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Commit 0bdfc73e authored by guangzlu's avatar guangzlu
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fixed bugs

parent ed26ceeb
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Showing with 14 additions and 342 deletions
client_example/08_fused_attention/CMakeLists.txt
View file @ 0bdfc73e
add_executable(client_fused_attention fused_attention.cpp) target_link_libraries(
client_fused_attention PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention fused_attention.cpp)
target_link_libraries(client_fused_attention PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bias fused_attention_bias.cpp) target_link_libraries(
client_fused_attention_bias PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bias fused_attention_bias.cpp)
target_link_libraries(client_fused_attention_bias PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bwd fused_attention_bwd.cpp) target_link_libraries(
client_fused_attention_bwd PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bwd_qloop_v1 fused_attention_bwd_qloop_v1.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_v1 PRIVATE composable_kernel::device_operations)
add_executable(
client_fused_attention_bwd_qloop_light_v1 fused_attention_bwd_qloop_light_v1.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_light_v1 PRIVATE
composable_kernel::device_operations)
add_executable(client_fused_attention_bwd_qloop_v2 fused_attention_bwd_qloop_v2.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_v2 PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bwd_qloop_light_v2
fused_attention_bwd_qloop_light_v2.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_light_v2 PRIVATE
composable_kernel::device_operations)
add_executable(client_fused_attention_bwd_qloop_light_v1 fused_attention_bwd_qloop_light_v1.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_light_v1 PRIVATE composable_kernel::device_operations)
add_executable(client_fused_attention_bwd_qloop_light_v2 fused_attention_bwd_qloop_light_v2.cpp)
target_link_libraries(client_fused_attention_bwd_qloop_light_v2 PRIVATE composable_kernel::device_operations)
client_example/08_fused_attention/fused_attention_bwd.cpp deleted 100644 → 0
View file @ ed26ceeb
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#define USING_MASK 0
#define DIM 64 // DIM should be a multiple of 8.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include <fstream>
#include "ck/ck.hpp"
#include "ck/library/tensor_operation_instance/gpu/batched_mha_bwd_qloop.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.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 INT32 = int32_t;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Scale = ck::tensor_operation::element_wise::Scale;
using QKVElementOp = PassThrough;
using YElementOp = PassThrough;
using InputDataType = F16;
using OutputDataType = F16;
using GemmDataType = F16;
using AccDataType = F32;
using ShuffleDataType = F32;
using LSEDataType = F32;
using ZDataType = U16; // INT32
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 ck::index_t CShuffleBlockTransferScalarPerVector_NPerBlock = 8;
#if USING_MASK
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskUpperTriangleFromTopLeft;
#else
static constexpr auto MaskingSpec =
ck::tensor_operation::device::MaskingSpecialization::MaskDisabled;
#endif
struct SimpleDeviceMem
{
SimpleDeviceMem() = delete;
SimpleDeviceMem(std::size_t mem_size) : p_mem_{}
{
(void)hipMalloc(static_cast<void**>(&p_mem_), mem_size);
}
void* GetDeviceBuffer() { return p_mem_; }
~SimpleDeviceMem() { (void)hipFree(p_mem_); }
void* p_mem_;
};
int main(int argc, char* argv[])
{
int init_method = 1;
ck::index_t M = 512;
ck::index_t N = 512;
ck::index_t K = DIM;
ck::index_t O = DIM;
ck::index_t G0 = 4;
ck::index_t G1 = 6;
bool input_permute = false;
bool output_permute = false;
float p_drop = 0.0;
const unsigned long long seed = 1;
const unsigned long long offset = 0;
float p_dropout = 1 - p_drop;
ZDataType p_dropout_in_16bits = ZDataType(std::floor(p_dropout * 65535.0));
float rp_dropout = 1.0 / p_dropout;
float alpha = 1.f / std::sqrt(K);
std::cout << "init_method: " << init_method << 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]
SimpleDeviceMem q_device_buf(sizeof(InputDataType) * G0 * G1 * M * K);
SimpleDeviceMem k_device_buf(sizeof(InputDataType) * G0 * G1 * N * K);
SimpleDeviceMem z_device_buf(sizeof(ZDataType) * G0 * G1 * M * N);
SimpleDeviceMem v_device_buf(sizeof(InputDataType) * G0 * G1 * O * N);
SimpleDeviceMem y_device_buf(sizeof(InputDataType) * G0 * G1 * M * O);
SimpleDeviceMem lse_device_buf(sizeof(LSEDataType) * G0 * G1 * M);
SimpleDeviceMem qgrad_device_buf(sizeof(OutputDataType) * G0 * G1 * M * K);
SimpleDeviceMem kgrad_device_buf(sizeof(OutputDataType) * G0 * G1 * N * K);
SimpleDeviceMem vgrad_device_buf(sizeof(OutputDataType) * G0 * G1 * O * N);
SimpleDeviceMem ygrad_device_buf(sizeof(InputDataType) * G0 * G1 * M * O);
using DeviceOp =
ck::tensor_operation::device::DeviceBatchedMultiheadAttentionBackward<2,
1,
1,
1,
1,
InputDataType,
OutputDataType,
ZDataType,
LSEDataType,
ck::Tuple<>,
ck::Tuple<>,
QKVElementOp,
QKVElementOp,
Scale,
QKVElementOp,
YElementOp,
MaskingSpec>;
// get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
std::string best_op_name;
int best_op_id = -1;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
// profile device op instances
std::cout << "Run all instances and do timing" << std::endl;
for(int i = 0; i < op_ptrs.size(); ++i)
{
auto& op_ptr = op_ptrs[i];
auto argument_ptr = op_ptr->MakeArgumentPointer(
q_device_buf.GetDeviceBuffer(),
k_device_buf.GetDeviceBuffer(),
nullptr, // set to nullptr
v_device_buf.GetDeviceBuffer(),
y_device_buf.GetDeviceBuffer(),
lse_device_buf.GetDeviceBuffer(),
ygrad_device_buf.GetDeviceBuffer(),
qgrad_device_buf.GetDeviceBuffer(),
kgrad_device_buf.GetDeviceBuffer(),
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));
auto invoker_ptr = op_ptr->MakeInvokerPointer();
std::string op_name = op_ptr->GetTypeString();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
float ave_time = invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, true});
std::size_t flop = (size_t(3) * M * N * K + size_t(2) * M * N * O) * 2 * BatchCount;
std::size_t num_btype =
(sizeof(InputDataType) * M * K + sizeof(InputDataType) * K * N +
sizeof(InputDataType) * N * O + sizeof(InputDataType) * M * O * size_t(2) +
sizeof(OutputDataType) * M * K + sizeof(OutputDataType) * K * N +
sizeof(OutputDataType) * N * O) *
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, " << op_name << std::endl;
if(tflops > best_tflops)
{
best_op_id = i;
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
}
else
{
std::cout << op_name << " does not support this problem" << std::endl;
}
}
// run the best instance
{
auto& op_ptr = op_ptrs[best_op_id];
std::cout << "Run the best instance without timing: " << op_ptr->GetTypeString()
<< std::endl;
auto argument_ptr = op_ptr->MakeArgumentPointer(
q_device_buf.GetDeviceBuffer(),
k_device_buf.GetDeviceBuffer(),
nullptr, // set to nullptr
v_device_buf.GetDeviceBuffer(),
y_device_buf.GetDeviceBuffer(),
lse_device_buf.GetDeviceBuffer(),
ygrad_device_buf.GetDeviceBuffer(),
qgrad_device_buf.GetDeviceBuffer(),
kgrad_device_buf.GetDeviceBuffer(),
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));
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, false});
}
std::cout << "Done" << std::endl;
}
return 0;
}
include/ck/tensor_operation/gpu/device/impl/device_batched_mha_bwd_xdl_cshuffle_qloop_v2.hpp
View file @ 0bdfc73e
......@@ -12,6 +12,7 @@
#include "ck/tensor_description/tensor_descriptor.hpp"
#include "ck/tensor_description/tensor_descriptor_helper.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/device_batched_gemm_softmax_gemm_permute.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/masking_specialization.hpp"
#include "ck/tensor_operation/gpu/device/matrix_padder.hpp"
......
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