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Unverified Commit 989ffa2b authored by Illia Silin's avatar Illia Silin Committed by GitHub
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Merge pull request #263 from ROCm/develop 

Merge from public repo
parents 0dd5d62a cabbacb6
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Showing with 869 additions and 110 deletions
include/ck_tile/ref/naive_attention.hpp 0 → 100644
View file @ 989ffa2b
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include/ck_tile/remod.py
View file @ 989ffa2b
......@@ -7,6 +7,7 @@ import copy
NS = 'ck_tile'
OPS = 'ops'
REF = 'ref'
OPS_COMMON = 'common' # common header will be duplicated into ops/* other module
HEADER_COMMON = f"""// SPDX-License-Identifier: MIT
......@@ -29,6 +30,9 @@ class submodule_t:
def push(self, f):
if len(f.parents) != 1: # ignore ./xxx.hpp
mod = get_module(f)
# ref is supposed to include one header on demand
if mod == REF:
return
if mod == OPS:
if mod not in self.m.keys():
self.m[mod] = dict()
......
library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_bf16_bf16_bf16/device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn.hpp
View file @ 989ffa2b
......@@ -52,6 +52,9 @@ using device_batched_gemm_xdl_universal_bf16_bf16_bf16_mk_nk_mn_comp_instances =
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 256, 256, 32, 8, 8, 32, 32, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 224, 256, 64, 8, 8, 16, 16, 7, 8, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 2, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 256, 224, 64, 8, 8, 16, 16, 8, 7, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 2, 1, S<1, 32, 1, 8>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 256, 160, 64, 8, 8, 16, 16, 8, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 2, 1, S<1, 32, 1, 8>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 160, 64, 8, 8, 32, 32, 1, 5, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 64, 1, 4>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 160, 128, 64, 8, 8, 32, 32, 5, 1, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v3>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v5>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, BF16, BF16, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 128, 128, 64, 8, 8, 32, 32, 2, 2, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, S<8, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 0, 1, 1, S<1, 16, 1, 16>, S<4>, BlockGemmPipelineScheduler::Interwave, BlockGemmPipelineVersion::v1>
......
library/src/tensor_operation_instance/gpu/gemm_universal_batched/device_batched_gemm_xdl_universal_f8_f8_bf16/device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn.hpp
View file @ 989ffa2b
......@@ -42,6 +42,7 @@ using device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_comp_instances = std
//##################################| | | | | Type| Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
//##################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//##################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
#ifdef __gfx94__
// Compute friendly
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, F8, F8, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 256, 256, 256, 64, 16, 16, 32, 32, 4, 4, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 32, 1, 8>, S<8>, BlockGemmPipelineScheduler::Intrawave, BlockGemmPipelineVersion::v4, F8>,
......@@ -72,6 +73,7 @@ using device_batched_gemm_xdl_universal_f8_f8_bf16_mk_nk_mn_mem_instances = std:
//##################################| | | | | Type| Type| Type| Type| Type| Type| Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MXdlPerWave_MWaveMPerXdl| ScalarPerVector| Pipeline| Pipeline|
//##################################| | | | | | | | | | | Operation| Operation| Operation| | | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NXdlPerWave_NWaveNPerXdl| _NWaveNPerXdl| Scheduler| Verision|
//##################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
#if defined(__gfx94__) || defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH)
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, F8, F8, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 128, 32, 16, 128, 16, 16, 16, 16, 1, 1, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 16, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 8>, S<2>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
DeviceBatchedGemmMultiD_Xdl_CShuffle_V3< Row, Col, DsLayout, Row, F8, F8, DsDataType, BF16, F32, BF16, PassThrough, PassThrough, PassThrough, GemmSpec, 64, 16, 16, 128, 16, 16, 16, 16, 1, 1, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, S<8, 8, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 16, 16, 0, 1, 1, S<1, 16, 1, 4>, S<4>, BlkGemmPipeSched, BlockGemmPipelineVersion::v1, F8>,
......
profiler/include/profiler/profile_gemm_universal_batched_impl.hpp
View file @ 989ffa2b
......@@ -48,6 +48,7 @@ bool profile_gemm_universal_batched_impl(int do_verification,
int StrideB,
int StrideC,
int BatchCount,
int KBatch,
int n_warmup,
int n_iter,
uint64_t rotating = 0)
......@@ -147,89 +148,100 @@ bool profile_gemm_universal_batched_impl(int do_verification,
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
float best_kbatch = 0;
// profile device op instances
for(auto& op_ptr : op_ptrs)
{
std::unique_ptr<tensor_operation::device::BaseArgument> argument_ptr;
// false branch for multi d dl kernel
argument_ptr =
op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
{},
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
BatchCount,
StrideA,
StrideB,
{},
StrideC,
BatchStrideA,
BatchStrideB,
{},
BatchStrideC,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{});
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
// re-init C to zero before profiling next kernel
c_device_buf.SetZero();
std::string op_name = op_ptr->GetTypeString();
std::vector<int> kbatch_list = {1, 2, 4, 8, 16, 19, 32, 38};
float ave_time = invoker_ptr->Run(
argument_ptr.get(),
StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter, true, rotating_count});
if(KBatch > 0)
{
kbatch_list = {KBatch};
}
std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
for(std::size_t i = 0; i < kbatch_list.size(); i++)
{
auto kbatch_curr = kbatch_list[i];
auto argument_ptr =
op_ptr->MakeArgumentPointer(static_cast<ADataType*>(a_device_buf.GetDeviceBuffer()),
static_cast<BDataType*>(b_device_buf.GetDeviceBuffer()),
{},
static_cast<CDataType*>(c_device_buf.GetDeviceBuffer()),
M,
N,
K,
BatchCount,
StrideA,
StrideB,
{},
StrideC,
BatchStrideA,
BatchStrideB,
{},
BatchStrideC,
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{},
kbatch_curr);
auto invoker_ptr = op_ptr->MakeInvokerPointer();
if(op_ptr->IsSupportedArgument(argument_ptr.get()))
{
std::string op_name = op_ptr->GetTypeString();
std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(CDataType) * M * N) *
BatchCount;
float ave_time = invoker_ptr->Run(
argument_ptr.get(),
StreamConfig{nullptr, time_kernel, 0, n_warmup, n_iter, true, rotating_count});
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
std::size_t flop = std::size_t(2) * BatchCount * M * N * K;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::size_t num_btype = (sizeof(ADataType) * M * K + sizeof(BDataType) * K * N +
sizeof(CDataType) * M * N) *
BatchCount;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << op_name << std::endl;
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
}
float gb_per_sec = num_btype / 1.E6 / ave_time;
if(do_verification)
{
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec
<< " GB/s, " << op_name << ", KBatch " << kbatch_curr << std::endl;
pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
if(tflops > best_tflops)
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
best_kbatch = kbatch_curr;
}
if(do_log)
if(do_verification)
{
LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
<< std::endl;
c_device_buf.FromDevice(c_g_m_n_device_result.mData.data());
pass = pass & ck::utils::check_err(c_g_m_n_device_result, c_g_m_n_host_result);
if(do_log)
{
LogRangeAsType<float>(std::cout << "a : ", a_g_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_g_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(
std::cout << "c_host: ", c_g_m_n_host_result.mData, ",")
<< std::endl;
LogRangeAsType<float>(
std::cout << "c_device: ", c_g_m_n_device_result.mData, ",")
<< std::endl;
}
}
}
}
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem" << std::endl;
else
{
std::cout << op_ptr->GetTypeString() << " does not support this problem"
<< std::endl;
}
}
}
......@@ -270,8 +282,8 @@ bool profile_gemm_universal_batched_impl(int do_verification,
std::cout << " B = " << BatchCount << " M = " << M << " N = " << N << " K = " << K
<< " StrideA = " << StrideA << " StrideB = " << StrideB << " StrideC = " << StrideC
<< ": " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl;
<< " KBatch = " << best_kbatch << ": " << best_ave_time << " ms, " << best_tflops
<< " TFlops, " << best_gb_per_sec << " GB/s, " << best_op_name << std::endl;
return pass;
}
......
profiler/include/profiler/profile_gemm_universal_impl.hpp
View file @ 989ffa2b
......@@ -144,6 +144,7 @@ bool profile_gemm_universal_impl(int do_verification,
}
std::string best_op_name;
std::optional<std::string> best_op_object_name;
float best_ave_time = 0;
float best_tflops = 0;
float best_gb_per_sec = 0;
......@@ -225,7 +226,8 @@ bool profile_gemm_universal_impl(int do_verification,
}
}
std::string op_name = op_ptr->GetTypeString();
std::string op_name = op_ptr->GetTypeString();
std::optional<std::string> op_obj_name = op_ptr->GetObjectName();
float ave_time = invoker_ptr->Run(argument_ptr.get(),
StreamConfig{nullptr,
......@@ -251,11 +253,12 @@ bool profile_gemm_universal_impl(int do_verification,
if(tflops > best_tflops && ave_time > 1e-10)
{
best_op_name = op_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
best_kbatch = kbatch_curr;
best_op_name = op_name;
best_op_object_name = op_obj_name;
best_tflops = tflops;
best_ave_time = ave_time;
best_gb_per_sec = gb_per_sec;
best_kbatch = kbatch_curr;
}
}
else
......@@ -306,6 +309,9 @@ bool profile_gemm_universal_impl(int do_verification,
<< " : " << best_ave_time << " ms, " << best_tflops << " TFlops, " << best_gb_per_sec
<< " GB/s, " << best_op_name << std::endl;
if(best_op_object_name)
std::cout << best_op_object_name.value() << std::endl;
return pass;
}
......
profiler/src/profile_gemm_universal_batched.cpp
View file @ 989ffa2b
......@@ -31,7 +31,7 @@ enum struct GemmDataType
int profile_batched_gemm_universal(int argc, char* argv[])
{
if(argc != 18 && argc != 21)
if(argc != 19 && argc != 22)
{
// clang-format off
printf("arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n");
......@@ -44,11 +44,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
printf("arg5: initialization (0: no init; 1: integer value; 2: decimal value)\n");
printf("arg6: print tensor value (0: no; 1: yes)\n");
printf("arg7: time kernel (0=n0, 1=yes)\n");
printf("arg8 to 17: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount\n");
printf("arg8 to 18: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount, KBatch\n");
printf("optional:\n");
printf("arg18: number of warm-up cycles (default 1)\n");
printf("arg19: number of iterations (default 10)\n");
printf("arg20: memory for rotating buffer (default 0, size in MB)\n");
printf("arg19: number of warm-up cycles (default 1)\n");
printf("arg20: number of iterations (default 10)\n");
printf("arg21: memory for rotating buffer (default 0, size in MB)\n");
// clang-format on
exit(1);
}
......@@ -56,11 +56,11 @@ int profile_batched_gemm_universal(int argc, char* argv[])
int n_warmup = 1;
int n_iter = 10;
uint64_t rotating = 0;
if(argc == 21)
if(argc == 22)
{
n_warmup = std::stoi(argv[18]);
n_iter = std::stoi(argv[19]);
rotating = std::stoull(argv[20]) * 1024 * 1024;
n_warmup = std::stoi(argv[19]);
n_iter = std::stoi(argv[20]);
rotating = std::stoull(argv[21]) * 1024 * 1024;
}
const auto data_type = static_cast<GemmDataType>(std::stoi(argv[2]));
......@@ -83,6 +83,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
const int BatchStrideC = std::stoi(argv[16]);
const int BatchCount = std::stoi(argv[17]);
const int KBatch = std::stoi(argv[18]);
#if defined(CK_USE_FP8_ON_UNSUPPORTED_ARCH) || defined(CK_USE_GFX94)
using F8 = ck::f8_t;
......@@ -159,6 +160,7 @@ int profile_batched_gemm_universal(int argc, char* argv[])
StrideB_,
StrideC_,
BatchCount,
KBatch,
n_warmup,
n_iter,
rotating);
......
script/process_perf_data.py
View file @ 989ffa2b
......@@ -332,7 +332,7 @@ def main():
table_name="ck_fmha_bwd_tflops"
tflops_base = get_baseline(table_name,conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, conn)
store_new_test_result(table_name, results, testlist, branch_name, node_id, gpu_arch, compute_units, rocm_vers, hip_vers, environment, sqlEngine)
conn.close()
#compare the results to the baseline if baseline exists
......
test/ck_tile/gemm/CMakeLists.txt
View file @ 989ffa2b
# Currently ck_tile is only built on gfx9
if(GPU_TARGETS MATCHES "gfx9")
add_gtest_executable(test_ck_tile_gemm_mem_pipeline test_gemm_mem_pipeline.cpp)
add_gtest_executable(test_ck_tile_gemm_pipeline test_gemm_pipeline.cpp)
endif()
test/ck_tile/gemm/test_gemm_mem_pipeline.cpp → test/ck_tile/gemm/test_gemm_pipeline.cpp
View file @ 989ffa2b
......@@ -6,7 +6,7 @@
#include "gtest/gtest.h"
#include "ck_tile/host.hpp"
#include "test_gemm_mem_pipeline_util.hpp"
#include "test_gemm_pipeline_util.hpp"
using F16 = ck_tile::half_t;
using F32 = float;
......@@ -16,21 +16,27 @@ using Intrawave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Intrawave>;
using Interwave = ck_tile::integral_constant<ck_tile::GemmPipelineScheduler,
ck_tile::GemmPipelineScheduler::Interwave>;
using Mem = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Mem>;
using Comp = ck_tile::integral_constant<GemmPipelineType, GemmPipelineType::Comp>;
// clang-format off
using KernelTypes = ::testing::Types<
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Interwave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Interwave>
// ALayout, BLayout, CLayout, ADataType, BDataType, AccDataType, CDataType, GemmPipelineScheduler, PipelineType
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Row, Row, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Row, Col, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Col, Row, Row, F16, F16, F32, F16, Interwave, Mem>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave, Mem>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Intrawave, Comp>,
std::tuple< Col, Col, Row, F16, F16, F32, F16, Interwave, Mem>
>;
// clang-format on
TYPED_TEST_SUITE(TestCkTileGemmMemPipeline, KernelTypes);
TYPED_TEST_SUITE(TestCkTileGemmPipeline, KernelTypes);
#include "test_gemm_mem_pipeline_ut_cases.inc"
#include "test_gemm_pipeline_ut_cases.inc"
test/ck_tile/gemm/test_gemm_mem_pipeline_ut_cases.inc → test/ck_tile/gemm/test_gemm_pipeline_ut_cases.inc
View file @ 989ffa2b
......@@ -3,7 +3,7 @@
#pragma once
TYPED_TEST(TestCkTileGemmMemPipeline, SmallM)
TYPED_TEST(TestCkTileGemmPipeline, SmallM)
{
std::vector<int> Ms{1, 2, 3, 4, 5, 6};
constexpr int N = 1024;
......@@ -13,7 +13,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, SmallM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, MidLargeM)
TYPED_TEST(TestCkTileGemmPipeline, MidLargeM)
{
std::vector<int> Ms{127, 255, 312, 799, 1573};
constexpr int N = 1024;
......@@ -23,7 +23,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, MidLargeM)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, PaddK)
TYPED_TEST(TestCkTileGemmPipeline, PaddK)
{
std::vector<int> Ms{127};
constexpr int N = 1024;
......@@ -33,7 +33,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, PaddK)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, Regular)
TYPED_TEST(TestCkTileGemmPipeline, Regular)
{
std::vector<int> Ms{512};
constexpr int N = 1024;
......@@ -43,7 +43,7 @@ TYPED_TEST(TestCkTileGemmMemPipeline, Regular)
this->Run(M, N, K);
}
TYPED_TEST(TestCkTileGemmMemPipeline, NotSupportedArgument)
TYPED_TEST(TestCkTileGemmPipeline, NotSupportedArgument)
{
constexpr int M = 512;
constexpr int N = 1025;
......
test/ck_tile/gemm/test_gemm_mem_pipeline_util.hpp → test/ck_tile/gemm/test_gemm_pipeline_util.hpp
View file @ 989ffa2b
......@@ -11,18 +11,24 @@
#include "ck_tile/ops/epilogue.hpp"
#include "ck_tile/ops/gemm.hpp"
enum struct GemmPipelineType
{
Mem,
Comp
};
template <typename Tuple>
class TestCkTileGemmMemPipeline : public ::testing::Test
class TestCkTileGemmPipeline : public ::testing::Test
{
protected:
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>;
using CLayout = std::tuple_element_t<2, Tuple>;
using ADataType = std::tuple_element_t<3, Tuple>;
using BDataType = std::tuple_element_t<4, Tuple>;
using AccDataType = std::tuple_element_t<5, Tuple>;
using CDataType = std::tuple_element_t<6, Tuple>;
static constexpr auto Scheduler = std::tuple_element_t<7, Tuple>::value;
static constexpr auto PipelineType = std::tuple_element_t<8, Tuple>::value;
// TODO: expose tile size through test t-param ?
struct gemm_args
......@@ -74,8 +80,13 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
using Traits = ck_tile::TileGemmTraits<kPadM, kPadN, kPadK, ALayout, BLayout, CLayout>;
using BaseGemmPipeline = ck_tile::BaseGemmPipelineAgBgCrMem<
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>;
using BaseGemmPipeline = std::conditional_t<
PipelineType == GemmPipelineType::Mem,
ck_tile::BaseGemmPipelineAgBgCrMem<
ck_tile::GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>,
ck_tile::BaseGemmPipelineAgBgCrCompV3<
ck_tile::
GemmPipelineProblem<ADataType, BDataType, AccDataType, GemmShape, Traits>>>;
const ck_tile::index_t num_loop = TilePartitioner::GetLoopNum(args.K);
const bool has_hot_loop = BaseGemmPipeline::BlockHasHotloop(num_loop);
......@@ -85,15 +96,26 @@ class TestCkTileGemmMemPipeline : public ::testing::Test
constexpr bool has_hot_loop_v = has_hot_loop_.value;
constexpr auto tail_number_v = tail_number_.value;
using GemmPipeline = ck_tile::GemmPipelineAgBgCrMem<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>;
using GemmPipeline =
std::conditional_t<PipelineType == GemmPipelineType::Mem,
ck_tile::GemmPipelineAgBgCrMem<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>,
ck_tile::GemmPipelineAgBgCrCompV3<
ck_tile::UniversalGemmPipelineProblem<ADataType,
BDataType,
AccDataType,
GemmShape,
Traits,
Scheduler,
has_hot_loop_v,
tail_number_v>>>;
using Kernel = ck_tile::GemmKernel<TilePartitioner, GemmPipeline, GemmEpilogue>;
auto kargs = Kernel::MakeKargs(args.p_a,
args.p_b,
......
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