
int numThreadsPerBlock = 256; // Common choice for threads per block
|
|
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
|
|
|
|
naive_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
|
|
<<<numBlocks, numThreadsPerBlock>>>(
|
|
a_ptr, b_ptr, c_ptr, M, N, K, stride_a, stride_b, stride_c);
|
|
|
|
return;
|
|
}
|
|
|
|
template <typename ADataType,
|
|
typename BDataType,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
void reference_blockwise_gemm_gpu(if_select_t<ADataType, tf32_t, float, ADataType>* a_ptr,
|
|
if_select_t<BDataType, tf32_t, float, BDataType>* b_ptr,
|
|
CDataType* c_ptr,
|
|
index_t M,
|
|
index_t N,
|
|
index_t K,
|
|
index_t stride_a,
|
|
index_t stride_b,
|
|
index_t stride_c,
|
|
index_t scale_granularity_m,
|
|
index_t scale_granularity_n,
|
|
index_t scale_granularity_k,
|
|
float* scale_A_ptr,
|
|
float* scale_B_ptr)
|
|
{
|
|
int totalElements = M * N;
|
|
int numThreadsPerBlock = 256; // Common choice for threads per block
|
|
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
|
|
|
|
blockwise_gemm_kernel<ADataType, BDataType, AccDataType, CDataType, LayoutA, LayoutB, LayoutC>
|
|
<<<numBlocks, numThreadsPerBlock>>>(a_ptr,
|
|
b_ptr,
|
|
c_ptr,
|
|
M,
|
|
N,
|
|
K,
|
|
stride_a,
|
|
stride_b,
|
|
stride_c,
|
|
scale_granularity_m,
|
|
scale_granularity_n,
|
|
scale_granularity_k,
|
|
scale_A_ptr,
|
|
scale_B_ptr);
|
|
|
|
return;
|
|
}
|
|
|
|
template <typename ADataType_,
|
|
typename BDataType_,
|
|
typename AccDataType,
|
|
typename CDataType,
|
|
typename LayoutA,
|
|
typename LayoutB,
|
|
typename LayoutC>
|
|
void reference_batched_gemm_gpu(if_select_t<ADataType_, tf32_t, float, ADataType_>* a_ptr,
|
|
if_select_t<BDataType_, tf32_t, float, BDataType_>* b_ptr,
|
|
CDataType* c_ptr,
|
|
index_t M,
|
|
index_t N,
|
|
index_t K,
|
|
index_t stride_a,
|
|
index_t stride_b,
|
|
index_t stride_c,
|
|
index_t batch_stride_A,
|
|
index_t batch_stride_B,
|
|
index_t batch_stride_C,
|
|
index_t batch_count)
|
|
{
|
|
using ADataTypeBuf = if_select_t<ADataType_, tf32_t, float, ADataType_>;
|
|
using BDataTypeBuf = if_select_t<BDataType_, tf32_t, float, BDataType_>;
|
|
|
|
using ADataTypeCompute = ADataType_;
|
|
using BDataTypeCompute = BDataType_;
|
|
|
|
int totalElements = M * N;
|
|
int numThreadsPerBlock = 256; // Common choice for threads per block
|
|
int numBlocks = (totalElements + numThreadsPerBlock - 1) / numThreadsPerBlock;
|
|
|
|
for(index_t batch_id = 0; batch_id < batch_count; ++batch_id)
|
|
{
|
|
ADataTypeBuf* d_ATemp = a_ptr + batch_id * batch_stride_A;
|
|
BDataTypeBuf* d_BTemp = b_ptr + batch_id * batch_stride_B;
|
|
CDataType* d_CTemp = c_ptr + batch_id * batch_stride_C;
|
|
naive_gemm_kernel<ADataTypeCompute,
|
|
BDataTypeCompute,
|
|
AccDataType,
|
|
CDataType,
|
|
LayoutA,
|
|
LayoutB,
|
|
LayoutC><<<numBlocks, numThreadsPerBlock>>>(
|
|
d_ATemp, d_BTemp, d_CTemp, M, N, K, stride_a, stride_b, stride_c);
|
|
}
|
|
|
|
return;
|
|
}
|
|
|
|
} // namespace ck_tile
|