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Commit 019d4b7c authored by illsilin's avatar illsilin
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merge from public repo

parents 07307ea1 5063a39f
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include/ck_tile/ops/smoothquant/pipeline/smoothquant_pipeline_two_pass.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -16,11 +16,11 @@ struct SmoothquantPipelineTwoPass ...@@ -16,11 +16,11 @@ struct SmoothquantPipelineTwoPass
using Problem = ck_tile::remove_cvref_t<Problem_>; using Problem = ck_tile::remove_cvref_t<Problem_>;
using Policy = ck_tile::remove_cvref_t<Policy_>; using Policy = ck_tile::remove_cvref_t<Policy_>;
using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>; using XDataType = ck_tile::remove_cvref_t<typename Problem::XDataType>;
using XScaleDataType = ck_tile::remove_cvref_t<typename Problem::XScaleDataType>; using SmoothScaleDataType = ck_tile::remove_cvref_t<typename Problem::SmoothScaleDataType>;
using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>; using ComputeDataType = ck_tile::remove_cvref_t<typename Problem::ComputeDataType>;
using QYDataType = ck_tile::remove_cvref_t<typename Problem::QYDataType>; using QYDataType = ck_tile::remove_cvref_t<typename Problem::QYDataType>;
using YScaleDataType = ck_tile::remove_cvref_t<typename Problem::YScaleDataType>; using YScaleDataType = ck_tile::remove_cvref_t<typename Problem::YScaleDataType>;
static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync; static constexpr bool kNeedCrossWarpSync = Problem::kNeedCrossWarpSync;
static constexpr bool kPadM = false; // TODO - BlockSmoothquantProblem::kPadM static constexpr bool kPadM = false; // TODO - BlockSmoothquantProblem::kPadM
...@@ -39,9 +39,12 @@ struct SmoothquantPipelineTwoPass ...@@ -39,9 +39,12 @@ struct SmoothquantPipelineTwoPass
return Policy::template GetSmemSize<Problem>(); return Policy::template GetSmemSize<Problem>();
} }
template <typename XWindow, typename XScaleWindow, typename QYWindow, typename YScaleWindow> template <typename XWindow,
typename SmoothScaleWindow,
typename QYWindow,
typename YScaleWindow>
CK_TILE_DEVICE auto operator()(const XWindow& x_window_, CK_TILE_DEVICE auto operator()(const XWindow& x_window_,
const XScaleWindow& xscale_window_, const SmoothScaleWindow& smscale_window_,
YScaleWindow& yscale_window, YScaleWindow& yscale_window,
QYWindow& qy_window, QYWindow& qy_window,
ck_tile::index_t row_size, ck_tile::index_t row_size,
...@@ -49,8 +52,8 @@ struct SmoothquantPipelineTwoPass ...@@ -49,8 +52,8 @@ struct SmoothquantPipelineTwoPass
{ {
auto x_window = auto x_window =
make_tile_window(x_window_, Policy::template MakeXBlockTileDistribution<Problem>()); make_tile_window(x_window_, Policy::template MakeXBlockTileDistribution<Problem>());
auto xscale_window = make_tile_window( auto smscale_window = make_tile_window(
xscale_window_, Policy::template MakeXScaleBlockTileDistribution<Problem>()); smscale_window_, Policy::template MakeSmoothScaleBlockTileDistribution<Problem>());
static constexpr index_t Block_N = Problem::BlockShape::Block_N; static constexpr index_t Block_N = Problem::BlockShape::Block_N;
index_t num_n_tile_iteration = index_t num_n_tile_iteration =
...@@ -76,14 +79,14 @@ struct SmoothquantPipelineTwoPass ...@@ -76,14 +79,14 @@ struct SmoothquantPipelineTwoPass
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN) for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{ {
const auto x = load_tile(x_window); const auto x = load_tile(x_window);
const auto xscale = load_tile(xscale_window); const auto smscale = load_tile(smscale_window);
const auto y = tile_elementwise_in( const auto y = tile_elementwise_in(
[&](const auto& a, const auto& b) { [&](const auto& a, const auto& b) {
return type_convert<ComputeDataType>(a) * type_convert<ComputeDataType>(b); return type_convert<ComputeDataType>(a) * type_convert<ComputeDataType>(b);
}, },
x, x,
xscale); smscale);
constexpr auto x_size_per_row = constexpr auto x_size_per_row =
x.get_tile_distribution().get_ys_to_d_descriptor().get_lengths().at(number<1>{}); x.get_tile_distribution().get_ys_to_d_descriptor().get_lengths().at(number<1>{});
...@@ -94,7 +97,7 @@ struct SmoothquantPipelineTwoPass ...@@ -94,7 +97,7 @@ struct SmoothquantPipelineTwoPass
block_reduce2d(y, absmax, reduce_absmax_func); block_reduce2d(y, absmax, reduce_absmax_func);
move_tile_window(x_window, {0, Block_N}); move_tile_window(x_window, {0, Block_N});
move_tile_window(xscale_window, {Block_N}); move_tile_window(smscale_window, {Block_N});
} }
// compute absmax, cross-lane->cross-warp // compute absmax, cross-lane->cross-warp
...@@ -114,20 +117,20 @@ struct SmoothquantPipelineTwoPass ...@@ -114,20 +117,20 @@ struct SmoothquantPipelineTwoPass
row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N; row_size % Block_N == 0 ? row_size - Block_N : row_size - row_size % Block_N;
move_tile_window(x_window, {0, -Block_N}); move_tile_window(x_window, {0, -Block_N});
move_tile_window(xscale_window, {-Block_N}); move_tile_window(smscale_window, {-Block_N});
move_tile_window(qy_window, {0, stride_to_right_most_window}); move_tile_window(qy_window, {0, stride_to_right_most_window});
// recompute y and quantize y to qy // recompute y and quantize y to qy
for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN) for(int iN = __builtin_amdgcn_readfirstlane(0); iN < num_n_tile_iteration; ++iN)
{ {
const auto x = load_tile(x_window); const auto x = load_tile(x_window);
const auto xscale = load_tile(xscale_window); const auto smscale = load_tile(smscale_window);
const auto y = tile_elementwise_in( const auto y = tile_elementwise_in(
[&](const auto& a, const auto& b) { [&](const auto& a, const auto& b) {
return type_convert<ComputeDataType>(a) * type_convert<ComputeDataType>(b); return type_convert<ComputeDataType>(a) * type_convert<ComputeDataType>(b);
}, },
x, x,
xscale); smscale);
auto qy = make_static_distributed_tensor<QYDataType>(y.get_tile_distribution()); auto qy = make_static_distributed_tensor<QYDataType>(y.get_tile_distribution());
sweep_tile(qy, [&](auto idx) { sweep_tile(qy, [&](auto idx) {
...@@ -138,7 +141,7 @@ struct SmoothquantPipelineTwoPass ...@@ -138,7 +141,7 @@ struct SmoothquantPipelineTwoPass
store_tile(qy_window, qy); store_tile(qy_window, qy);
move_tile_window(x_window, {0, -Block_N}); move_tile_window(x_window, {0, -Block_N});
move_tile_window(xscale_window, {0, -Block_N}); move_tile_window(smscale_window, {0, -Block_N});
move_tile_window(qy_window, {0, -Block_N}); move_tile_window(qy_window, {0, -Block_N});
} }
} }
......
include/ck_tile/ops/softmax.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
include/ck_tile/ops/topk.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
include/ck_tile/ops/topk_softmax.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
......
include/ck_tile/ref/naive_attention.hpp
View file @ 019d4b7c
...@@ -13,13 +13,18 @@ namespace ck_tile { ...@@ -13,13 +13,18 @@ namespace ck_tile {
enum class naive_attention_layout_enum enum class naive_attention_layout_enum
{ {
BSHD, // [batch, seqlen, nhead, hdim] DEFAULT, // maybe this tensor is not used, set some irrelevant value
BHSD, // [batch, nhead, seqlen, hdim] BSHD, // [batch, seqlen, nhead, hdim]
BS3HD, // [batch, nhead, 3, seqlen, hdim], used when qkv are packed BHSD, // [batch, nhead, seqlen, hdim]
PHSD, // [pages, nhead, page_size, hdim] BS3HD, // [batch, nhead, 3, seqlen, hdim], used when qkv are packed
PHSD, // [pages, nhead, page_size, hdim]
// PHSDX, // [pages, nhead, page_size/x, hdim, x], where <# used pages>*page_size = seqlen // PHSDX, // [pages, nhead, page_size/x, hdim, x], where <# used pages>*page_size = seqlen
PHDSX, // [pages, nhead, hdim/x, page_size, x], where <# used pages>*page_size = seqlen PHDSX, // [pages, nhead, hdim/x, page_size, x], where <# used pages>*page_size = seqlen
PHDS, // [pages, nhead, hdim, page_size], where <# used pages>*page_size = seqlen PHDS, // [pages, nhead, hdim, page_size], where <# used pages>*page_size = seqlen
// scale layout used for dynamic dequant
SCALE_HS, // [nhead, tokens] or [nhead, tokens-per-group], nhe KVCache quant
SCALE_SH, // [tokens, nhead]
}; };
// will used to specialize kernel variation // will used to specialize kernel variation
...@@ -30,6 +35,15 @@ enum class naive_attention_variation_enum ...@@ -30,6 +35,15 @@ enum class naive_attention_variation_enum
DECODE_PAGED, // decode attn, where kv token from another buffer called kvcache DECODE_PAGED, // decode attn, where kv token from another buffer called kvcache
}; };
enum class naive_attention_quant_algo
{
NO = 0,
KV_8BIT_PERHEAD = 1,
// FP8/INT8 quant for KVCache, per-token quant
// [num_tokens, nhead, hdim] -> [nhead, num_tokens]
KV_8BIT_PERTOKEN = 2,
};
// TODO: for simplicity, this will be used as host/device arg // TODO: for simplicity, this will be used as host/device arg
struct naive_attention_fwd_args struct naive_attention_fwd_args
{ {
...@@ -40,7 +54,8 @@ struct naive_attention_fwd_args ...@@ -40,7 +54,8 @@ struct naive_attention_fwd_args
void* context_len_ptr; // [batch] used when seqlen kv come from a pointer(each element is a void* context_len_ptr; // [batch] used when seqlen kv come from a pointer(each element is a
// number, not cumsum) // number, not cumsum)
void* page_table_ptr; // [batch, max_pages_per_seq] seqlen_kv is in different block(paged attn) void* page_table_ptr; // [batch, max_pages_per_seq] seqlen_kv is in different block(paged attn)
void* kvscale_ptr; // [nhead, 2(kv), hdim] used for kvcache dequant void* kscale_ptr; // [nhead, max_kv_tokens] used for kvcache dequant
void* vscale_ptr; // [nhead, max_kv_tokens] used for kvcache dequant
float scale_s; float scale_s;
int hdim; int hdim;
int hdim_v; // could be cross-attn, where V and Q/K hdim are different int hdim_v; // could be cross-attn, where V and Q/K hdim are different
...@@ -54,6 +69,7 @@ struct naive_attention_fwd_args ...@@ -54,6 +69,7 @@ struct naive_attention_fwd_args
int nhead_ratio_kv; // nhead_q / nhead_kv int nhead_ratio_kv; // nhead_q / nhead_kv
int page_size; // if paged, the seqlen-kv per each block int page_size; // if paged, the seqlen-kv per each block
int max_pages_per_seq; int max_pages_per_seq;
int max_kv_tokens; // used as stride to access kv scale ptr
}; };
// this is trait for host API // this is trait for host API
...@@ -67,14 +83,16 @@ struct naive_attention_fwd_traits ...@@ -67,14 +83,16 @@ struct naive_attention_fwd_traits
std::string k_layout; std::string k_layout;
std::string v_layout; std::string v_layout;
std::string o_layout; std::string o_layout;
int variation; // sync with naive_attention_variation_enum int variation; // sync with naive_attention_variation_enum
int quant_algo; // sync with naive_attention_quant_algo
}; };
// this is trait for kernel template // this is trait for kernel template
template <naive_attention_variation_enum variation_> template <naive_attention_variation_enum variation_, naive_attention_quant_algo quant_algo_>
struct naive_attention_fwd_kernel_traits struct naive_attention_fwd_kernel_traits
{ {
static constexpr naive_attention_variation_enum variation = variation_; static constexpr naive_attention_variation_enum variation = variation_;
static constexpr naive_attention_quant_algo quant_algo = quant_algo_;
}; };
// for simplicity, please do not use const-reference type for the template type // for simplicity, please do not use const-reference type for the template type
...@@ -83,28 +101,39 @@ template <typename QType, ...@@ -83,28 +101,39 @@ template <typename QType,
typename VType, typename VType,
typename OType, typename OType,
typename AccType, typename AccType,
typename KVScaleType,
naive_attention_layout_enum QLayout, naive_attention_layout_enum QLayout,
naive_attention_layout_enum KLayout, naive_attention_layout_enum KLayout,
naive_attention_layout_enum VLayout, naive_attention_layout_enum VLayout,
naive_attention_layout_enum OLayout, naive_attention_layout_enum OLayout,
naive_attention_layout_enum KScaleLayout,
naive_attention_layout_enum VScaleLayout,
typename Traits> typename Traits>
struct naive_attention_fwd_kernel struct naive_attention_fwd_kernel
{ {
static constexpr bool is_kvcache_i8 = static constexpr bool is_kvcache_i8 =
std::is_same_v<KType, int8_t> && std::is_same_v<VType, int8_t> && sizeof(QType) != 1; std::is_same_v<KType, int8_t> && std::is_same_v<VType, int8_t>;
static constexpr bool is_kvcache_fp8 =
std::is_same_v<KType, fp8_t> && std::is_same_v<VType, fp8_t>;
// kvcache-i8 will have per head scale, we apply this scale to Q/P matrix instead of original static constexpr int v_per_token_quant_group_size = 64;
// K/V matrix. This can speed up conversion since Q/P usually is fp16/bf16/fp32
static constexpr bool is_kvcache_i8_forward_quant = is_kvcache_i8;
// TODO: hardcode // TODO: hardcode
using KVScaleType = float; using SoftmaxType = float; // always using float to do softmax compute
using SoftmaxType = float; using QuantComputeType = float; // used for quant/dequant scale compute
using PType = VType; // src A of gemm2, same type as V using QCompute = KType; // src A of gemm1, same type as K
using PType = VType; // src A of gemm2, same type as V
using OAccType = float; // always float, in case int8 FA
using p_vec_type = ext_vector_t<PType, 16 / sizeof(PType)>; using p_vec_type = ext_vector_t<PType, 16 / sizeof(PType)>;
static constexpr int p_vec_elem = vector_traits<p_vec_type>::vector_size; static constexpr int p_vec_elem = vector_traits<p_vec_type>::vector_size;
// clang-format off
template <typename T_> struct scale_max { static constexpr float value = 1; /* dummy code */ };
template <> struct scale_max<int8_t> { static constexpr float value = 127.0; };
template <> struct scale_max<fp8_t> { static constexpr float value = 240.0; };
// clang-format on
__host__ __device__ naive_attention_fwd_kernel() {} __host__ __device__ naive_attention_fwd_kernel() {}
template <typename T, naive_attention_layout_enum Layout> template <typename T, naive_attention_layout_enum Layout>
...@@ -198,24 +227,31 @@ struct naive_attention_fwd_kernel ...@@ -198,24 +227,31 @@ struct naive_attention_fwd_kernel
__device__ void store(T /*value*/, int /*i_s*/, int /*i_d*/) {} __device__ void store(T /*value*/, int /*i_s*/, int /*i_d*/) {}
}; };
template <typename T> template <typename T, naive_attention_layout_enum Layout>
struct kvscale_addresser struct kvscale_addresser
{ {
int h, d; // nhead, hdim int s, h, d; // seqlen(tokens), nhead, hdim
T* base_ptr; T* base_ptr;
__device__ kvscale_addresser(int h_, int d_, void* p_) __device__ kvscale_addresser(int s_, int h_, int d_, void* p_)
: h(h_), d(d_), base_ptr(reinterpret_cast<T*>(p_)) : s(s_), h(h_), d(d_), base_ptr(reinterpret_cast<T*>(p_))
{ {
} }
__device__ int get_offset(int i_h, int i_d, int i_kv /*0 or 1*/) __device__ int get_offset(int i_s, int i_h, int i_d)
{ {
if constexpr(Layout == naive_attention_layout_enum::SCALE_HS)
{
// [nhead, tokens]
(void)i_d;
return i_h * s + i_s;
}
else if constexpr(Layout == naive_attention_layout_enum::DEFAULT)
{
return 0;
}
// [h, 2, d] // [h, 2, d]
return i_h * 2 * d + i_kv * d + i_d; // return i_h * 2 * d + i_kv * d + i_d;
}
__device__ T load(int i_h, int i_d, int i_kv)
{
return base_ptr[get_offset(i_h, i_d, i_kv)];
} }
__device__ T load(int i_s, int i_h, int i_d) { return base_ptr[get_offset(i_s, i_h, i_d)]; }
}; };
__device__ __host__ static constexpr int get_block_size() { return 256; } __device__ __host__ static constexpr int get_block_size() { return 256; }
...@@ -282,12 +318,13 @@ struct naive_attention_fwd_kernel ...@@ -282,12 +318,13 @@ struct naive_attention_fwd_kernel
__device__ void operator()(naive_attention_fwd_args args) __device__ void operator()(naive_attention_fwd_args args)
{ {
constexpr int wg_size = get_block_size(); constexpr int wg_size = get_block_size();
__shared__ char smem[wg_size * 4 * sizeof(float)]; // should enough __shared__ char smem[wg_size * 4 * sizeof(float)]; // should enough
int i_dv = blockIdx.x * wg_size + threadIdx.x; // index of hdim_v char* smem_quant_q = smem + wg_size * 2 * sizeof(float); // second half, should enough
int i_sq = blockIdx.y; // index of seqlen_q int i_dv = blockIdx.x * wg_size + threadIdx.x; // index of hdim_v
int i_batch = blockIdx.z; // index of batch_q * nhead_q int i_sq = blockIdx.y; // index of seqlen_q
int i_bq = i_batch / args.nhead_q; // index of batch_q int i_batch = blockIdx.z; // index of batch_q * nhead_q
int i_hq = i_batch % args.nhead_q; // index of nhead_q int i_bq = i_batch / args.nhead_q; // index of batch_q
int i_hq = i_batch % args.nhead_q; // index of nhead_q
int i_bk = i_bq / args.batch_ratio_kv; int i_bk = i_bq / args.batch_ratio_kv;
int i_hk = i_hq / args.nhead_ratio_kv; int i_hk = i_hq / args.nhead_ratio_kv;
...@@ -360,9 +397,10 @@ struct naive_attention_fwd_kernel ...@@ -360,9 +397,10 @@ struct naive_attention_fwd_kernel
auto f_max = [](auto x_, auto y_) { return max(x_, y_); }; auto f_max = [](auto x_, auto y_) { return max(x_, y_); };
auto f_sum = [](auto x_, auto y_) { return x_ + y_; }; auto f_sum = [](auto x_, auto y_) { return x_ + y_; };
auto f_absmax_f32 = [](float v_0_, float v_1_) { auto f_absmax_f32 = [](float v_0_, float v_1_) {
float rtn; // float rtn;
asm volatile("v_max_f32 %0, abs(%1), abs(%2)" : "=v"(rtn) : "v"(v_0_), "v"(v_1_)); // asm volatile("v_max_f32 %0, abs(%1), abs(%2)" : "=v"(rtn) : "v"(v_0_), "v"(v_1_));
return rtn; // return rtn;
return max(abs(v_0_), abs(v_1_));
}; };
int seqlen_kv = [&]() { int seqlen_kv = [&]() {
...@@ -378,45 +416,82 @@ struct naive_attention_fwd_kernel ...@@ -378,45 +416,82 @@ struct naive_attention_fwd_kernel
SoftmaxType row_max = -numeric<SoftmaxType>::infinity(); SoftmaxType row_max = -numeric<SoftmaxType>::infinity();
SoftmaxType l{0}; SoftmaxType l{0};
AccType o_acc = {0}; // AccType o_acc = {0};
OAccType o_acc = {0};
int sk_loops = (seqlen_kv + wg_size - 1) / wg_size; int sk_loops = (seqlen_kv + wg_size - 1) / wg_size;
float qf_scale = .0f; QuantComputeType q_dequant_scale = .0f;
kvscale_addresser<KVScaleType> kvscale_addr{args.nhead_kv, args.hdim, args.kvscale_ptr}; kvscale_addresser<KVScaleType, KScaleLayout> kscale_addr{
args.max_kv_tokens, args.nhead_kv, args.hdim, args.kscale_ptr};
kvscale_addresser<KVScaleType, VScaleLayout> vscale_addr{
args.max_kv_tokens, args.nhead_kv, args.hdim_v, args.vscale_ptr};
if constexpr(is_kvcache_i8_forward_quant) if constexpr(Traits::quant_algo == naive_attention_quant_algo::KV_8BIT_PERHEAD)
{ {
// AccType is i32 now, seqlen_q = 1, hdim up to 256 // AccType is i32 now, seqlen_q = 1, hdim up to 256
float q = 0; AccType q = 0;
float k_s = 0; AccType k_s = 0;
if(static_cast<int>(threadIdx.x) < args.hdim) if(static_cast<int>(threadIdx.x) < args.hdim)
{ {
q = type_convert<float>(q_addr.load(0, threadIdx.x)); q = type_convert<AccType>(q_addr.load(0, threadIdx.x));
k_s = type_convert<float>(kvscale_addr.load(i_hk, threadIdx.x, 0)); k_s = type_convert<AccType>(kscale_addr.load(i_hk, threadIdx.x, 0));
} }
// 1) we apply the k scale to q // 1) we apply the k scale to q
float q_forwarded = q * k_s; AccType q_forwarded = q * k_s;
// 2) apply smooth-quant // 2) apply smooth-quant
// find absmax // find absmax
float qf_max = wave_reduce(q_forwarded, f_absmax_f32); AccType qf_max = wave_reduce(q_forwarded, f_absmax_f32);
qf_max = cross_wave_reduce(qf_max, f_absmax_f32, reinterpret_cast<float*>(smem)); qf_max = cross_wave_reduce(qf_max, f_absmax_f32, reinterpret_cast<AccType*>(smem));
// per-token scale // per-token scale
qf_scale = qf_max / 127.0; q_dequant_scale = type_convert<QuantComputeType>(qf_max) / scale_max<QCompute>::value;
// devide by scale // devide by scale
q = q / qf_scale; q = q / q_dequant_scale;
// fp32->i8 // fp32->i8
int8_t quantized_q = static_cast<int8_t>(q); QCompute quantized_q = static_cast<QCompute>(q);
__syncthreads(); __syncthreads();
reinterpret_cast<int8_t*>(smem)[threadIdx.x] = quantized_q; reinterpret_cast<QCompute*>(smem)[threadIdx.x] = quantized_q;
__syncthreads(); __syncthreads();
// after above process, we have 2 data // after above process, we have 2 data
// 1) int8 q data stored in smem(no need to reload) // 1) int8 q data stored in smem(no need to reload)
// 2) per-token scale qf_scale, to be mul after 1st gemm // 2) per-token scale q_dequant_scale, to be mul after 1st gemm
}
else if constexpr(Traits::quant_algo == naive_attention_quant_algo::KV_8BIT_PERTOKEN)
{
if(std::is_same_v<QType, fp16_t> || std::is_same_v<QType, bf16_t>)
{
// dyanmic quant q here
float q = 0;
if(static_cast<int>(threadIdx.x) < args.hdim)
{
q = type_convert<float>(q_addr.load(i_sq, threadIdx.x));
}
// apply smooth-quant
// find absmax
float q_max = wave_reduce(q, f_absmax_f32);
q_max = cross_wave_reduce(q_max, f_absmax_f32, reinterpret_cast<float*>(smem));
// per-token scale
q_dequant_scale =
type_convert<QuantComputeType>(q_max) / scale_max<QCompute>::value;
// devide by scale
q = q / q_dequant_scale;
QCompute quantized_q = type_convert<QCompute>(q);
__syncthreads();
reinterpret_cast<QCompute*>(smem_quant_q)[threadIdx.x] = quantized_q;
__syncthreads();
// after above process, we have 2 data
// 1) fp8 q data stored in smem(no need to reload from global)
// 2) per-token scale q_dequant_scale, to be mul after 1st gemm
}
} }
for(int i_loop1 = 0; i_loop1 < sk_loops; i_loop1++) for(int i_loop1 = 0; i_loop1 < sk_loops; i_loop1++)
...@@ -429,33 +504,41 @@ struct naive_attention_fwd_kernel ...@@ -429,33 +504,41 @@ struct naive_attention_fwd_kernel
AccType s_acc{0}; // clear for every loop AccType s_acc{0}; // clear for every loop
for(auto i_dq = 0; i_dq < args.hdim; i_dq++) for(auto i_dq = 0; i_dq < args.hdim; i_dq++)
{ {
if constexpr(is_kvcache_i8_forward_quant) auto q = [&]() {
{ if constexpr(Traits::quant_algo ==
int8_t q = reinterpret_cast<int8_t*>(smem)[i_dq]; naive_attention_quant_algo::KV_8BIT_PERHEAD ||
auto k = k_addr.load(i_sk, i_dq); Traits::quant_algo ==
naive_attention_quant_algo::KV_8BIT_PERTOKEN)
s_acc += type_convert<AccType>(q) * type_convert<AccType>(k); {
} return reinterpret_cast<QCompute*>(smem_quant_q)[i_dq];
else }
{ else
auto q = q_addr.load(i_sq, i_dq); // q will have duplicate load return q_addr.load(i_sq, i_dq); // q will have duplicate load
auto k = k_addr.load(i_sk, i_dq); }();
auto k = [&]() { return k_addr.load(i_sk, i_dq); }();
s_acc += type_convert<AccType>(q) * type_convert<AccType>(k); s_acc += type_convert<AccType>(q) * type_convert<AccType>(k);
}
} }
// scale // scale
s_softmax = type_convert<SoftmaxType>(s_acc); s_softmax = type_convert<SoftmaxType>(s_acc);
s_softmax *= s_softmax *=
type_convert<SoftmaxType>(args.scale_s * ck_tile::log2e_v<SoftmaxType>); type_convert<SoftmaxType>(args.scale_s * ck_tile::log2e_v<SoftmaxType>);
if constexpr(is_kvcache_i8_forward_quant) if constexpr(Traits::quant_algo == naive_attention_quant_algo::KV_8BIT_PERHEAD)
{
s_softmax *= q_dequant_scale; // post scale the per-token factor
}
else if constexpr(Traits::quant_algo ==
naive_attention_quant_algo::KV_8BIT_PERTOKEN)
{ {
s_softmax *= qf_scale; // post scale the per-token factor SoftmaxType k_per_token_scale =
type_convert<SoftmaxType>(kscale_addr.load(i_sk, i_hk, 0));
s_softmax *= q_dequant_scale;
s_softmax *= k_per_token_scale;
} }
} }
// s->p // s->p
float pf_scale = 0.; // used for i8 quant QuantComputeType p_dequant_scale = 1.;
{ {
// softmax, find max // softmax, find max
SoftmaxType old_max = row_max; SoftmaxType old_max = row_max;
...@@ -473,41 +556,69 @@ struct naive_attention_fwd_kernel ...@@ -473,41 +556,69 @@ struct naive_attention_fwd_kernel
// l, pre-scall o_acc // l, pre-scall o_acc
SoftmaxType tmp = __builtin_amdgcn_exp2f(old_max - row_max); SoftmaxType tmp = __builtin_amdgcn_exp2f(old_max - row_max);
l = tmp * l + row_sum; l = tmp * l + row_sum;
o_acc = type_convert<AccType>(type_convert<SoftmaxType>(o_acc) * tmp); o_acc = type_convert<OAccType>(type_convert<SoftmaxType>(o_acc) * tmp);
// prepare the p_compute into smem, to let every thread read same p_compute and do // prepare the p_compute into smem, to let every thread read same p_compute and do
// 2nd gemm // 2nd gemm
if constexpr(is_kvcache_i8_forward_quant) if constexpr(Traits::quant_algo == naive_attention_quant_algo::KV_8BIT_PERHEAD)
{ {
float v_s = 0; QuantComputeType v_s = 0;
if(static_cast<int>(threadIdx.x) < args.hdim_v) if(static_cast<int>(threadIdx.x) < args.hdim_v)
{ {
v_s = type_convert<float>(kvscale_addr.load(i_hk, threadIdx.x, 1)); v_s =
type_convert<QuantComputeType>(vscale_addr.load(i_hk, threadIdx.x, 1));
} }
// 1) we apply the v scale to p // 1) we apply the v scale to p
float p_forwarded = p_compute * v_s; QuantComputeType p_forwarded = p_compute * v_s;
// 2) apply smooth-quant // 2) apply smooth-quant
// find absmax // find absmax
float pf_max = wave_reduce(p_forwarded, f_absmax_f32); QuantComputeType pf_max = wave_reduce(p_forwarded, f_absmax_f32);
pf_max = pf_max = cross_wave_reduce(
cross_wave_reduce(pf_max, f_absmax_f32, reinterpret_cast<float*>(smem)); pf_max, f_absmax_f32, reinterpret_cast<QuantComputeType*>(smem));
// per-token scale // per-token scale
pf_scale = pf_max / 127.0; p_dequant_scale = pf_max / scale_max<PType>::value; // 127.0;
// devide by scale // devide by scale
p_compute = p_compute / pf_scale; p_compute = p_compute / p_dequant_scale;
// fp32->i8 // fp32->i8
int8_t quantized_p = static_cast<int8_t>(p_compute); PType quantized_p = static_cast<PType>(p_compute);
__syncthreads(); __syncthreads();
reinterpret_cast<int8_t*>(smem)[threadIdx.x] = quantized_p; reinterpret_cast<PType*>(smem)[threadIdx.x] = quantized_p;
__syncthreads(); __syncthreads();
// after above process, we have 2 data // after above process, we have 2 data
// 1) int8 p data stored in smem(no need to reload) // 1) int8 p data stored in smem(no need to reload)
// 2) per-token scale pf_scale, to be mul after 2nd gemm // 2) per-token scale p_dequant_scale, to be mul after 2nd gemm
}
else if constexpr(Traits::quant_algo ==
naive_attention_quant_algo::KV_8BIT_PERTOKEN)
{
// forward apply the v scale to p_compute, this is compute friendly
auto v_scale = type_convert<QuantComputeType>(vscale_addr.load(i_sk, i_hk, 0));
p_compute *= v_scale;
// smooth-quant
// find absmax
QuantComputeType p_max = wave_reduce(p_compute, f_absmax_f32);
p_max = cross_wave_reduce(
p_max, f_absmax_f32, reinterpret_cast<QuantComputeType*>(smem));
// per-token scale
p_dequant_scale = p_max / scale_max<PType>::value; // 240.0;
// devide by scale
p_compute = p_compute / p_dequant_scale;
// fp32->i8
PType quantized_p = type_convert<PType>(p_compute);
__syncthreads();
reinterpret_cast<PType*>(smem)[threadIdx.x] = quantized_p;
__syncthreads();
// after above process, we have 2 data
// 1) fp8_t p data stored in smem(no need to reload)
// 2) per-token scale p_dequant_scale, to be mul after 2nd gemm
} }
else else
{ {
...@@ -531,29 +642,45 @@ struct naive_attention_fwd_kernel ...@@ -531,29 +642,45 @@ struct naive_attention_fwd_kernel
int sv_offset = i_loop2 * p_vec_elem + i_j; int sv_offset = i_loop2 * p_vec_elem + i_j;
int i_sv = sk_start + sv_offset; int i_sv = sk_start + sv_offset;
VType v = 0.f; VType v = 0;
if(i_dv < args.hdim_v && i_sv < seqlen_kv) if(i_dv < args.hdim_v && i_sv < seqlen_kv)
{ {
v = v_addr.load(i_sv, i_dv); v = v_addr.load(i_sv, i_dv);
} }
o_acc_local += type_convert<AccType>(p_vec[i_j]) * type_convert<AccType>(v); AccType v_compute = [&]() { return type_convert<AccType>(v); }();
o_acc_local += type_convert<AccType>(p_vec[i_j]) * v_compute;
} }
} }
if constexpr(is_kvcache_i8_forward_quant)
{ OAccType post_scale_o_acc_local = [&]() {
// apply pr scale to local acc if constexpr(Traits::quant_algo == naive_attention_quant_algo::KV_8BIT_PERHEAD)
o_acc_local = {
type_convert<AccType>(type_convert<float>(o_acc_local) * pf_scale); // apply pr scale to local acc
} return type_convert<OAccType>(type_convert<QuantComputeType>(o_acc_local) *
o_acc += o_acc_local; p_dequant_scale);
}
else if constexpr(Traits::quant_algo ==
naive_attention_quant_algo::KV_8BIT_PERTOKEN)
{
// apply pr scale to local acc
return type_convert<OAccType>(type_convert<QuantComputeType>(o_acc_local) *
p_dequant_scale);
}
else
{
return type_convert<OAccType>(o_acc_local);
}
}();
o_acc += post_scale_o_acc_local;
} }
} }
// post scale o_acc // post scale o_acc
{ {
SoftmaxType tmp = l == 0.f ? 0.f : 1.f / l; // in case masking SoftmaxType tmp = l == 0.f ? 0.f : 1.f / l; // in case masking
o_acc = type_convert<AccType>(type_convert<SoftmaxType>(o_acc) * tmp); o_acc = type_convert<OAccType>(type_convert<SoftmaxType>(o_acc) * tmp);
} }
// store O // store O
...@@ -564,18 +691,21 @@ struct naive_attention_fwd_kernel ...@@ -564,18 +691,21 @@ struct naive_attention_fwd_kernel
#define CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_() \ #define CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_() \
{ \ { \
using ktraits_ = \ using ktraits_ = naive_attention_fwd_kernel_traits< \
naive_attention_fwd_kernel_traits<static_cast<naive_attention_variation_enum>( \ static_cast<naive_attention_variation_enum>(variation_), \
variation_)>; \ static_cast<naive_attention_quant_algo>(quant_algo_)>; \
using k_ = naive_attention_fwd_kernel<q_type_, \ using k_ = naive_attention_fwd_kernel<q_type_, \
k_type_, \ k_type_, \
v_type_, \ v_type_, \
o_type_, \ o_type_, \
acc_type_, \ acc_type_, \
kvscale_type_, \
q_layout_, \ q_layout_, \
k_layout_, \ k_layout_, \
v_layout_, \ v_layout_, \
o_layout_, \ o_layout_, \
k_scale_layout_, \
v_scale_layout_, \
ktraits_>; \ ktraits_>; \
dim3 grids = k_::get_grid_size(a); \ dim3 grids = k_::get_grid_size(a); \
r = ck_tile::launch_kernel(s, \ r = ck_tile::launch_kernel(s, \
...@@ -586,31 +716,37 @@ struct naive_attention_fwd_kernel ...@@ -586,31 +716,37 @@ struct naive_attention_fwd_kernel
if(t.variation == 0 && t.q_layout == "bshd" && t.k_layout == "bshd" && t.v_layout == "bshd" && \ if(t.variation == 0 && t.q_layout == "bshd" && t.k_layout == "bshd" && t.v_layout == "bshd" && \
t.o_layout == "bshd") \ t.o_layout == "bshd") \
{ \ { \
constexpr auto q_layout_ = naive_attention_layout_enum::BSHD; \ constexpr auto q_layout_ = naive_attention_layout_enum::BSHD; \
constexpr auto k_layout_ = naive_attention_layout_enum::BSHD; \ constexpr auto k_layout_ = naive_attention_layout_enum::BSHD; \
constexpr auto v_layout_ = naive_attention_layout_enum::BSHD; \ constexpr auto v_layout_ = naive_attention_layout_enum::BSHD; \
constexpr auto o_layout_ = naive_attention_layout_enum::BSHD; \ constexpr auto o_layout_ = naive_attention_layout_enum::BSHD; \
constexpr int variation_ = 0; \ constexpr auto k_scale_layout_ = naive_attention_layout_enum::DEFAULT; \
constexpr auto v_scale_layout_ = naive_attention_layout_enum::DEFAULT; \
constexpr int variation_ = 0; \
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \ CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \
} \ } \
else if(t.variation == 0 && t.q_layout == "bhsd" && t.k_layout == "bhsd" && \ else if(t.variation == 0 && t.q_layout == "bhsd" && t.k_layout == "bhsd" && \
t.v_layout == "bhsd" && t.o_layout == "bhsd") \ t.v_layout == "bhsd" && t.o_layout == "bhsd") \
{ \ { \
constexpr auto q_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto q_layout_ = naive_attention_layout_enum::BHSD; \
constexpr auto k_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto k_layout_ = naive_attention_layout_enum::BHSD; \
constexpr auto v_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto v_layout_ = naive_attention_layout_enum::BHSD; \
constexpr auto o_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto o_layout_ = naive_attention_layout_enum::BHSD; \
constexpr int variation_ = 0; \ constexpr auto k_scale_layout_ = naive_attention_layout_enum::DEFAULT; \
constexpr auto v_scale_layout_ = naive_attention_layout_enum::DEFAULT; \
constexpr int variation_ = 0; \
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \ CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \
} \ } \
else if(t.variation == 2 && t.q_layout == "bhsd" && t.k_layout == "phdsx" && \ else if(t.variation == 2 && t.q_layout == "bhsd" && t.k_layout == "phdsx" && \
t.v_layout == "phds" && t.o_layout == "bhsd") \ t.v_layout == "phds" && t.o_layout == "bhsd") \
{ \ { \
constexpr auto q_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto q_layout_ = naive_attention_layout_enum::BHSD; \
constexpr auto k_layout_ = naive_attention_layout_enum::PHDSX; \ constexpr auto k_layout_ = naive_attention_layout_enum::PHDSX; \
constexpr auto v_layout_ = naive_attention_layout_enum::PHDS; \ constexpr auto v_layout_ = naive_attention_layout_enum::PHDS; \
constexpr auto o_layout_ = naive_attention_layout_enum::BHSD; \ constexpr auto o_layout_ = naive_attention_layout_enum::BHSD; \
constexpr int variation_ = 2; \ constexpr auto k_scale_layout_ = naive_attention_layout_enum::SCALE_HS; \
constexpr auto v_scale_layout_ = naive_attention_layout_enum::SCALE_HS; \
constexpr int variation_ = 2; \
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \ CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_INTERNAL_(); \
} }
...@@ -621,40 +757,64 @@ CK_TILE_HOST float naive_attention_fwd(naive_attention_fwd_traits t, ...@@ -621,40 +757,64 @@ CK_TILE_HOST float naive_attention_fwd(naive_attention_fwd_traits t,
{ {
float r = -1; float r = -1;
// TODO: do not explicitly create too much instance! // TODO: do not explicitly create too much instance!
if(t.q_type == "fp16" && t.k_type == "fp16" && t.v_type == "fp16" && t.o_type == "fp16") if(t.q_type == "fp16" && t.k_type == "fp16" && t.v_type == "fp16" && t.o_type == "fp16" &&
t.quant_algo == 0)
{
using q_type_ = fp16_t;
using k_type_ = fp16_t;
using v_type_ = fp16_t;
using o_type_ = fp16_t;
using acc_type_ = float;
using kvscale_type_ = float;
constexpr int quant_algo_ = 0;
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_();
}
else if(t.q_type == "bf16" && t.k_type == "bf16" && t.v_type == "bf16" && t.o_type == "bf16" &&
t.quant_algo == 0)
{ {
using q_type_ = fp16_t; using q_type_ = bf16_t;
using k_type_ = fp16_t; using k_type_ = bf16_t;
using v_type_ = fp16_t; using v_type_ = bf16_t;
using o_type_ = fp16_t; using o_type_ = bf16_t;
using acc_type_ = float; using acc_type_ = float;
using kvscale_type_ = float;
constexpr int quant_algo_ = 0;
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_(); CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_();
} }
else if(t.q_type == "bf16" && t.k_type == "bf16" && t.v_type == "bf16" && t.o_type == "bf16") else if(t.q_type == "bf16" && t.k_type == "fp8" && t.v_type == "fp8" && t.o_type == "bf16" &&
t.quant_algo == 2)
{ {
using q_type_ = bf16_t; using q_type_ = bf16_t;
using k_type_ = bf16_t; using k_type_ = fp8_t;
using v_type_ = bf16_t; using v_type_ = fp8_t;
using o_type_ = bf16_t; using o_type_ = bf16_t;
using acc_type_ = float; using acc_type_ = float; // NOTE!
using kvscale_type_ = float;
constexpr int quant_algo_ = 2;
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_(); CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_();
} }
else if(t.q_type == "bf16" && t.k_type == "int8" && t.v_type == "int8" && t.o_type == "bf16") else if(t.q_type == "fp16" && t.k_type == "fp8" && t.v_type == "fp8" && t.o_type == "fp16" &&
t.quant_algo == 2)
{ {
using q_type_ = bf16_t; using q_type_ = fp16_t;
using k_type_ = int8_t; using k_type_ = fp8_t;
using v_type_ = int8_t; using v_type_ = fp8_t;
using o_type_ = bf16_t; using o_type_ = fp16_t;
using acc_type_ = int32_t; // NOTE! using acc_type_ = float; // NOTE!
using kvscale_type_ = float;
constexpr int quant_algo_ = 2;
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_(); CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_();
} }
else if(t.q_type == "fp16" && t.k_type == "int8" && t.v_type == "int8" && t.o_type == "fp16") else if(t.q_type == "bf16" && t.k_type == "int8" && t.v_type == "int8" && t.o_type == "bf16" &&
t.quant_algo == 2)
{ {
using q_type_ = fp16_t; using q_type_ = bf16_t;
using k_type_ = int8_t; using k_type_ = int8_t;
using v_type_ = int8_t; using v_type_ = int8_t;
using o_type_ = fp16_t; using o_type_ = bf16_t;
using acc_type_ = int32_t; // NOTE! using acc_type_ = int32_t; // NOTE!
using kvscale_type_ = float;
constexpr int quant_algo_ = 2;
CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_(); CK_TILE_DISPATCH_NAIVE_ATTEN_FWD_LAOYUT_();
} }
return r; return r;
......
library/include/ck/library/reference_tensor_operation/cpu/reference_fpAintB_gemm.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -73,39 +73,9 @@ struct ReferencefpAintBGemm : public device::BaseOperator ...@@ -73,39 +73,9 @@ struct ReferencefpAintBGemm : public device::BaseOperator
ScaleDataType v_scale; ScaleDataType v_scale;
ADataType v_converted_b; ADataType v_converted_b;
// use PassThrough instead of ConvertBF16RTN for reference calculation arg.a_element_op_(v_a, arg.a_m_k_(m, k));
if constexpr(is_same_v<AElementwiseOperation, arg.b_element_op_(v_b, arg.b_k_n_(k, n));
ck::tensor_operation::element_wise::ConvertBF16RTN>) arg.b_element_op_(v_scale, arg.scale_k_n_(k, n));
{
ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k));
}
else
{
arg.a_element_op_(v_a, arg.a_m_k_(m, k));
}
// same for B matrix
if constexpr(is_same_v<BElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_k_n_(k, n));
}
else
{
arg.b_element_op_(v_b, arg.b_k_n_(k, n));
}
// same for scale matrix
if constexpr(is_same_v<BElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_scale,
arg.scale_k_n_(k, n));
}
else
{
arg.b_element_op_(v_scale, arg.scale_k_n_(k, n));
}
v_converted_b = type_convert<ADataType>(v_b) * v_scale; v_converted_b = type_convert<ADataType>(v_b) * v_scale;
v_acc += ck::type_convert<AccDataType>(v_a) * v_acc += ck::type_convert<AccDataType>(v_a) *
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -68,21 +68,32 @@ struct ReferenceGemm : public device::BaseOperator ...@@ -68,21 +68,32 @@ struct ReferenceGemm : public device::BaseOperator
for(int k = 0; k < K; ++k) for(int k = 0; k < K; ++k)
{ {
// use PassThrough instead of ConvertBF16RTN for reference calculation if constexpr(is_same_v<ADataType, pk_i4_t>)
if constexpr(is_same_v<AElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{ {
ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k)); uint8_t i4x2 = arg.a_m_k_(m, k).data;
int8_t i4 = 0;
if(k % 2 == 1)
i4 = (i4x2 >> 0) & 0xf;
else
i4 = (i4x2 >> 4) & 0xf;
i4 = i4 - 8;
v_a = type_convert<ComputeTypeA>(i4);
} }
else else
{ {
arg.a_element_op_(v_a, arg.a_m_k_(m, k)); arg.a_element_op_(v_a, arg.a_m_k_(m, k));
} }
// same for B matrix
if constexpr(is_same_v<BElementwiseOperation, if constexpr(is_same_v<BDataType, pk_i4_t>)
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{ {
ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_k_n_(k, n)); uint8_t i4x2 = arg.b_k_n_(k, n).data;
int8_t i4 = 0;
if(k % 2 == 1)
i4 = (i4x2 >> 0) & 0xf;
else
i4 = (i4x2 >> 4) & 0xf;
i4 = i4 - 8;
v_b = type_convert<ComputeTypeB>(i4);
} }
else else
{ {
......
library/include/ck/library/reference_tensor_operation/cpu/reference_gemm_multiple_d.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -74,26 +74,8 @@ struct ReferenceGemmMultipleD : public device::BaseOperator ...@@ -74,26 +74,8 @@ struct ReferenceGemmMultipleD : public device::BaseOperator
for(int k = 0; k < K; ++k) for(int k = 0; k < K; ++k)
{ {
// use PassThrough instead of ConvertBF16RTN for reference calculation arg.a_element_op_(v_a, arg.a_m_k_(m, k));
if constexpr(is_same_v<AElementwiseOperation, arg.b_element_op_(v_b, arg.b_k_n_(k, n));
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_a, arg.a_m_k_(m, k));
}
else
{
arg.a_element_op_(v_a, arg.a_m_k_(m, k));
}
// same for B matrix
if constexpr(is_same_v<BElementwiseOperation,
ck::tensor_operation::element_wise::ConvertBF16RTN>)
{
ck::tensor_operation::element_wise::PassThrough{}(v_b, arg.b_k_n_(k, n));
}
else
{
arg.b_element_op_(v_b, arg.b_k_n_(k, n));
}
v_acc += v_acc +=
ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b); ck::type_convert<AccDataType>(v_a) * ck::type_convert<AccDataType>(v_b);
......
library/include/ck/library/tensor_operation_instance/device_operation_instance_factory.hpp
View file @ 019d4b7c
...@@ -22,6 +22,7 @@ using I8 = int8_t; ...@@ -22,6 +22,7 @@ using I8 = int8_t;
using I32 = int32_t; using I32 = int32_t;
using F8 = ck::f8_t; using F8 = ck::f8_t;
using BF8 = ck::bf8_t; using BF8 = ck::bf8_t;
using I4 = ck::pk_i4_t;
using Empty_Tuple = ck::Tuple<>; using Empty_Tuple = ck::Tuple<>;
......
library/include/ck/library/tensor_operation_instance/gpu/gemm.hpp
View file @ 019d4b7c
...@@ -15,6 +15,9 @@ ...@@ -15,6 +15,9 @@
#ifdef DL_KERNELS #ifdef DL_KERNELS
#include "gemm_dl.inc" #include "gemm_dl.inc"
#endif #endif
#ifdef DPP_KERNELS
#include "gemm_dpp.inc"
#endif
#ifdef CK_USE_WMMA #ifdef CK_USE_WMMA
#include "gemm_wmma.inc" #include "gemm_wmma.inc"
#endif #endif
...@@ -92,32 +95,24 @@ struct DeviceOperationInstanceFactory< ...@@ -92,32 +95,24 @@ struct DeviceOperationInstanceFactory<
{ {
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
} }
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> && else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
{ {
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
} }
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> && else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
{ {
add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
} }
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> && else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>) is_same_v<CLayout, Row>)
{ {
add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs); add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
} }
} }
#endif #endif
...@@ -153,6 +148,39 @@ struct DeviceOperationInstanceFactory< ...@@ -153,6 +148,39 @@ struct DeviceOperationInstanceFactory<
#endif #endif
#endif // DL_KERNELS #endif // DL_KERNELS
#ifdef DPP_KERNELS
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<CDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(op_ptrs);
add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(op_ptrs);
}
}
#endif
#endif // DPP_KERNELS
#ifdef CK_USE_WMMA #ifdef CK_USE_WMMA
#ifdef CK_ENABLE_FP16 #ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> && if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_b_scale.hpp 0 → 100644
View file @ 019d4b7c
// SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_gemm_xdl_cshuffle_v3_b_scale.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include <memory>
#include <vector>
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#if(defined(CK_ENABLE_FP16) || defined(CK_ENABLE_FP8))
void add_device_gemm_b_scale_xdl_f16_i4_f16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemmV2BScale<Row,
Col,
Row,
F16,
I4,
F16,
F16,
1,
128,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
template <typename ADataType,
typename BDataType,
typename BScaleDataType,
typename CDataType,
typename ALayout,
typename BLayout,
typename CLayout,
index_t ScaleBlockK>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmV2BScale<
ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
BScaleDataType,
CDataType,
1,
ScaleBlockK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{
using DeviceOp = DeviceGemmV2BScale<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
BScaleDataType,
CDataType,
1,
ScaleBlockK,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, pk_i4_t> &&
is_same_v<CDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_b_scale_xdl_f16_i4_f16_mk_nk_mn_mem_v2_default_instances(op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/gemm_dl.inc
View file @ 019d4b7c
...@@ -28,16 +28,6 @@ void add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances( ...@@ -28,16 +28,6 @@ void add_device_gemm_dl_f16_f16_f16_km_kn_mn_irregular_instances(
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances); instances);
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances( void add_device_gemm_dl_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
...@@ -48,16 +38,6 @@ void add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances( ...@@ -48,16 +38,6 @@ void add_device_gemm_dl_f16_f16_f16_km_nk_mn_irregular_instances(
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances); instances);
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances( void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
...@@ -68,16 +48,6 @@ void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances( ...@@ -68,16 +48,6 @@ void add_device_gemm_dl_f16_f16_f16_mk_kn_mn_irregular_instances(
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances); instances);
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances( void add_device_gemm_dl_f16_f16_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_dpp.inc 0 → 100644
View file @ 019d4b7c
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <memory>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
#if defined(CK_ENABLE_FP16)
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_km_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Col, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_kn_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Row, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_dpp_f16_f16_f16_mk_nk_mn_irregular_instances(
std::vector<std::unique_ptr<
DeviceGemm<Row, Col, Row, F16, F16, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
#endif
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/tensor_operation_instance/gpu/gemm_multiply_multiply.hpp
View file @ 019d4b7c
...@@ -16,7 +16,8 @@ namespace ck { ...@@ -16,7 +16,8 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
namespace instance { namespace instance {
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_FP8)) #ifdef CK_ENABLE_FP8
#ifdef CK_ENABLE_BF16
void add_device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_comp_default_instances( void add_device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row, std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col, Col,
...@@ -95,6 +96,86 @@ void add_device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_i ...@@ -95,6 +96,86 @@ void add_device_gemm_multiply_multiply_xdl_f8_f8_bf16_mk_nk_mn_mem_v2_kpadding_i
PassThrough, PassThrough,
MultiplyMultiply>>>& instances); MultiplyMultiply>>>& instances);
#endif #endif
#ifdef CK_ENABLE_FP16
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
void add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemmMultipleDSplitK<Row,
Col,
Tuple<Row, Col>,
Row,
F8,
F8,
Tuple<F32, F32>,
F16,
PassThrough,
PassThrough,
MultiplyMultiply>>>& instances);
#endif
#endif
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8)) #if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8))
void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instances( void add_device_gemm_multiply_multiply_xdl_i8_i8_bf16_mk_nk_mn_comp_default_instances(
...@@ -213,7 +294,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu ...@@ -213,7 +294,8 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
{ {
std::vector<std::unique_ptr<DeviceOp>> op_ptrs; std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_FP8)) #ifdef CK_ENABLE_FP8
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, f8_t> && if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, f8_t> &&
is_same_v<CDataType, bhalf_t>) is_same_v<CDataType, bhalf_t>)
{ {
...@@ -237,6 +319,31 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu ...@@ -237,6 +319,31 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemmMu
} }
} }
#endif #endif
#ifdef CK_ENABLE_FP16
if constexpr(is_same_v<ADataType, f8_t> && is_same_v<BDataType, f8_t> &&
is_same_v<CDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_comp_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_multiply_multiply_xdl_f8_f8_f16_mk_nk_mn_mem_v2_kpadding_instances(
op_ptrs);
}
}
#endif
#endif
#if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8)) #if(defined(CK_ENABLE_BF16) || defined(CK_ENABLE_INT8))
if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> && if constexpr(is_same_v<ADataType, int8_t> && is_same_v<BDataType, int8_t> &&
is_same_v<CDataType, bhalf_t>) is_same_v<CDataType, bhalf_t>)
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_universal.hpp
View file @ 019d4b7c
...@@ -166,11 +166,22 @@ void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v1_kpadding_instances ...@@ -166,11 +166,22 @@ void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v1_kpadding_instances
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>&
instances); instances);
void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v2_default_instances( void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>&
instances); instances);
void add_device_gemm_xdl_universal_f16_i4_f16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<
DeviceGemmV2<Row, Col, Row, F16, I4, F16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_xdl_universal_bf16_i4_bf16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<
DeviceGemmV2<Row, Col, Row, BF16, I4, BF16, PassThrough, PassThrough, PassThrough>>>&
instances);
void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v2_kpadding_instances( void add_device_gemm_xdl_universal_f16_f8_f16_mk_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>& DeviceGemmV2<Row, Col, Row, F16, F8, F16, PassThrough, PassThrough, PassThrough>>>&
...@@ -810,6 +821,28 @@ struct DeviceOperationInstanceFactory< ...@@ -810,6 +821,28 @@ struct DeviceOperationInstanceFactory<
} }
} }
#endif #endif
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, pk_i4_t> &&
is_same_v<CDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_f16_i4_f16_mk_nk_mn_mem_v2_default_instances(op_ptrs);
}
}
if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, pk_i4_t> &&
is_same_v<CDataType, bhalf_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_bf16_i4_bf16_mk_nk_mn_mem_v2_default_instances(
op_ptrs);
}
}
return op_ptrs; return op_ptrs;
} }
}; };
......
library/include/ck/library/tensor_operation_instance/gpu/gemm_universal_streamk.hpp
View file @ 019d4b7c
...@@ -238,6 +238,403 @@ void add_device_gemm_xdl_universal_streamk_f16_f16_f16_mk_nk_mn_mem_v2_mnkpaddin ...@@ -238,6 +238,403 @@ void add_device_gemm_xdl_universal_streamk_f16_f16_f16_mk_nk_mn_mem_v2_mnkpaddin
PassThrough>>>& instances); PassThrough>>>& instances);
#endif #endif
#ifdef CK_ENABLE_BF16
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_mnpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Row,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_mnpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_mnkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Row,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_mpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_mkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_mkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_default_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_kpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_mkpadding_instances(
std::vector<std::unique_ptr<DeviceGemm_Streamk_V2<Col,
Col,
Row,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
#endif
#if(defined(CK_ENABLE_FP8)) #if(defined(CK_ENABLE_FP8))
void add_device_gemm_xdl_universal_streamk_f16_f8_f16_mk_kn_mn_comp_default_instances( void add_device_gemm_xdl_universal_streamk_f16_f8_f16_mk_kn_mn_comp_default_instances(
std::vector<std::unique_ptr< std::vector<std::unique_ptr<
...@@ -527,6 +924,109 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemm_S ...@@ -527,6 +924,109 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGemm_S
} }
#endif #endif
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<ADataType, bhalf_t> && is_same_v<BDataType, bhalf_t> &&
is_same_v<CDataType, bhalf_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_mnpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_comp_mnkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v1_mnkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_kn_mn_mem_v2_mnkpadding_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_comp_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_mk_nk_mn_mem_v2_kpadding_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_mnpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_comp_mnkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v1_mnkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_kn_mn_mem_v2_mnkpadding_instances(
op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_mpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_comp_mkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v1_mkpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_default_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_kpadding_instances(
op_ptrs);
add_device_gemm_xdl_universal_streamk_bf16_bf16_bf16_km_nk_mn_mem_v2_mkpadding_instances(
op_ptrs);
}
}
#endif
#if(defined(CK_ENABLE_FP8)) #if(defined(CK_ENABLE_FP8))
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> && if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, f8_t> &&
is_same_v<CDataType, half_t>) is_same_v<CDataType, half_t>)
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2024-2025, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
...@@ -81,6 +81,28 @@ using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_f16_instances ...@@ -81,6 +81,28 @@ using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_f16_instances
// clang-format on // clang-format on
>; >;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_f16_irregular_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 48, 64, 32, 8, 16, 16, 3, 4, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 3, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 48, 32, 8, 16, 16, 4, 3, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 3, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 80, 32, 8, 16, 16, 4, 5, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 5, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 112, 32, 8, 16, 16, 4, 7, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 7, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, F16, F16, F16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 208, 32, 8, 16, 16, 4, 13, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 13, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
...@@ -130,6 +152,28 @@ using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_instance ...@@ -130,6 +152,28 @@ using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_instance
// clang-format on // clang-format on
>; >;
template <ck::index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename ELayout,
ConvolutionBackwardWeightSpecialization ConvSpec,
BlockGemmPipelineScheduler Scheduler,
BlockGemmPipelineVersion PipelineVersion>
using device_grouped_conv_bwd_weight_two_stage_nhwgc_xdl_c_shuffle_bf16_irregular_instances =
std::tuple<
// clang-format off
//#########################################| Num| InLayout| WeiLayout| OutLayout| InData| WeiData| OutData| AccData| In| Wei| Out| ConvBackward| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransfer| CBlockTransfer| BlockGemm| BlockGemm| NumGroups|
//#########################################| Dim| | | | Type| Type| Type| Type| Elementwise| Elementwise| Elementwise| Weight| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| ClusterLengths| ScalarPerVector| Pipeline| Pipeline| ToMerge|
//#########################################| Spatial| | | | | | | | Operation| Operation| Operation| Specialization| | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| MBlock_MPerBlock| NWaveNPerXdl| Scheduler| Version| |
//#########################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | NBlock_NPerBlock| | | | |
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 48, 64, 32, 8, 16, 16, 3, 4, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 3, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 48, 32, 8, 16, 16, 4, 3, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 3, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 80, 32, 8, 16, 16, 4, 5, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 5, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 112, 32, 8, 16, 16, 4, 7, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 7, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>,
DeviceGroupedConvBwdWeightTwoStage_Xdl_CShuffle< NDimSpatial, ALayout, BLayout, ELayout, BF16, BF16, BF16, F32, PassThrough, PassThrough, PassThrough, ConvSpec, 64, 64, 208, 32, 8, 16, 16, 4, 13, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 4, 4, false, S<4, 16, 1>, S<2, 0, 1>, S<2, 0, 1>, 1, 13, 4, false, 1, 1, S<1, 8, 1, 8>, 1, Scheduler, PipelineVersion, 1>
// clang-format on
>;
template <ck::index_t NDimSpatial, template <ck::index_t NDimSpatial,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight.hpp
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -358,6 +358,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -358,6 +358,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_irregular_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_irregular_instances(
op_ptrs);
} }
#endif #endif
#ifdef CK_ENABLE_BF16 #ifdef CK_ENABLE_BF16
...@@ -383,6 +387,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -383,6 +387,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instances( add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_irregular_instances(
op_ptrs);
add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_irregular_instances(
op_ptrs);
} }
#endif #endif
} }
...@@ -478,6 +486,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -478,6 +486,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_irregular_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_irregular_instances(
op_ptrs);
} }
#endif #endif
#ifdef CK_ENABLE_BF16 #ifdef CK_ENABLE_BF16
...@@ -503,6 +515,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -503,6 +515,10 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_instances( add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_instances(
op_ptrs); op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_irregular_instances(
op_ptrs);
add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_irregular_instances(
op_ptrs);
} }
#endif #endif
#if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8 #if defined CK_ENABLE_FP16 && defined CK_ENABLE_FP8 && defined CK_ENABLE_BF8
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_backward_weight_xdl.inc
View file @ 019d4b7c
// SPDX-License-Identifier: MIT // SPDX-License-Identifier: MIT
// Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved. // Copyright (c) 2018-2025, Advanced Micro Devices, Inc. All rights reserved.
#pragma once #pragma once
...@@ -149,6 +149,30 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_p ...@@ -149,6 +149,30 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_p
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev2_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_bf16_pipev5_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW, NGCHW,
...@@ -234,6 +258,30 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pi ...@@ -234,6 +258,30 @@ void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pi
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev2_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_nhwgc_gkyxc_nhwgk_f16_pipev5_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NHWGC,
GKYXC,
NHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instances( void add_device_grouped_conv2d_bwd_weight_two_stage_xdl_ngchw_gkyxc_ngkhw_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<2,
NGCHW, NGCHW,
...@@ -384,6 +432,30 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf1 ...@@ -384,6 +432,30 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf1
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev2_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_pipev5_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
BF16,
BF16,
BF16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev1_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_bf16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW, NGCDHW,
...@@ -469,6 +541,30 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16 ...@@ -469,6 +541,30 @@ void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16
PassThrough, PassThrough,
PassThrough>>>& instances); PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev2_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ndhwgc_gkzyxc_ndhwgk_f16_pipev5_irregular_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev1_instances( void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev1_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3, std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NGCDHW, NGCDHW,
......
library/include/ck/library/tensor_operation_instance/gpu/grouped_convolution_forward.hpp
View file @ 019d4b7c
...@@ -304,7 +304,23 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe ...@@ -304,7 +304,23 @@ struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupe
op_ptrs); op_ptrs);
} }
#endif #endif
#ifdef CK_ENABLE_BF16
if constexpr(is_same_v<InDataType, ck::bhalf_t> &&
is_same_v<WeiDataType, ck::bhalf_t> &&
is_same_v<OutDataType, ck::bhalf_t> &&
is_same_v<AComputeType, ck::bhalf_t> &&
is_same_v<BComputeType, ck::bhalf_t>)
{
add_device_grouped_conv2d_fwd_xdl_merged_groups_ngchw_gkyxc_ngkhw_bf16_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_ngchw_gkyxc_ngkhw_bf16_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_ngchw_gkyxc_ngkhw_bf16_comp_instances(op_ptrs);
add_device_grouped_conv2d_fwd_xdl_ngchw_gkyxc_ngkhw_bf16_mem_intra_instances(
op_ptrs);
add_device_grouped_conv2d_fwd_xdl_ngchw_gkyxc_ngkhw_bf16_mem_inter_instances(
op_ptrs);
}
#endif
#ifdef CK_ENABLE_INT8 #ifdef CK_ENABLE_INT8
if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> && if constexpr(is_same_v<InDataType, int8_t> && is_same_v<WeiDataType, int8_t> &&
is_same_v<OutDataType, int8_t> && is_same_v<AComputeType, int8_t> && is_same_v<OutDataType, int8_t> && is_same_v<AComputeType, int8_t> &&
......
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