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Commit 62357796 authored by danyao12's avatar danyao12
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add corresponding templates for the different scopes and merge the bwd examples

parent 36ca02f3
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example/32_batched_gemm_scale_softmax_gemm/CMakeLists.txt
View file @ 62357796
......@@ -7,8 +7,7 @@ add_example_executable(example_batched_gemm_lower_triangle_scale_softmax_gemm_pe
add_example_executable(example_grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16 grouped_gemm_lower_triangle_scale_softmax_gemm_permute_xdl_fp16.cpp)
add_example_executable(example_grouped_multihead_attention_forward grouped_multihead_attention_forward.cpp)
add_example_executable(example_batched_multihead_attention_forward batched_multihead_attention_forward.cpp)
add_example_executable(example_batched_multihead_attention_backward_pt1 batched_multihead_attention_backward_pt1.cpp)
add_example_executable(example_batched_multihead_attention_backward_pt2 batched_multihead_attention_backward_pt2.cpp)
add_example_executable(example_batched_multihead_attention_backward batched_multihead_attention_backward.cpp)
add_example_executable(example_batched_multihead_attention_train batched_multihead_attention_train.cpp)
add_custom_target(example_gemm_scale_softmax_gemm)
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_pt2.cpp → example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward.cpp
View file @ 62357796
......@@ -25,7 +25,7 @@ Kernel outputs:
#define PRINT_HOST 0
#define USING_MASK 1
#define USING_K128 1
#define RANGE_HDKO 2 // 0~2
#include <iostream>
#include <numeric>
......@@ -36,6 +36,7 @@ Kernel outputs:
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_pt1.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
......@@ -90,9 +91,81 @@ static constexpr auto TensorSpecK = ck::tensor_operation::device::TensorSpeciali
static constexpr auto TensorSpecV = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecY = ck::tensor_operation::device::TensorSpecialization::Default;
#if USING_K128
// Headdim/K/O should be a multiple of 8.
// If Headdim/K/O <= 32 , ues prototype1 1st template.
// If 32 < Headdim/K/O <= 64 , ues prototype1 2nd template.
// If 64 < Headdim/K/O <= 128, ues prototype2 2nd template.
#if(RANGE_HDKO == 0)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle<
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
32, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
1, // Gemm1NXdlPerWave
1, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 1)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
NumDimM,
NumDimN,
......@@ -121,7 +194,7 @@ using DeviceGemmInstance =
128, // MPerBlock
128, // NPerBlock
64, // KPerBlock
128, // Gemm1NPerBlock
64, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
......@@ -130,7 +203,7 @@ using DeviceGemmInstance =
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
2, // Gemm1NXdlPerWave
2, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
......@@ -154,12 +227,78 @@ using DeviceGemmInstance =
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
4, // CShuffleNXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#else
// using DeviceGemmInstance =
// ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle<
// NumDimG,
// NumDimM,
// NumDimN,
// NumDimK,
// NumDimO,
// DataType,
// GemmDataType,
// ZDataType,
// LSEDataType,
// Acc0BiasDataType,
// Acc1BiasDataType,
// AccDataType,
// ShuffleDataType,
// QKVElementOp,
// QKVElementOp,
// Scale,
// QKVElementOp,
// YElementOp,
// GemmSpec,
// TensorSpecQ,
// TensorSpecK,
// TensorSpecV,
// TensorSpecY,
// 1,
// 256,
// 128, // MPerBlock
// 128, // NPerBlock
// 64, // KPerBlock
// 64, // Gemm1NPerBlock
// 64, // Gemm1KPerBlock
// 8, // AK1
// 8, // BK1
// 2, // B1K1
// 32, // MPerXDL
// 32, // NPerXDL
// 1, // MXdlPerWave
// 4, // NXdlPerWave
// 2, // Gemm1NXdlPerWave
// 2, // Gemm2NXdlPerWave
// S<4, 64, 1>, // ABlockTransfer
// S<1, 0, 2>,
// S<1, 0, 2>,
// 2,
// 8,
// 8,
// true,
// S<4, 64, 1>, // BBlockTransfer
// S<1, 0, 2>,
// S<1, 0, 2>,
// 2,
// 8,
// 8,
// true,
// S<8, 32, 1>, // B1BlockTransfer
// S<0, 2, 1>,
// S<0, 2, 1>,
// 1,
// 2,
// 2,
// false,
// 1, // CShuffleMXdlPerWavePerShuffle
// 2, // CShuffleNXdlPerWavePerShuffle
// S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
// 8, // CShuffleBlockTransferScalarPerVector_NPerBlock
// MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 2)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle<
NumDimG,
......@@ -190,8 +329,8 @@ using DeviceGemmInstance =
128, // MPerBlock
128, // NPerBlock
64, // KPerBlock
64, // Gemm1NPerBlock
64, // Gemm1KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
......@@ -199,7 +338,7 @@ using DeviceGemmInstance =
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
4, // Gemm1NXdlPerWave
2, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
......@@ -219,15 +358,16 @@ using DeviceGemmInstance =
S<0, 2, 1>,
S<0, 2, 1>,
1,
2,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
4, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: S = alpha * Q * K^T
// fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<DataType,
......@@ -339,13 +479,14 @@ int run(int argc, char* argv[])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 512;
ck::index_t N = 512;
#if USING_K128
ck::index_t K = 128;
ck::index_t O = 128;
#else
ck::index_t K = 64;
ck::index_t O = 64;
#if(RANGE_HDKO == 0)
ck::index_t K = 32; // K/O<=32
#elif(RANGE_HDKO == 1)
ck::index_t K = 64; // 32<K/O<=64
#elif(RANGE_HDKO == 2)
ck::index_t K = 80; // 64<K/O<=128
#endif
ck::index_t O = K;
ck::index_t G0 = 3;
ck::index_t G1 = 2;
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_backward_pt1.cpp deleted 100644 → 0
View file @ 36ca02f3
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
/*
Backprop for Gemm + Softmax + Gemm fused operation, where forward prop is defined as:
Y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
Computation graph:
K^T V
| |
| |
Q --- * ----- Softmax ----- * --> Y
S P
Kernel inputs:
Q, K, V, Y, dY, per-row softmax stats (LSE)
Kernel outputs:
dQ, dK, dV
*/
#define PRINT_HOST 0
#define USING_MASK 1
#define USING_HD32 0
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_pt1.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/device_memory.hpp"
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batched_gemm.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_softmax.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_dropout.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using BF16 = ck::bhalf_t;
using F32 = float;
using U16 = unsigned short;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Scale = ck::tensor_operation::element_wise::Scale;
using QKVElementOp = PassThrough;
using YElementOp = PassThrough;
using DataType = BF16;
using GemmDataType = BF16;
using AccDataType = F32;
using ShuffleDataType = F32;
using LSEDataType = F32;
using ZDataType = U16;
using Acc0BiasDataType = ck::Tuple<>;
using Acc1BiasDataType = ck::Tuple<>;
static constexpr ck::index_t NumDimG = 2;
static constexpr ck::index_t NumDimM = 1;
static constexpr ck::index_t NumDimN = 1;
static constexpr ck::index_t NumDimK = 1;
static constexpr ck::index_t NumDimO = 1;
static constexpr auto GemmSpec = ck::tensor_operation::device::GemmSpecialization::MNKOPadding;
#if USING_MASK
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskOutUpperTriangle;
#else
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
#endif
static constexpr auto TensorSpecQ = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecK = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecV = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecY = ck::tensor_operation::device::TensorSpecialization::Default;
// Headdim/K/O should be a multiple of 8, and it's only supported up to 64 in prototype1.
// If Headdim/K/O <= 32, ues 1st template.
// If 32 < Headdim/K/O <= 64, ues 2nd template.
#if USING_HD32
// 1st template
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
32, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
1, // Gemm1NXdlPerWave
1, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#else
// 2nd template
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
64, // KPerBlock
64, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
2, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: S = alpha * Q * K^T
// fp16 in, fp32 out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<DataType,
DataType,
AccDataType,
AccDataType,
PassThrough,
PassThrough,
Scale>;
// Ref Softmax: P = Softmax(S)
// fp32 in, fp16 out
using ReferenceSoftmaxInstance =
ck::tensor_operation::host::ReferenceSoftmax<AccDataType, DataType, AccDataType>;
// Ref Gemm1: Y = P * V
// fp16 in, fp16 out
using ReferenceGemm1Instance = ck::tensor_operation::host::ReferenceBatchedGemm<DataType,
DataType,
DataType,
AccDataType,
PassThrough,
PassThrough,
PassThrough>;
// Ref Gemm for backward pass
// fp16 in, fp16 out
using ReferenceGemmGradInstance = ck::tensor_operation::host::ReferenceBatchedGemm<DataType,
DataType,
DataType,
AccDataType,
PassThrough,
PassThrough,
Scale>;
// Ref dropout
using ReferenceDropoutInstance =
ck::tensor_operation::host::ReferenceDropout<ushort, DataType, DataType>;
template <typename TensorQ,
typename TensorK,
typename TensorV,
typename TensorS,
typename TensorP,
typename TensorZ,
typename TensorY,
typename TensorLSE = TensorP>
void run_attention_fwd_host(const TensorQ& q_g_m_k,
const TensorK& k_g_n_k,
const TensorV& v_g_n_o,
const float alpha,
TensorS& s_g_m_n,
TensorP& p_g_m_n,
TensorY& y_g_m_o,
TensorLSE& lse_g_m,
TensorP& p_drop_g_m_n,
TensorZ& z_g_m_n,
ushort p_dropout_in_16bits,
float rp_dropout)
{
// S = alpha * Q * K^T
auto k_g_k_n = k_g_n_k.Transpose({0, 2, 1});
auto ref_gemm0 = ReferenceGemm0Instance{};
auto ref_gemm0_invoker = ref_gemm0.MakeInvoker();
auto ref_gemm0_argument = ref_gemm0.MakeArgument(
q_g_m_k, k_g_k_n, s_g_m_n, PassThrough{}, PassThrough{}, Scale{alpha});
ref_gemm0_invoker.Run(ref_gemm0_argument);
// masking
#if USING_MASK
auto N = s_g_m_n.GetLengths()[2];
const auto mask = DeviceGemmInstance::C0MatrixMask(N);
s_g_m_n.ForEach([&](auto& self, auto idx) {
if(mask.IsMaskedElement(idx[1], idx[2]))
self(idx) = -ck::NumericLimits<float>::Infinity();
});
#endif
// P = Softmax(S)
auto ref_softmax = ReferenceSoftmaxInstance{};
auto ref_softmax_invoker = ref_softmax.MakeInvoker();
auto ref_softmax_argument = ref_softmax.MakeArgument(s_g_m_n, p_g_m_n, 1, 0, {2}, &lse_g_m);
ref_softmax_invoker.Run(ref_softmax_argument);
// P_dropped
auto ref_dropout = ReferenceDropoutInstance{};
auto ref_dropout_invoker = ref_dropout.MakeInvoker();
auto ref_dropout_argment =
ref_dropout.MakeArgument(z_g_m_n, p_g_m_n, p_drop_g_m_n, p_dropout_in_16bits, rp_dropout);
ref_dropout_invoker.Run(ref_dropout_argment);
// Y = P_dropout * V
auto ref_gemm1 = ReferenceGemm1Instance{};
auto ref_gemm1_invoker = ref_gemm1.MakeInvoker();
auto ref_gemm1_argument = ref_gemm1.MakeArgument(
p_drop_g_m_n, v_g_n_o, y_g_m_o, PassThrough{}, PassThrough{}, PassThrough{});
ref_gemm1_invoker.Run(ref_gemm1_argument);
}
int run(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 2; // method 1 will have slightly higher error; TODO: to investigate
bool time_kernel = true;
// Overall QKV matrices shape
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 1536; // 512
ck::index_t N = 1536; // 512
#if USING_HD32
ck::index_t K = 32; // K/O<=32
ck::index_t O = 32;
#else
ck::index_t K = 64; // 32<K/O<=64
ck::index_t O = 64;
#endif
ck::index_t G0 = 1; // 54
ck::index_t G1 = 1; // 16
float alpha = 1.f / std::sqrt(K);
bool input_permute = false;
bool output_permute = false;
float p_drop = 0.2;
float p_dropout = 1 - p_drop;
uint16_t p_dropout_in_16bits = uint16_t(std::floor(p_dropout * 65535.0));
float rp_dropout = 1.0 / p_dropout;
const unsigned long long seed = 1;
const unsigned long long offset = 0;
if(argc == 1)
{
// use default case
}
else if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else if(argc == 13)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
M = std::stoi(argv[4]);
N = std::stoi(argv[5]);
K = std::stoi(argv[6]);
O = std::stoi(argv[7]);
G0 = std::stoi(argv[8]);
G1 = std::stoi(argv[9]);
alpha = std::stof(argv[10]);
input_permute = std::stoi(argv[11]);
output_permute = std::stoi(argv[12]);
p_drop = std::stoi(argv[13]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=no, 1=yes)\n");
printf("arg4 to 11: M, N, K, O, G0, G1\n");
printf("arg10: scale (alpha)\n");
printf("arg11 to 12: input / output permute\n");
exit(0);
}
std::cout << "do_verification: " << do_verification << std::endl;
std::cout << "init_method: " << init_method << std::endl;
std::cout << "time_kernel: " << time_kernel << std::endl;
std::cout << "M: " << M << std::endl;
std::cout << "N: " << N << std::endl;
std::cout << "K: " << K << std::endl;
std::cout << "O: " << O << std::endl;
std::cout << "G0: " << G0 << std::endl;
std::cout << "G1: " << G1 << std::endl;
std::cout << "alpha: " << alpha << std::endl;
std::cout << "input_permute: " << input_permute << std::endl;
std::cout << "output_permute: " << output_permute << std::endl;
std::cout << "p_drop: " << p_drop << std::endl;
std::cout << "seed: " << seed << std::endl;
std::cout << "offset: " << offset << std::endl;
const ck::index_t BatchCount = G0 * G1;
std::vector<ck::index_t> q_gs_ms_ks_lengths{G0, G1, M, K};
std::vector<ck::index_t> q_gs_ms_ks_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * K, K, G1 * K, 1} // Q layout [G0, M, G1, K]
: std::vector<ck::index_t>{G1 * M * K, M * K, K, 1}; // Q layout [G0, G1, M, K]
std::vector<ck::index_t> k_gs_ns_ks_lengths{G0, G1, N, K};
std::vector<ck::index_t> k_gs_ns_ks_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * K, K, G1 * K, 1} // K layout [G0, N, G1, K]
: std::vector<ck::index_t>{G1 * N * K, N * K, K, 1}; // K layout [G0, G1, N, K]
std::vector<ck::index_t> v_gs_os_ns_lengths{G0, G1, O, N};
std::vector<ck::index_t> v_gs_os_ns_strides =
input_permute
? std::vector<ck::index_t>{N * G1 * O, O, 1, G1 * O} // V layout [G0, N, G1, O]
: std::vector<ck::index_t>{G1 * N * O, N * O, 1, O}; // V layout [G0, G1, N, O]
std::vector<ck::index_t> y_gs_ms_os_lengths{G0, G1, M, O};
std::vector<ck::index_t> y_gs_ms_os_strides =
output_permute
? std::vector<ck::index_t>{M * G1 * O, O, G1 * O, 1} // Y layout [G0, M, G1, O]
: std::vector<ck::index_t>{G1 * M * O, M * O, O, 1}; // Y layout [G0, G1, M, O]
std::vector<ck::index_t> z_gs_ms_ns_lengths{G0, G1, M, N};
std::vector<ck::index_t> z_gs_ms_ns_strides =
input_permute
? std::vector<ck::index_t>{M * G1 * N, N, G1 * N, 1} // Z layout [G0, M, G1, N]
: std::vector<ck::index_t>{G1 * M * N, M * N, N, 1}; // Z layout [G0, G1, M, N]
// The softmax stat log-sum-exp (LSE) is used to speed up softmax calculation in backward pass
// Pi = exp(Si) / sum(exp(S0) + exp(S1) + ...)
// = exp(Si) / exp(log(sum(exp() + ...)))
// = exp(Si - log(sum(exp() + ...)))
// ^^^^^^^^^^^^^^^^^^^^^
// LSE
std::vector<ck::index_t> lse_gs_ms_lengths{G0, G1, M};
std::vector<ck::index_t> lse_gs_ms_strides{G1 * M, M, 1}; // LSE layout [G0, G1, M]
Tensor<DataType> q_gs_ms_ks(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<DataType> k_gs_ns_ks(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<ZDataType> z_gs_ms_ns(z_gs_ms_ns_lengths, z_gs_ms_ns_strides);
Tensor<DataType> v_gs_os_ns(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<DataType> y_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<DataType> ygrad_gs_ms_os(y_gs_ms_os_lengths, y_gs_ms_os_strides);
Tensor<LSEDataType> lse_gs_ms(lse_gs_ms_lengths, lse_gs_ms_strides);
std::cout << "q_gs_ms_ks: " << q_gs_ms_ks.mDesc << std::endl;
std::cout << "k_gs_ns_ks: " << k_gs_ns_ks.mDesc << std::endl;
std::cout << "z_gs_ms_ns: " << z_gs_ms_ns.mDesc << std::endl;
std::cout << "v_gs_os_ns: " << v_gs_os_ns.mDesc << std::endl;
std::cout << "y_gs_ms_os: " << y_gs_ms_os.mDesc << std::endl;
std::cout << "lse_gs_ms_os: " << lse_gs_ms.mDesc << std::endl;
z_gs_ms_ns.GenerateTensorValue(GeneratorTensor_1<DataType>{0});
switch(init_method)
{
case 0: break;
case 1:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<DataType>{-2, 2});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_2<DataType>{-2, 2});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_2<DataType>{-2, 2});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_2<DataType>{-2, 2});
break;
case 2:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
break;
case 3:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
break;
case 4:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
break;
case 5:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<2>{}); // dy[g0, g1, m, o]
// dO dot O = [0; 1; 2; ...]
break;
case 6:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_Sequential<3>{}); // dy[g0, g1, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
// dP = dO V = [0, 1, 2, ...; 0, 1, 2, ...; ...]
// dO dot O = [127.5; ...]
// dS = P * (dP - dO dot O)
//
break;
default:
q_gs_ms_ks.GenerateTensorValue(GeneratorTensor_1<DataType>{1});
k_gs_ns_ks.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
v_gs_os_ns.GenerateTensorValue(GeneratorTensor_Diagonal<DataType>{});
ygrad_gs_ms_os.GenerateTensorValue(GeneratorTensor_1<DataType>{1}); // dy[g0, g1, m, o]
// assume mnko = 256
// P = softmax(QK) = 0.0039 * ones
// O = P V = 0.0039 * ones
// dP = dO V = ones
// dS = P * (dP - (dO dot O))
// = 0.0039 * ones * (ones - 0.0039*256)
// = 0.0039 * ones * (ones - 1)
// = 0
}
// calculate y & log-sum-exp beforehand
Tensor<DataType> q_g_m_k({BatchCount, M, K});
Tensor<DataType> k_g_n_k({BatchCount, N, K});
Tensor<ZDataType> z_g_m_n({BatchCount, M, N});
Tensor<DataType> v_g_n_o({BatchCount, N, O});
Tensor<AccDataType> s_g_m_n({BatchCount, M, N});
Tensor<DataType> p_g_m_n({BatchCount, M, N});
Tensor<DataType> p_drop_g_m_n({BatchCount, M, N});
Tensor<DataType> y_g_m_o({BatchCount, M, O});
Tensor<LSEDataType> lse_g_m({BatchCount, M});
q_gs_ms_ks.ForEach(
[&](auto& self, auto idx) { q_g_m_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
k_gs_ns_ks.ForEach(
[&](auto& self, auto idx) { k_g_n_k(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
v_gs_os_ns.ForEach(
[&](auto& self, auto idx) { v_g_n_o(idx[0] * G1 + idx[1], idx[3], idx[2]) = self(idx); });
// qkv gradients have the same descriptor as with qkv
DeviceMem q_device_buf(sizeof(DataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem k_device_buf(sizeof(DataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem z_device_buf(sizeof(ZDataType) * z_gs_ms_ns.mDesc.GetElementSpaceSize());
DeviceMem v_device_buf(sizeof(DataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem y_device_buf(sizeof(DataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
DeviceMem lse_device_buf(sizeof(LSEDataType) * lse_gs_ms.mDesc.GetElementSpaceSize());
DeviceMem qgrad_device_buf(sizeof(DataType) * q_gs_ms_ks.mDesc.GetElementSpaceSize());
DeviceMem kgrad_device_buf(sizeof(DataType) * k_gs_ns_ks.mDesc.GetElementSpaceSize());
DeviceMem vgrad_device_buf(sizeof(DataType) * v_gs_os_ns.mDesc.GetElementSpaceSize());
DeviceMem ygrad_device_buf(sizeof(DataType) * y_gs_ms_os.mDesc.GetElementSpaceSize());
q_device_buf.ToDevice(q_gs_ms_ks.mData.data());
k_device_buf.ToDevice(k_gs_ns_ks.mData.data());
z_device_buf.ToDevice(z_gs_ms_ns.mData.data());
v_device_buf.ToDevice(v_gs_os_ns.mData.data());
ygrad_device_buf.ToDevice(ygrad_gs_ms_os.mData.data());
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// get z matrix
{
auto argument = gemm.MakeArgument(
static_cast<DataType*>(q_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(k_device_buf.GetDeviceBuffer()),
static_cast<ZDataType*>(z_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(v_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(y_device_buf.GetDeviceBuffer()),
static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(ygrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(qgrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(kgrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(vgrad_device_buf.GetDeviceBuffer()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
k_gs_ns_ks_strides,
z_gs_ms_ns_lengths,
z_gs_ms_ns_strides,
v_gs_os_ns_lengths,
v_gs_os_ns_strides,
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
QKVElementOp{},
QKVElementOp{},
Scale{alpha},
QKVElementOp{},
YElementOp{},
p_drop,
std::tuple<unsigned long long, unsigned long long>(seed, offset));
if(!gemm.IsSupportedArgument(argument))
{
std::cout << gemm.GetTypeString() << " does not support this problem" << std::endl;
return 0;
}
invoker.Run(argument, StreamConfig{nullptr, false});
}
// not need output z matrix
auto argument = gemm.MakeArgument(
static_cast<DataType*>(q_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(k_device_buf.GetDeviceBuffer()),
static_cast<ZDataType*>(nullptr), // set to nullptr
static_cast<DataType*>(v_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(y_device_buf.GetDeviceBuffer()),
static_cast<LSEDataType*>(lse_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(ygrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(qgrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(kgrad_device_buf.GetDeviceBuffer()),
static_cast<DataType*>(vgrad_device_buf.GetDeviceBuffer()),
{}, // std::array<void*, 1> p_acc0_biases;
{}, // std::array<void*, 1> p_acc1_biases;
q_gs_ms_ks_lengths,
q_gs_ms_ks_strides,
k_gs_ns_ks_lengths,
k_gs_ns_ks_strides,
z_gs_ms_ns_lengths,
z_gs_ms_ns_strides,
v_gs_os_ns_lengths,
v_gs_os_ns_strides,
y_gs_ms_os_lengths,
y_gs_ms_os_strides,
lse_gs_ms_lengths,
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc0_biases_gs_ms_ns_strides},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_lengths},
{}, // std::array<std::vector<ck::index_t>, 1>{acc1_biases_gs_ms_os_strides},
QKVElementOp{},
QKVElementOp{},
Scale{alpha},
QKVElementOp{},
YElementOp{},
p_drop,
std::tuple<unsigned long long, unsigned long long>(seed, offset));
kgrad_device_buf.SetZero(); // reset global accum buffer and rerun
vgrad_device_buf.SetZero();
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
// 5 GEMM ops in total:
// S_MNK / dP_MNO Gemm (Gemm0 rcr)
// dQ_MKN Gemm (Gemm1 rrr)
// dV_NOM / dK_NKM Gemm (Gemm2 crr)
// 3x MNK + 2x MNO
std::size_t flop = (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
// Q/K/V/Y, dQ/dK/dV/dY, LSE
std::size_t num_btype = (sizeof(DataType) * M * K + sizeof(DataType) * K * N +
sizeof(DataType) * N * O + sizeof(DataType) * M * O) *
size_t(2) * BatchCount +
sizeof(LSEDataType) * M * BatchCount;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
// copy z matirx data form device
z_device_buf.FromDevice(z_gs_ms_ns.mData.data());
z_gs_ms_ns.ForEach(
[&](auto& self, auto idx) { z_g_m_n(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx); });
// std::cout << "z_g_m_n ref:\n" << z_g_m_n;
bool pass = true;
if(do_verification)
{
// run fwd again for y, cause z_g_m_n update
run_attention_fwd_host(q_g_m_k,
k_g_n_k,
v_g_n_o,
alpha,
s_g_m_n,
p_g_m_n,
y_g_m_o,
lse_g_m,
p_drop_g_m_n,
z_g_m_n,
p_dropout_in_16bits,
rp_dropout);
y_gs_ms_os.ForEach([&](auto& self, auto idx) {
self(idx) = y_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]);
});
lse_gs_ms.ForEach(
[&](auto& self, auto idx) { self(idx) = lse_g_m(idx[0] * G1 + idx[1], idx[2]); });
y_device_buf.ToDevice(y_gs_ms_os.mData.data());
lse_device_buf.ToDevice(lse_gs_ms.mData.data());
// call kernel again
kgrad_device_buf.SetZero(); // reset global accum buffer and rerun
vgrad_device_buf.SetZero();
invoker.Run(argument, StreamConfig{nullptr, false});
Tensor<DataType> qgrad_g_m_k({BatchCount, M, K});
Tensor<DataType> kgrad_g_n_k({BatchCount, N, K});
Tensor<DataType> vgrad_g_n_o({BatchCount, N, O});
Tensor<DataType> sgrad_g_m_n({BatchCount, M, N});
Tensor<DataType> pgrad_g_m_n({BatchCount, M, N});
Tensor<DataType> pgrad_drop_g_m_n({BatchCount, M, N});
Tensor<DataType> ygrad_g_m_o({BatchCount, M, O});
Tensor<DataType> ygrad_dot_y_g_m({BatchCount, M});
ygrad_gs_ms_os.ForEach([&](auto& self, auto idx) {
ygrad_g_m_o(idx[0] * G1 + idx[1], idx[2], idx[3]) = self(idx);
});
#if PRINT_HOST
{
std::cout << "q_g_m_k ref:\n" << q_g_m_k;
std::cout << "k_g_n_k ref:\n" << k_g_n_k;
std::cout << "v_g_n_o ref:\n" << v_g_n_o;
std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
}
#endif
// Gradients
auto ref_gemm_grad = ReferenceGemmGradInstance{};
auto ref_gemm_grad_invoker = ref_gemm_grad.MakeInvoker();
using RefGemmGradArg = ReferenceGemmGradInstance::Argument;
// dP_dropout = dY * V^T
auto v_g_o_n = v_g_n_o.Transpose({0, 2, 1});
ref_gemm_grad_invoker.Run(RefGemmGradArg{
ygrad_g_m_o, v_g_o_n, pgrad_drop_g_m_n, PassThrough{}, PassThrough{}, Scale{1.f}});
#if PRINT_HOST
{
std::cout << "===== dP = dY * V^T\n";
std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
std::cout << "v_g_o_n ref:\n" << v_g_o_n;
std::cout << "pgrad_drop_g_m_n ref:\n" << pgrad_drop_g_m_n;
}
#endif
// dP = dP_dropout x Z
auto ref_dropout = ReferenceDropoutInstance{};
auto ref_dropout_invoker = ref_dropout.MakeInvoker();
auto ref_dropout_argment = ref_dropout.MakeArgument(
z_g_m_n, pgrad_drop_g_m_n, pgrad_g_m_n, p_dropout_in_16bits, rp_dropout);
ref_dropout_invoker.Run(ref_dropout_argment);
// dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)
sgrad_g_m_n.ForEach([&](auto& self, auto idx_gmn) {
float ygrad_dot_y = 0;
for(int o = 0; o < O; o++)
{
auto idx_gmo = idx_gmn;
idx_gmo[2] = o;
ygrad_dot_y += ck::type_convert<AccDataType>(ygrad_g_m_o(idx_gmo)) *
ck::type_convert<AccDataType>(y_g_m_o(idx_gmo));
}
self(idx_gmn) = ck::type_convert<DataType>(
ck::type_convert<AccDataType>(p_g_m_n(idx_gmn)) *
(ck::type_convert<AccDataType>(pgrad_g_m_n(idx_gmn)) - ygrad_dot_y));
});
#if PRINT_HOST
{
std::cout << "===== dS_i_j = P_i_j .* (dP_i_j - dY_i dot Y_i)\n";
std::cout << "p_g_m_n ref:\n" << p_g_m_n;
std::cout << "pgrad_g_m_n ref:\n" << pgrad_g_m_n;
std::cout << "y_g_m_o ref:\n" << y_g_m_o;
std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
}
#endif
// dV = P_drop^T * dY
auto p_drop_g_n_m = p_drop_g_m_n.Transpose({0, 2, 1});
ref_gemm_grad_invoker.Run(RefGemmGradArg{
p_drop_g_n_m, ygrad_g_m_o, vgrad_g_n_o, PassThrough{}, PassThrough{}, Scale{1.0f}});
#if PRINT_HOST
{
std::cout << "===== dV = P^T * dY\n";
std::cout << "p_drop_g_n_m ref:\n" << p_drop_g_n_m;
std::cout << "ygrad_g_m_o ref:\n" << ygrad_g_m_o;
std::cout << "vgrad_g_n_o ref:\n" << vgrad_g_n_o;
}
#endif
// dQ = alpha * dS * K
ref_gemm_grad_invoker.Run(RefGemmGradArg{
sgrad_g_m_n, k_g_n_k, qgrad_g_m_k, PassThrough{}, PassThrough{}, Scale{alpha}});
#if PRINT_HOST
{
std::cout << "===== dQ = alpha * dS * K\n";
std::cout << "sgrad_g_m_n ref:\n" << sgrad_g_m_n;
std::cout << "k_g_n_k ref:\n" << k_g_n_k;
std::cout << "qgrad_g_m_k ref:\n" << qgrad_g_m_k;
}
#endif
// dK = alpha * dS^T * Q
auto sgrad_g_n_m = sgrad_g_m_n.Transpose({0, 2, 1});
ref_gemm_grad_invoker.Run(RefGemmGradArg{
sgrad_g_n_m, q_g_m_k, kgrad_g_n_k, PassThrough{}, PassThrough{}, Scale{alpha}});
#if PRINT_HOST
{
std::cout << "===== dK = alpha * dS^T * Q\n";
std::cout << "sgrad_g_n_m ref:\n" << sgrad_g_n_m;
std::cout << "q_g_m_k ref:\n" << q_g_m_k;
std::cout << "kgrad_g_n_k ref:\n" << kgrad_g_n_k;
}
#endif
Tensor<DataType> qgrad_gs_ms_ks_host_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<DataType> kgrad_gs_ns_ks_host_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<DataType> vgrad_gs_os_ns_host_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
Tensor<DataType> qgrad_gs_ms_ks_device_result(q_gs_ms_ks_lengths, q_gs_ms_ks_strides);
Tensor<DataType> kgrad_gs_ns_ks_device_result(k_gs_ns_ks_lengths, k_gs_ns_ks_strides);
Tensor<DataType> vgrad_gs_os_ns_device_result(v_gs_os_ns_lengths, v_gs_os_ns_strides);
qgrad_device_buf.FromDevice(qgrad_gs_ms_ks_device_result.mData.data());
kgrad_device_buf.FromDevice(kgrad_gs_ns_ks_device_result.mData.data());
vgrad_device_buf.FromDevice(vgrad_gs_os_ns_device_result.mData.data());
// permute
qgrad_gs_ms_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t g = g0 * G1 + g1;
self(idx) = qgrad_g_m_k(g, idx[2], idx[3]);
});
kgrad_gs_ns_ks_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t g = g0 * G1 + g1;
self(idx) = kgrad_g_n_k(g, idx[2], idx[3]);
});
vgrad_gs_os_ns_host_result.ForEach([&](auto& self, auto idx) {
const size_t& g0 = idx[0];
const size_t& g1 = idx[1];
const size_t g = g0 * G1 + g1;
self(idx) = vgrad_g_n_o(g, idx[3], idx[2]);
});
std::cout << "Checking qgrad:\n";
pass &= ck::utils::check_err(qgrad_gs_ms_ks_device_result.mData,
qgrad_gs_ms_ks_host_result.mData,
"error",
1e-2,
1e-2);
std::cout << "Checking kgrad:\n";
pass &= ck::utils::check_err(kgrad_gs_ns_ks_device_result.mData,
kgrad_gs_ns_ks_host_result.mData,
"error",
1e-2,
1e-2);
std::cout << "Checking vgrad:\n";
pass &= ck::utils::check_err(vgrad_gs_os_ns_device_result.mData,
vgrad_gs_os_ns_host_result.mData,
"error",
1e-2,
1e-2);
}
return pass ? ((void)(std::cout << "pass\n"), 0) : ((void)(std::cout << "fail\n"), 1);
}
int main(int argc, char* argv[]) { return run(argc, argv); }
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_forward.cpp
View file @ 62357796
......@@ -9,6 +9,8 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g
Gemm1
*/
#define RANGE_HDKO 0 // 0~2
#include <iostream>
#include <numeric>
#include <initializer_list>
......@@ -73,6 +75,77 @@ static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecial
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(RANGE_HDKO == 0)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
32, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
1, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
2,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 1)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
......@@ -142,6 +215,77 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 2)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
......
example/32_batched_gemm_scale_softmax_gemm/batched_multihead_attention_train.cpp
View file @ 62357796
......@@ -32,7 +32,7 @@ Kernel outputs:
#define PRINT_HOST 0
#define USING_MASK 0
#define USING_HD32 0
#define RANGE_HDKO 0 // 0~2
#include <iostream>
#include <numeric>
......@@ -43,8 +43,9 @@ Kernel outputs:
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/tensor_specialization.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_forward_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle_pt1.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_backward_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batched_multihead_attention_forward_xdl_cshuffle.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
......@@ -99,6 +100,11 @@ static constexpr auto TensorSpecK = ck::tensor_operation::device::TensorSpeciali
static constexpr auto TensorSpecV = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecY = ck::tensor_operation::device::TensorSpecialization::Default;
// Headdim/K/O should be a multiple of 8.
// If Headdim/K/O <= 32 , ues bwd prototype1 1st template.
// If 32 < Headdim/K/O <= 64 , ues bwd prototype1 2nd template.
// If 64 < Headdim/K/O <= 128, ues bwd prototype2 2nd template.
#if(RANGE_HDKO == 0)
using DeviceGemmInstanceFWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
......@@ -132,7 +138,7 @@ using DeviceGemmInstanceFWD =
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
64, // Gemm1NPerBlock
32, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
......@@ -141,7 +147,7 @@ using DeviceGemmInstanceFWD =
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
1, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
......@@ -160,21 +166,15 @@ using DeviceGemmInstanceFWD =
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
1, // CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// Headdim/K/O should be a multiple of 8, and it's only supported up to 64 in prototype1.
// If Headdim/K/O <= 32, ues 1st template.
// If 32 < Headdim/K/O <= 64, ues 2nd template.
#if USING_HD32
// 1st template
using DeviceGemmInstanceBWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
......@@ -242,8 +242,77 @@ using DeviceGemmInstanceBWD =
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#else
// 2nd template
#elif(RANGE_HDKO == 1)
using DeviceGemmInstanceFWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
DataType,
DataType,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
64, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
2, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
using DeviceGemmInstanceBWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle_PT1<
NumDimG,
......@@ -310,6 +379,211 @@ using DeviceGemmInstanceBWD =
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
// using DeviceGemmInstanceBWD =
// ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle<
// NumDimG,
// NumDimM,
// NumDimN,
// NumDimK,
// NumDimO,
// DataType,
// GemmDataType,
// ZDataType,
// LSEDataType,
// Acc0BiasDataType,
// Acc1BiasDataType,
// AccDataType,
// ShuffleDataType,
// QKVElementOp,
// QKVElementOp,
// Scale,
// QKVElementOp,
// YElementOp,
// GemmSpec,
// TensorSpecQ,
// TensorSpecK,
// TensorSpecV,
// TensorSpecY,
// 1,
// 256,
// 128, // MPerBlock
// 128, // NPerBlock
// 64, // KPerBlock
// 64, // Gemm1NPerBlock
// 64, // Gemm1KPerBlock
// 8, // AK1
// 8, // BK1
// 2, // B1K1
// 32, // MPerXDL
// 32, // NPerXDL
// 1, // MXdlPerWave
// 4, // NXdlPerWave
// 2, // Gemm1NXdlPerWave
// 2, // Gemm2NXdlPerWave
// S<4, 64, 1>, // ABlockTransfer
// S<1, 0, 2>,
// S<1, 0, 2>,
// 2,
// 8,
// 8,
// true,
// S<4, 64, 1>, // BBlockTransfer
// S<1, 0, 2>,
// S<1, 0, 2>,
// 2,
// 8,
// 8,
// true,
// S<8, 32, 1>, // B1BlockTransfer
// S<0, 2, 1>,
// S<0, 2, 1>,
// 1,
// 2,
// 2,
// false,
// 1, // CShuffleMXdlPerWavePerShuffle
// 2, // CShuffleNXdlPerWavePerShuffle
// S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
// 8, // CShuffleBlockTransferScalarPerVector_NPerBlock
// MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 2)
using DeviceGemmInstanceFWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
DataType,
DataType,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
using DeviceGemmInstanceBWD =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
DataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
ShuffleDataType,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
GemmSpec,
TensorSpecQ,
TensorSpecK,
TensorSpecV,
TensorSpecY,
1,
256,
128, // MPerBlock
128, // NPerBlock
64, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
2, // Gemm2NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
4, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#endif
......@@ -424,10 +698,16 @@ int run(int argc, char* argv[])
// y_g_m_o = Softmax(alpha * Q_g_m_k * K_g_k_n) * V_g_n_o
// y_g0_g1_m_o = reshape(y_g_m_o, [G0, G1, M, O])
// y_g0_m_g1_o = permute(y_g0_g1_m_o, [0, 2, 1, 3])
ck::index_t M = 129; // 512
ck::index_t N = 129; // 512
ck::index_t K = 64;
ck::index_t O = 64;
ck::index_t M = 512; // 512
ck::index_t N = 512; // 512
#if(RANGE_HDKO == 0)
ck::index_t K = 32; // K/O<=32
#elif(RANGE_HDKO == 1)
ck::index_t K = 64; // 32<K/O<=64
#elif(RANGE_HDKO == 2)
ck::index_t K = 72; // 64<K/O<=128
#endif
ck::index_t O = K;
ck::index_t G0 = 4; // 54
ck::index_t G1 = 6; // 16
......@@ -436,7 +716,7 @@ int run(int argc, char* argv[])
bool input_permute = true;
bool output_permute = true;
float p_drop = 0.0;
float p_drop = 0.3;
float p_dropout = 1 - p_drop;
uint16_t p_dropout_in_16bits = uint16_t(std::floor(p_dropout * 65535.0));
float rp_dropout = 1.0 / p_dropout;
......
example/32_batched_gemm_scale_softmax_gemm/grouped_multihead_attention_forward.cpp
View file @ 62357796
......@@ -9,6 +9,8 @@ Gemm + Softmax + Gemm fused operation. Computes C_g_m_o = Softmax(A_g_m_k * B0_g
Gemm1
*/
#define RANGE_HDKO 0 // 0~2
#include <iostream>
#include <numeric>
#include <initializer_list>
......@@ -73,6 +75,77 @@ static constexpr auto TensorSpecB0 = ck::tensor_operation::device::TensorSpecial
static constexpr auto TensorSpecB1 = ck::tensor_operation::device::TensorSpecialization::Default;
static constexpr auto TensorSpecC = ck::tensor_operation::device::TensorSpecialization::Default;
#if(RANGE_HDKO == 0)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
32, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
1, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<16, 16, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
2,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 64, 1, 4>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 1)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
......@@ -142,6 +215,77 @@ using DeviceGemmInstance =
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#elif(RANGE_HDKO == 2)
using DeviceGemmInstance =
ck::tensor_operation::device::DeviceGroupedMultiheadAttentionForward_Xdl_CShuffle<
NumDimG,
NumDimM,
NumDimN,
NumDimK,
NumDimO,
ADataType,
B0DataType,
B1DataType,
CDataType,
GemmDataType,
ZDataType,
LSEDataType,
Acc0BiasDataType,
Acc1BiasDataType,
AccDataType,
CShuffleDataType,
AElementOp,
B0ElementOp,
Acc0ElementOp,
B1ElementOp,
CElementOp,
GemmSpec,
TensorSpecA,
TensorSpecB0,
TensorSpecB1,
TensorSpecC,
1,
256,
128, // MPerBlock
128, // NPerBlock
32, // KPerBlock
128, // Gemm1NPerBlock
32, // Gemm1KPerBlock
8, // AK1
8, // BK1
2, // B1K1
32, // MPerXDL
32, // NPerXDL
1, // MXdlPerWave
4, // NXdlPerWave
4, // Gemm1NXdlPerWave
S<4, 64, 1>, // ABlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<4, 64, 1>, // BBlockTransfer
S<1, 0, 2>,
S<1, 0, 2>,
2,
8,
8,
true,
S<8, 32, 1>, // B1BlockTransfer
S<0, 2, 1>,
S<0, 2, 1>,
1,
4,
2,
false,
1, // CShuffleMXdlPerWavePerShuffle
2, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
8, // CShuffleBlockTransferScalarPerVector_NPerBlock
MaskingSpec>; // MaskingSpecialization
#endif
// Ref Gemm0: DataType in, AccDataType out
using ReferenceGemm0Instance = ck::tensor_operation::host::ReferenceBatchedGemm<ADataType,
......
example/32_batched_gemm_scale_softmax_gemm/run_batched_multihead_attention_forward.inc
View file @ 62357796
......@@ -11,8 +11,14 @@ int run(int argc, char* argv[])
// C_g_m_o = A_g_m_k * B0_g_k_n * B1_g_n_o
ck::index_t M = 1000; // 120
ck::index_t N = 1000; // 1000
ck::index_t K = 64;
ck::index_t O = 64;
#if(RANGE_HDKO == 0)
ck::index_t K = 32; // K/O<=32
#elif(RANGE_HDKO == 1)
ck::index_t K = 64; // 32<K/O<=64
#elif(RANGE_HDKO == 2)
ck::index_t K = 72; // 64<K/O<=128
#endif
ck::index_t O = K;
// Output shape C[G0, M, G1, O]. Batch dim, outer dim, inner dim must match GEMM shape
// C_g0_g1_m_o = reshape(C_g_m_o, [g0, g1, m, o])
......
example/32_batched_gemm_scale_softmax_gemm/run_grouped_multihead_attention_forward.inc
View file @ 62357796
......@@ -83,8 +83,14 @@ int run(int argc, char* argv[])
int M = 128 * (rand() % 8 + 1);
int N = 128 * (rand() % 8 + 1);
int K = 64;
int O = 64;
#if(RANGE_HDKO == 0)
int K = 32; // K/O<=32
#elif(RANGE_HDKO == 1)
int K = 64; // 32<K/O<=64
#elif(RANGE_HDKO == 2)
int K = 72; // 64<K/O<=128
#endif
int O = K;
int G0 = rand() % 3 + 1;
int G1 = rand() % 5 + 1;
......
include/ck/tensor_operation/gpu/grid/gridwise_batched_multihead_attention_forward_xdl_cshuffle.hpp
View file @ 62357796
......@@ -274,11 +274,11 @@ struct GridwiseBatchedMultiheadAttentionForward_Xdl_CShuffle
const auto K = a_grid_desc_ak0_m_ak1.GetLength(I0) * a_grid_desc_ak0_m_ak1.GetLength(I2);
const auto Gemm1N = b1_grid_desc_bk0_n_bk1.GetLength(I1);
if(Gemm1N != K)
{
std::cout << "SizeK must be equal to SizeO (equal attention head size)" << '\n';
return false;
}
// if(Gemm1N != K)
// {
// std::cout << "SizeK must be equal to SizeO (equal attention head size)" << '\n';
// return false;
// }
if(!(M == c_grid_desc_m_n.GetLength(I0) && Gemm1N == c_grid_desc_m_n.GetLength(I1)))
{
......
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