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Commit 082cf643 authored by Jun Liu's avatar Jun Liu
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Merge branch 'develop' into amd-develop

parents 7e8230da 59136091
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library/src/tensor_operation_instance/gpu/gemm_splitk/device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instance.cpp
View file @ 082cf643
...@@ -26,7 +26,7 @@ using S = ck::Sequence<Is...>; ...@@ -26,7 +26,7 @@ using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::MNKPadding;
// Compilation parameters for a[k, m] * b[n, k] = c[m, n] // Compilation parameters for a[k, m] * b[n, k] = c[m, n]
using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple< using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple<
...@@ -35,6 +35,9 @@ using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple< ...@@ -35,6 +35,9 @@ using device_gemm_xdl_splitk_f32_f32_f32_km_nk_mn_instances = std::tuple<
//#########################| 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| //#########################| 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|
//#########################| | | | | | | | 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| //#########################| | | | | | | | 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|
//#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //#########################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
//Generic instances
DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 1, 4, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 1, 4, true, 1, 1, S<1, 32, 1, 8>, 1>,
//
DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 4, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>, DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 4, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 4, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>, DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 4, 32, 32, 2, 4, S<1, 4, 64, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 2, 4, true, S<1, 4, 64, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 32, 1, 8>, 4>,
DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 16, 1, 8>, 4>, DeviceGemmXdlSplitKCShuffle< F32, F32, F32, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 4, 32, 32, 4, 2, S<1, 4, 32, 1>, S<0, 1, 3, 2>, S<0, 1, 3, 2>, 2, 4, 4, true, S<1, 4, 32, 1>, S<0, 2, 1, 3>, S<0, 2, 1, 3>, 3, 4, 4, true, 1, 1, S<1, 16, 1, 8>, 4>,
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/CMakeLists.txt
View file @ 082cf643
...@@ -5,7 +5,7 @@ add_instance_library(device_grouped_conv3d_bwd_data_instance ...@@ -5,7 +5,7 @@ add_instance_library(device_grouped_conv3d_bwd_data_instance
xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_input_f16_comp_bf8_f8_instance.cpp
wmma/device_grouped_conv3d_bwd_data_wmma_gndhwc_gkzyxc_gndhwk_f16_instance.cpp wmma/device_grouped_conv3d_bwd_data_wmma_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
wmma/device_grouped_conv3d_bwd_data_wmma_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp wmma/device_grouped_conv3d_bwd_data_wmma_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
wmma/device_grouped_conv3d_bwd_data_wmma_gndhwc_gkzyxc_gndhwk_i8_instance.cpp wmma/device_grouped_conv3d_bwd_data_wmma_gndhwc_gkzyxc_gndhwk_i8_instance.cpp
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_data/xdl/device_grouped_conv3d_bwd_data_xdl_ndhwgc_gkzyxc_ndhwgk_input_f16_comp_bf8_f8_instance.cpp 0 → 100644
View file @ 082cf643
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_data/device_grouped_conv_bwd_data_xdl_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
// Compilation parameters for out[n, di, hi, wi, g, c] * wei[g, k, z, y, x, c] = in[n, do, ho, wo,
// g, k]
void add_device_grouped_conv3d_bwd_data_xdl_ndhwgk_gkzyxc_ndhwgc_input_f16_comp_bf8f8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdDataMultipleD<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough,
BF8,
F8>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_data_xdl_input_fp16_comp_bf8f8_instances<3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(instances,
device_grouped_conv_bwd_data_xdl_input_fp16_comp_bf8f8_instances<
3,
NDHWGK,
GKZYXC,
Empty_Tuple,
NDHWGC,
ConvBwdDataFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/CMakeLists.txt
View file @ 082cf643
...@@ -4,7 +4,8 @@ set(GROUPED_CONV3D_BWD_WEIGHT ...@@ -4,7 +4,8 @@ set(GROUPED_CONV3D_BWD_WEIGHT
device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp device_grouped_conv3d_bwd_weight_xdl_gndhwc_gkzyxc_gndhwk_bf16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp) device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_fp8_instance.cpp)
if(DL_KERNELS) if(DL_KERNELS)
list(APPEND GROUPED_CONV3D_BWD_WEIGHT list(APPEND GROUPED_CONV3D_BWD_WEIGHT
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_fp8_instance.cpp 0 → 100644
View file @ 082cf643
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_bwd_weight_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_bf8_f8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
NDHWGC,
GKZYXC,
NDHWGK,
F16,
F16,
F16,
PassThrough,
PassThrough,
PassThrough,
BF8,
F8>>>& instances)
{
// 1. Default
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_comp_bf8_f8_instances<
3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightDefault>{});
// 2. Filter1x1Stride1Pad0
add_device_operation_instances(
instances,
device_grouped_conv_bwd_weight_xdl_c_shuffle_f16_comp_bf8_f8_instances<
3,
NDHWGC,
GKZYXC,
NDHWGK,
ConvBwdWeightFilter1x1Stride1Pad0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/CMakeLists.txt
View file @ 082cf643
...@@ -4,10 +4,12 @@ add_instance_library(device_grouped_conv3d_fwd_instance ...@@ -4,10 +4,12 @@ add_instance_library(device_grouped_conv3d_fwd_instance
xdl/device_grouped_conv3d_fwd_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_gndhwc_gkzyxc_gndhwk_f32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_gndhwc_gkzyxc_gndhwk_int8_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_gndhwc_gkzyxc_gndhwk_int8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_bf16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f32_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_int8_instance.cpp xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_int8_instance.cpp
xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_fp8_instance.cpp
wmma/device_grouped_conv3d_fwd_wmma_gndhwc_gkzyxc_gndhwk_f16_instance.cpp wmma/device_grouped_conv3d_fwd_wmma_gndhwc_gkzyxc_gndhwk_f16_instance.cpp
wmma/device_grouped_conv3d_fwd_wmma_gndhwc_gkzyxc_gndhwk_i8_instance.cpp wmma/device_grouped_conv3d_fwd_wmma_gndhwc_gkzyxc_gndhwk_i8_instance.cpp
......
library/src/tensor_operation_instance/gpu/grouped_conv3d_fwd/xdl/device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_fp8_instance.cpp 0 → 100644
View file @ 082cf643
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2023, Advanced Micro Devices, Inc. All rights reserved.
#include "ck/library/tensor_operation_instance/gpu/grouped_conv_fwd/device_grouped_conv_fwd_xdl_instance.hpp"
#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
void add_device_grouped_conv3d_fwd_xdl_ndhwgc_gkzyxc_ndhwgk_f16_comp_f8_instances(
std::vector<std::unique_ptr<DeviceGroupedConvFwdMultipleD<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
F16,
F16,
Empty_Tuple,
F16,
PassThrough,
PassThrough,
PassThrough,
F8>>>& instances)
{
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f16_comp_f8_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwdDefault>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f16_comp_f8_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1P0>{});
add_device_operation_instances(
instances,
device_grouped_conv_fwd_xdl_f16_comp_f8_instances<3,
NDHWGC,
GKZYXC,
Empty_Tuple,
NDHWGK,
ConvFwd1x1S1P0>{});
}
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
profiler/README.md
View file @ 082cf643
...@@ -50,23 +50,21 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s ...@@ -50,23 +50,21 @@ Best Perf: 1.42509 ms, 102.988 TFlops, 234.086 GB/s
## Profile contraction kernels ## Profile contraction kernels
```bash ```bash
#arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear) #arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear)
#arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16) #arg2: data type (0: fp32; 1: f64)\n"
#arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16) #arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
#arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]; # 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]; # 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1]) # 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1])
#arg5: verification (0: no; 1: yes) #arg4: verification (0: no; 1: yes)
#arg6: initialization (0: no init; 1: integer value; 2: decimal value) #arg5: initialization (0: no init; 1: integer value; 2: decimal value)
#arg7: print tensor value (0: no; 1: yes) #arg6: print tensor value (0: no; 1: yes)
#arg8: time kernel (0: no, 1: yes) #arg7: time kernel (0: no, 1: yes)
#arg9: alpha #arg8 and arg9: alpha and beta
#arg10: beta #arg10 to 15: M0, M1, N0, N1, K0, K1
#arg11 to 16: M0, M1, N0, N1, K0, K1 #arg16 to 31: Strides for A, B, D and E (skip for default)
#arg17 to 32: Strides for A, B, D and E (skip for default)
################ op datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
################ op datatype compute_datatype layout verify init log time alpha beta M0 M1 N0 N1 K0 K1 ./bin/ckProfiler contraction_bilinear 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
./bin/ckProfiler contraction_bilinear 0 0 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
``` ```
Result (MI100) Result (MI100)
......
profiler/include/profiler/profile_contraction_impl.hpp
View file @ 082cf643
...@@ -31,14 +31,10 @@ namespace profiler { ...@@ -31,14 +31,10 @@ namespace profiler {
using Bilinear = ck::tensor_operation::element_wise::Bilinear; using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale; using Scale = ck::tensor_operation::element_wise::Scale;
using F32 = float;
using F64 = double;
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
typename CDELayout, typename CDELayout,
typename DataType, typename DataType,
typename ComputeDataType,
typename DTupleDataType, typename DTupleDataType,
typename CDElementOp> typename CDElementOp>
int profile_contraction_impl(ck::index_t do_verification, int profile_contraction_impl(ck::index_t do_verification,
...@@ -49,10 +45,10 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -49,10 +45,10 @@ int profile_contraction_impl(ck::index_t do_verification,
const std::vector<ck::index_t>& M, const std::vector<ck::index_t>& M,
const std::vector<ck::index_t>& N, const std::vector<ck::index_t>& N,
const std::vector<ck::index_t>& K, const std::vector<ck::index_t>& K,
const std::vector<ck::index_t>& StridesA, // [M0, M1, K0, K1] const std::vector<ck::index_t>& StridesA,
const std::vector<ck::index_t>& StridesB, // [N0, N1, K0, K1] const std::vector<ck::index_t>& StridesB,
const std::vector<ck::index_t>& StridesE, // [M0, M1, N0, N1] const std::vector<ck::index_t>& StridesE,
const std::vector<ck::index_t>& StridesD) // [M0, M1, N0, N1] const std::vector<ck::index_t>& StridesD)
{ {
bool pass = true; bool pass = true;
...@@ -67,13 +63,13 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -67,13 +63,13 @@ int profile_contraction_impl(ck::index_t do_verification,
}; };
Tensor<DataType> a_m_k(f_host_tensor_descriptor(M, K, StridesA)); Tensor<DataType> a_m_k(f_host_tensor_descriptor(M, K, StridesA));
Tensor<DataType> b_n_k(f_host_tensor_descriptor(N, K, StridesB)); Tensor<DataType> b_k_n(f_host_tensor_descriptor(K, N, StridesB));
Tensor<DataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StridesE));
Tensor<DataType> d_m_n(f_host_tensor_descriptor(M, N, StridesD)); Tensor<DataType> d_m_n(f_host_tensor_descriptor(M, N, StridesD));
std::cout << "a_m_k: " << a_m_k.mDesc << std::endl; std::cout << "a_m_k: " << a_m_k.mDesc << std::endl;
std::cout << "b_n_k: " << b_n_k.mDesc << std::endl; std::cout << "b_k_n: " << b_k_n.mDesc << std::endl;
std::cout << "d_m_n: " << d_m_n.mDesc << std::endl; std::cout << "d_m_n: " << d_m_n.mDesc << std::endl;
std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl; std::cout << "e_m_n: " << e_m_n_device_result.mDesc << std::endl;
...@@ -82,12 +78,12 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -82,12 +78,12 @@ int profile_contraction_impl(ck::index_t do_verification,
case 0: break; case 0: break;
case 1: case 1:
a_m_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); a_m_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
b_n_k.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); b_k_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
d_m_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5}); d_m_n.GenerateTensorValue(GeneratorTensor_2<DataType>{-5, 5});
break; break;
default: default:
a_m_k.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0}); a_m_k.GenerateTensorValue(GeneratorTensor_3<DataType>{0.0, 1.0});
b_n_k.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5}); b_k_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
d_m_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5}); d_m_n.GenerateTensorValue(GeneratorTensor_3<DataType>{-0.5, 0.5});
} }
...@@ -95,12 +91,12 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -95,12 +91,12 @@ int profile_contraction_impl(ck::index_t do_verification,
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = ck::tensor_operation::element_wise::PassThrough;
DeviceMem a_device_buf(sizeof(DataType) * a_m_k.mDesc.GetElementSpaceSize()); DeviceMem a_device_buf(sizeof(DataType) * a_m_k.mDesc.GetElementSpaceSize());
DeviceMem b_device_buf(sizeof(DataType) * b_n_k.mDesc.GetElementSpaceSize()); DeviceMem b_device_buf(sizeof(DataType) * b_k_n.mDesc.GetElementSpaceSize());
DeviceMem e_device_buf(sizeof(DataType) * e_m_n_device_result.mDesc.GetElementSpaceSize()); DeviceMem e_device_buf(sizeof(DataType) * e_m_n_device_result.mDesc.GetElementSpaceSize());
DeviceMem d_device_buf(sizeof(DataType) * d_m_n.mDesc.GetElementSpaceSize()); DeviceMem d_device_buf(sizeof(DataType) * d_m_n.mDesc.GetElementSpaceSize());
a_device_buf.ToDevice(a_m_k.mData.data()); a_device_buf.ToDevice(a_m_k.mData.data());
b_device_buf.ToDevice(b_n_k.mData.data()); b_device_buf.ToDevice(b_k_n.mData.data());
e_device_buf.SetZero(); e_device_buf.SetZero();
d_device_buf.ToDevice(d_m_n.mData.data()); d_device_buf.ToDevice(d_m_n.mData.data());
...@@ -122,8 +118,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -122,8 +118,7 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType, DataType,
AElementOp, AElementOp,
BElementOp, BElementOp,
CDElementOp, CDElementOp>;
ComputeDataType>;
// get device op instances // get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
...@@ -131,9 +126,6 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -131,9 +126,6 @@ int profile_contraction_impl(ck::index_t do_verification,
std::cout << "found " << op_ptrs.size() << " instances" << std::endl; std::cout << "found " << op_ptrs.size() << " instances" << std::endl;
using AccDataType =
typename std::conditional<std::is_same<ComputeDataType, F64>::value, F64, F32>::type;
// Run reference op // Run reference op
if(do_verification) if(do_verification)
{ {
...@@ -144,8 +136,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -144,8 +136,7 @@ int profile_contraction_impl(ck::index_t do_verification,
DataType, DataType,
DataType, DataType,
DataType, DataType,
AccDataType, DataType,
ComputeDataType,
AElementOp, AElementOp,
BElementOp>; BElementOp>;
...@@ -155,7 +146,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -155,7 +146,7 @@ int profile_contraction_impl(ck::index_t do_verification,
Tensor<DataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE)); Tensor<DataType> c_m_n_host_result(f_host_tensor_descriptor(M, N, StridesE));
auto ref_argument = auto ref_argument =
ref_op.MakeArgument(a_m_k, b_n_k, c_m_n_host_result, a_element_op, b_element_op); ref_op.MakeArgument(a_m_k, b_k_n, c_m_n_host_result, a_element_op, b_element_op);
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
...@@ -281,29 +272,8 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -281,29 +272,8 @@ int profile_contraction_impl(ck::index_t do_verification,
{ {
e_device_buf.FromDevice(e_m_n_device_result.mData.data()); e_device_buf.FromDevice(e_m_n_device_result.mData.data());
// Both the kernel and the reference use `AccDataType`, so an absolute error of both float threshold =
// of them is bounded by `nelems_k * std::numeric_limits<AccDataType>::epsilon()`. static_cast<DataType>(nelems_k) * std::numeric_limits<DataType>::epsilon();
// Comparing one to another can result in an absolute error as high as twice that
// value.
double threshold = 2 * nelems_k * std::numeric_limits<AccDataType>::epsilon();
// Handle the possible casting error of either AccDataType -> DataType or
// DataType -> ComputeDataType.
// TODO: Add a generic solution for calculating thresholds in CK.
if constexpr(ck::is_same_v<DataType, ck::bhalf_t> ||
ck::is_same_v<ComputeDataType, ck::bhalf_t>)
{
const double epsilon = std::pow(2, -7);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
else if constexpr(ck::is_same_v<DataType, ck::half_t> ||
ck::is_same_v<ComputeDataType, ck::half_t>)
{
const double epsilon = std::pow(2, -10);
// Maximum relative casting error when rounding to zero.
threshold += epsilon * 2;
}
pass = pass & ck::utils::check_err(e_m_n_device_result, pass = pass & ck::utils::check_err(e_m_n_device_result,
e_m_n_host_result, e_m_n_host_result,
"Error: incorrect results!", "Error: incorrect results!",
...@@ -313,7 +283,7 @@ int profile_contraction_impl(ck::index_t do_verification, ...@@ -313,7 +283,7 @@ int profile_contraction_impl(ck::index_t do_verification,
if(do_log) if(do_log)
{ {
LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "a : ", a_m_k.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "b: ", b_n_k.mData, ",") << std::endl; LogRangeAsType<float>(std::cout << "b: ", b_k_n.mData, ",") << std::endl;
LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",") LogRangeAsType<float>(std::cout << "c_host : ", e_m_n_host_result.mData, ",")
<< std::endl; << std::endl;
LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",") LogRangeAsType<float>(std::cout << "c_device: ", e_m_n_device_result.mData, ",")
......
profiler/include/profiler/profile_contraction_utils.hpp
View file @ 082cf643
...@@ -23,18 +23,8 @@ enum struct ContractionMatrixLayout ...@@ -23,18 +23,8 @@ enum struct ContractionMatrixLayout
enum struct ContractionDataType enum struct ContractionDataType
{ {
F32_F32_F32_F32, // 0 F32_F32_F32_F32, // 0
F64_F64_F64_F64, // 1 F64_F64_F64_F64, // 1
F16_F16_F16_F16, // 2
BF16_BF16_BF16_BF16, // 3
};
enum struct ContractionComputeDataType
{
F32 = 0,
F64,
F16,
BF16,
}; };
inline void collect_index_params(char* argv[], inline void collect_index_params(char* argv[],
......
profiler/include/profiler/profile_grouped_conv_bwd_weight_impl.hpp
View file @ 082cf643
...@@ -33,7 +33,9 @@ template <ck::index_t NDimSpatial, ...@@ -33,7 +33,9 @@ template <ck::index_t NDimSpatial,
typename OutLayout, typename OutLayout,
typename InDataType, typename InDataType,
typename WeiDataType, typename WeiDataType,
typename OutDataType> typename OutDataType,
typename ComputeTypeA = InDataType,
typename ComputeTypeB = ComputeTypeA>
bool profile_grouped_conv_bwd_weight_impl(int do_verification, bool profile_grouped_conv_bwd_weight_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
...@@ -120,7 +122,9 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification, ...@@ -120,7 +122,9 @@ bool profile_grouped_conv_bwd_weight_impl(int do_verification,
OutDataType, OutDataType,
InElementOp, InElementOp,
WeiElementOp, WeiElementOp,
OutElementOp>; OutElementOp,
ComputeTypeA,
ComputeTypeB>;
// get device op instances // get device op instances
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory< const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
......
profiler/src/profile_contraction_bilinear.cpp
View file @ 082cf643
...@@ -17,9 +17,8 @@ ...@@ -17,9 +17,8 @@
static void print_helper_msg() static void print_helper_msg()
{ {
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg2: data type (0: fp32; 1: f64)\n"
<< "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + " << " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
...@@ -27,42 +26,40 @@ static void print_helper_msg() ...@@ -27,42 +26,40 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + " << " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg5: verification (0: no; 1: yes)\n" << "arg4: verification (0: no; 1: yes)\n"
<< "arg6: initialization (0: no init; 1: integer value; 2: decimal " << "arg5: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n" << "value)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n" << "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n" << "arg7: time kernel (0: no, 1: yes)\n"
<< "arg9: alpha\n" << "arg8 and arg9: alpha and beta\n"
<< "arg10: beta\n" << "arg10 to 15: M0, M1, N0, N1, K0, K1\n"
<< "arg11 to 16: M0, M1, N0, N1, K0, K1\n" << "arg16 to 31: Strides for A, B, D and E (skip for default)\n"
<< "arg17 to 32: Strides for A, B, D and E (skip for default)\n"
<< std::endl; << std::endl;
} }
int profile_contraction_bilinear(int argc, char* argv[]) int profile_contraction_bilinear(int argc, char* argv[])
{ {
const bool default_strides = argc == 17; const bool default_strides = argc == 16;
if(argc != 33 && argc != 17) if(argc != 32 && argc != 16)
{ {
print_helper_msg(); print_helper_msg();
exit(1); exit(1);
} }
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2])); const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3])); const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4])); const bool do_verification = std::stoi(argv[4]);
const bool do_verification = std::stoi(argv[5]); const ck::index_t init_method = std::stoi(argv[5]);
const ck::index_t init_method = std::stoi(argv[6]); const bool do_log = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]); const float alpha = std::stof(argv[8]);
const float alpha = std::stof(argv[9]); const float beta = std::stof(argv[9]);
const float beta = std::stof(argv[10]);
std::vector<ck::index_t> M; std::vector<ck::index_t> M;
std::vector<ck::index_t> N; std::vector<ck::index_t> N;
std::vector<ck::index_t> K; std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 11; const ck::index_t dims_arg_num = 10;
collect_index_params(argv, M, dims_arg_num, 2); collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2); collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2); collect_index_params(argv, K, dims_arg_num + 4, 2);
...@@ -79,130 +76,90 @@ int profile_contraction_bilinear(int argc, char* argv[]) ...@@ -79,130 +76,90 @@ int profile_contraction_bilinear(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4); collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
} }
using F16 = ck::half_t; using F32 = float;
using BF16 = ck::bhalf_t; using F64 = double;
using F32 = float;
using F64 = double; auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) {
using ALayout = decltype(a_layout);
auto profile = using BLayout = decltype(b_layout);
[&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) { using CDELayout = decltype(cde_layout);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout); using DataType = decltype(type);
using CDELayout = decltype(cde_layout);
if(default_strides)
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<DataType>,
Bilinear>(do_verification,
init_method,
do_log,
time_kernel,
Bilinear{alpha, beta},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
return profile(Col{}, Col{}, Row{}, type, compute_type); assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
} }
return false; bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ck::Tuple<DataType>,
Bilinear>(do_verification,
init_method,
do_log,
time_kernel,
Bilinear{alpha, beta},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
}; };
if(data_type == ContractionDataType::F32_F32_F32_F32) if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Row{}, Row{}, Row{}, F32{});
{
return run_profile_for_datatype(F32{}, F32{});
}
else if(compute_data_type == ContractionComputeDataType::F16)
{
return run_profile_for_datatype(F32{}, F16{});
}
else if(compute_data_type == ContractionComputeDataType::BF16)
{
return run_profile_for_datatype(F32{}, BF16{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F64) return profile(Row{}, Col{}, Row{}, F32{});
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F16_F16_F16_F16) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Col{}, Row{}, Row{}, F32{});
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Col{}, Col{}, Row{}, F32{});
{ }
return run_profile_for_datatype(BF16{}, F32{}); else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
} layout == ContractionMatrixLayout::MK_KN_MN_MN)
else {
{ return profile(Row{}, Row{}, Row{}, F64{});
std::cout << "Incorrect combination of data type and compute data type." << std::endl; }
return 1; else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
} layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, F64{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
} }
return 1;
} }
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_bilinear); REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_bilinear);
profiler/src/profile_contraction_scale.cpp
View file @ 082cf643
...@@ -17,9 +17,8 @@ ...@@ -17,9 +17,8 @@
static void print_helper_msg() static void print_helper_msg()
{ {
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg2: data type (0: fp32; 1: f64)\n"
<< "arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)\n" << "arg3: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
<< "arg4: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + " << " 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
...@@ -27,40 +26,39 @@ static void print_helper_msg() ...@@ -27,40 +26,39 @@ static void print_helper_msg()
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1];\n"
<< " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + " << " 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + "
"D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n" "D[m0, m1, n0, n1] = E[m0, m1, n0, n1])\n"
<< "arg5: verification (0: no; 1: yes)\n" << "arg4: verification (0: no; 1: yes)\n"
<< "arg6: initialization (0: no init; 1: integer value; 2: decimal " << "arg5: initialization (0: no init; 1: integer value; 2: decimal "
<< "value)\n" << "value)\n"
<< "arg7: print tensor value (0: no; 1: yes)\n" << "arg6: print tensor value (0: no; 1: yes)\n"
<< "arg8: time kernel (0: no, 1: yes)\n" << "arg7: time kernel (0: no, 1: yes)\n"
<< "arg9: alpha\n" << "arg8: alpha\n"
<< "arg10 to 15: M0, M1, N0, N1, K0, K1\n" << "arg9 to 14: M0, M1, N0, N1, K0, K1\n"
<< "arg16 to 31: Strides for A, B, D and E (skip for default)\n" << "arg15 to 30: Strides for A, B, D and E (skip for default)\n"
<< std::endl; << std::endl;
} }
int profile_contraction_scale(int argc, char* argv[]) int profile_contraction_scale(int argc, char* argv[])
{ {
const bool default_strides = argc == 16; const bool default_strides = argc == 15;
if(argc != 32 && argc != 16) if(argc != 31 && argc != 15)
{ {
print_helper_msg(); print_helper_msg();
exit(1); exit(1);
} }
const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2])); const auto data_type = static_cast<ContractionDataType>(std::stoi(argv[2]));
const auto compute_data_type = static_cast<ContractionComputeDataType>(std::stoi(argv[3])); const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[3]));
const auto layout = static_cast<ContractionMatrixLayout>(std::stoi(argv[4])); const bool do_verification = std::stoi(argv[4]);
const bool do_verification = std::stoi(argv[5]); const ck::index_t init_method = std::stoi(argv[5]);
const ck::index_t init_method = std::stoi(argv[6]); const bool do_log = std::stoi(argv[6]);
const bool do_log = std::stoi(argv[7]); const bool time_kernel = std::stoi(argv[7]);
const bool time_kernel = std::stoi(argv[8]); const float alpha = std::stof(argv[8]);
const float alpha = std::stof(argv[9]);
std::vector<ck::index_t> M; std::vector<ck::index_t> M;
std::vector<ck::index_t> N; std::vector<ck::index_t> N;
std::vector<ck::index_t> K; std::vector<ck::index_t> K;
const ck::index_t dims_arg_num = 10; const ck::index_t dims_arg_num = 9;
collect_index_params(argv, M, dims_arg_num, 2); collect_index_params(argv, M, dims_arg_num, 2);
collect_index_params(argv, N, dims_arg_num + 2, 2); collect_index_params(argv, N, dims_arg_num + 2, 2);
collect_index_params(argv, K, dims_arg_num + 4, 2); collect_index_params(argv, K, dims_arg_num + 4, 2);
...@@ -77,131 +75,88 @@ int profile_contraction_scale(int argc, char* argv[]) ...@@ -77,131 +75,88 @@ int profile_contraction_scale(int argc, char* argv[])
collect_index_params(argv, StridesD, dims_arg_num + 18, 4); collect_index_params(argv, StridesD, dims_arg_num + 18, 4);
} }
using F16 = ck::half_t; using F32 = float;
using BF16 = ck::bhalf_t; using F64 = double;
using F32 = float;
using F64 = double; auto profile = [&](auto a_layout, auto b_layout, auto cde_layout, auto type) {
using ALayout = decltype(a_layout);
auto profile = using BLayout = decltype(b_layout);
[&](auto a_layout, auto b_layout, auto cde_layout, auto type, auto compute_type) { using CDELayout = decltype(cde_layout);
using ALayout = decltype(a_layout);
using BLayout = decltype(b_layout); using DataType = decltype(type);
using CDELayout = decltype(cde_layout);
if(default_strides)
using DataType = decltype(type);
using ComputeDataType = decltype(compute_type);
if(default_strides)
{
assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
}
bool pass = ck::profiler::profile_contraction_impl<ALayout,
BLayout,
CDELayout,
DataType,
ComputeDataType,
ck::Tuple<>,
Scale>(do_verification,
init_method,
do_log,
time_kernel,
Scale{alpha},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
};
auto run_profile_for_datatype = [&](auto type, auto compute_type) {
if(layout == ContractionMatrixLayout::MK_KN_MN_MN)
{
return profile(Row{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, type, compute_type);
}
else if(layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
return profile(Col{}, Col{}, Row{}, type, compute_type); assign_default_strides(a_layout, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(b_layout, StridesB, {K[0], K[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesE, {M[0], M[1], N[0], N[1]});
assign_default_strides(cde_layout, StridesD, {M[0], M[1], N[0], N[1]});
} }
return false;
bool pass = ck::profiler::
profile_contraction_impl<ALayout, BLayout, CDELayout, DataType, ck::Tuple<>, Scale>(
do_verification,
init_method,
do_log,
time_kernel,
Scale{alpha},
M,
N,
K,
StridesA,
StridesB,
StridesE,
StridesD);
return pass;
}; };
if(data_type == ContractionDataType::F32_F32_F32_F32) if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_KN_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Row{}, Row{}, Row{}, F32{});
{
return run_profile_for_datatype(F32{}, F32{});
}
else if(compute_data_type == ContractionComputeDataType::F16)
{
return run_profile_for_datatype(F32{}, F16{});
}
else if(compute_data_type == ContractionComputeDataType::BF16)
{
return run_profile_for_datatype(F32{}, BF16{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F64_F64_F64_F64) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::MK_NK_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F64) return profile(Row{}, Col{}, Row{}, F32{});
{
return run_profile_for_datatype(F64{}, F64{});
}
else if(compute_data_type == ContractionComputeDataType::F32)
{
return run_profile_for_datatype(F64{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::F16_F16_F16_F16) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Col{}, Row{}, Row{}, F32{});
{
return run_profile_for_datatype(F16{}, F32{});
}
else
{
std::cout << "Incorrect combination of data type and compute data type." << std::endl;
return 1;
}
} }
else if(data_type == ContractionDataType::BF16_BF16_BF16_BF16) else if(data_type == ContractionDataType::F32_F32_F32_F32 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{ {
if(compute_data_type == ContractionComputeDataType::F32) return profile(Col{}, Col{}, Row{}, F32{});
{ }
return run_profile_for_datatype(BF16{}, F32{}); else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
} layout == ContractionMatrixLayout::MK_KN_MN_MN)
else {
{ return profile(Row{}, Row{}, Row{}, F64{});
std::cout << "Incorrect combination of data type and compute data type." << std::endl; }
return 1; else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
} layout == ContractionMatrixLayout::MK_NK_MN_MN)
{
return profile(Row{}, Col{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_KN_MN_MN)
{
return profile(Col{}, Row{}, Row{}, F64{});
}
else if(data_type == ContractionDataType::F64_F64_F64_F64 &&
layout == ContractionMatrixLayout::KM_NK_MN_MN)
{
return profile(Col{}, Col{}, Row{}, F64{});
}
else
{
std::cout << "this data_type & layout is not implemented" << std::endl;
return 1;
} }
return 1;
} }
REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale); REGISTER_PROFILER_OPERATION(OP_NAME, OP_DESC, profile_contraction_scale);
profiler/src/profile_grouped_conv_bwd_weight.cpp
View file @ 082cf643
...@@ -20,9 +20,10 @@ enum struct ConvLayout ...@@ -20,9 +20,10 @@ enum struct ConvLayout
enum struct ConvDataType enum struct ConvDataType
{ {
F32_F32_F32, // 0 F32_F32_F32, // 0
F16_F16_F16, // 1 F16_F16_F16, // 1
BF16_F32_BF16, // 2 BF16_F32_BF16, // 2
F16_F16_F16_BF8_F8 // 3
}; };
#define OP_NAME "grouped_conv_bwd_weight" #define OP_NAME "grouped_conv_bwd_weight"
...@@ -33,7 +34,8 @@ static void print_helper_msg() ...@@ -33,7 +34,8 @@ static void print_helper_msg()
std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n" std::cout << "arg1: tensor operation (" OP_NAME ": " OP_DESC ")\n"
<< "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n" << "arg2: data type (0: Input fp32, Weight fp32, Output fp32\n"
<< " 1: Input fp16, Weight fp16, Output fp16\n" << " 1: Input fp16, Weight fp16, Output fp16\n"
<< " 2: Input bf16, Weight fp32, Output bf16)\n" << " 2: Input bf16, Weight fp32, Output bf16\n"
<< " 3: Input fp16, Weight fp16, Output fp16, Gemm bf8@fp8)\n"
<< "arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, " << "arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, "
"N, K, Ho, Wo]\n" "N, K, Ho, Wo]\n"
<< " 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, " << " 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, "
...@@ -82,6 +84,12 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -82,6 +84,12 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
using F32 = float; using F32 = float;
using F16 = ck::half_t; using F16 = ck::half_t;
using BF16 = ck::bhalf_t; using BF16 = ck::bhalf_t;
#ifdef CK_ENABLE_FP8
using F8 = ck::f8_t;
#endif
#ifdef CK_ENABLE_BF8
using BF8 = ck::bf8_t;
#endif
using namespace ck::tensor_layout::convolution; using namespace ck::tensor_layout::convolution;
...@@ -95,7 +103,9 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -95,7 +103,9 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
auto out_layout, auto out_layout,
auto in_type, auto in_type,
auto wei_type, auto wei_type,
auto out_type) { auto out_type,
auto compute_type_a,
auto compute_type_b) {
constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value; constexpr ck::index_t NDimSpatial = num_dim_spatial_tmp.value;
using InLayout = decltype(in_layout); using InLayout = decltype(in_layout);
...@@ -106,13 +116,18 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -106,13 +116,18 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
using WeiDataType = decltype(wei_type); using WeiDataType = decltype(wei_type);
using OutDataType = decltype(out_type); using OutDataType = decltype(out_type);
using ComputeTypeA = decltype(compute_type_a);
using ComputeTypeB = decltype(compute_type_b);
bool pass = ck::profiler::profile_grouped_conv_bwd_weight_impl<NDimSpatial, bool pass = ck::profiler::profile_grouped_conv_bwd_weight_impl<NDimSpatial,
InLayout, InLayout,
WeiLayout, WeiLayout,
OutLayout, OutLayout,
InDataType, InDataType,
WeiDataType, WeiDataType,
OutDataType>( OutDataType,
ComputeTypeA,
ComputeTypeB>(
do_verification, init_method, do_log, time_kernel, params, split_k); do_verification, init_method, do_log, time_kernel, params, split_k);
return pass ? 0 : 1; return pass ? 0 : 1;
...@@ -122,80 +137,84 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[]) ...@@ -122,80 +137,84 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
{ {
return profile(I1, GNWC{}, GKXC{}, GNWK{}, F32{}, F32{}, F32{}); return profile(I1, GNWC{}, GKXC{}, GNWK{}, F32{}, F32{}, F32{}, F32{}, F32{});
} }
else if(data_type == ConvDataType::F16_F16_F16) else if(data_type == ConvDataType::F16_F16_F16)
{ {
return profile(I1, GNWC{}, GKXC{}, GNWK{}, F16{}, F16{}, F16{}); return profile(I1, GNWC{}, GKXC{}, GNWK{}, F16{}, F16{}, F16{}, F16{}, F16{});
} }
else if(data_type == ConvDataType::BF16_F32_BF16) else if(data_type == ConvDataType::BF16_F32_BF16)
{ {
// fp32 atomic add is used for weight tensor in bf16 kernel // fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I1, GNWC{}, GKXC{}, GNWK{}, BF16{}, F32{}, BF16{}); return profile(I1, GNWC{}, GKXC{}, GNWK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
} }
} }
else if(num_dim_spatial == 2 && layout == ConvLayout::GNHWC_GKYXC_GNHWK) else if(num_dim_spatial == 2 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
{ {
return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, F32{}, F32{}, F32{}); return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, F32{}, F32{}, F32{}, F32{}, F32{});
} }
else if(data_type == ConvDataType::F16_F16_F16) else if(data_type == ConvDataType::F16_F16_F16)
{ {
return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, F16{}, F16{}, F16{}); return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, F16{}, F16{}, F16{}, F16{}, F16{});
} }
else if(data_type == ConvDataType::BF16_F32_BF16) else if(data_type == ConvDataType::BF16_F32_BF16)
{ {
// fp32 atomic add is used for weight tensor in bf16 kernel // fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, F32{}, BF16{}); return profile(I2, GNHWC{}, GKYXC{}, GNHWK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
} }
} }
else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK) else if(num_dim_spatial == 2 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
{ {
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}); return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
} }
else if(data_type == ConvDataType::F16_F16_F16) else if(data_type == ConvDataType::F16_F16_F16)
{ {
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{}); return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
} }
else if(data_type == ConvDataType::BF16_F32_BF16) else if(data_type == ConvDataType::BF16_F32_BF16)
{ {
// fp32 atomic add is used for weight tensor in bf16 kernel // fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{}); return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
} }
} }
else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK) else if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
{ {
return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, F32{}, F32{}, F32{}); return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, F32{}, F32{}, F32{}, F32{}, F32{});
} }
else if(data_type == ConvDataType::F16_F16_F16) else if(data_type == ConvDataType::F16_F16_F16)
{ {
return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, F16{}, F16{}, F16{}); return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, F16{}, F16{}, F16{}, F16{}, F16{});
} }
else if(data_type == ConvDataType::BF16_F32_BF16) else if(data_type == ConvDataType::BF16_F32_BF16)
{ {
// fp32 atomic add is used for weight tensor in bf16 kernel // fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, BF16{}, F32{}, BF16{}); return profile(I3, GNDHWC{}, GKZYXC{}, GNDHWK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
} }
} }
else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK) else if(num_dim_spatial == 3 && layout == ConvLayout::NHWGC_GKYXC_NHWGK)
{ {
if(data_type == ConvDataType::F32_F32_F32) if(data_type == ConvDataType::F32_F32_F32)
{ {
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}); return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F32{}, F32{}, F32{}, F32{}, F32{});
} }
else if(data_type == ConvDataType::F16_F16_F16) else if(data_type == ConvDataType::F16_F16_F16)
{ {
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}); return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, F16{}, F16{});
} }
else if(data_type == ConvDataType::BF16_F32_BF16) else if(data_type == ConvDataType::BF16_F32_BF16)
{ {
// fp32 atomic add is used for weight tensor in bf16 kernel // fp32 atomic add is used for weight tensor in bf16 kernel
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, F32{}, BF16{}); return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
}
else if(data_type == ConvDataType::F16_F16_F16_BF8_F8)
{
return profile(I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, F16{}, F16{}, F16{}, BF8{}, F8{});
} }
} }
......
test/CMakeLists.txt
View file @ 082cf643
...@@ -32,7 +32,7 @@ function(add_test_executable TEST_NAME) ...@@ -32,7 +32,7 @@ function(add_test_executable TEST_NAME)
set(test 0) set(test 0)
break() break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1)) NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal #if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1) set(test 1)
...@@ -61,7 +61,7 @@ function(add_test_executable TEST_NAME) ...@@ -61,7 +61,7 @@ function(add_test_executable TEST_NAME)
set(result 0) set(result 0)
endif() endif()
#message("add_test returns ${result}") #message("add_test returns ${result}")
return(PROPAGATE result) set(result ${result} PARENT_SCOPE)
endfunction(add_test_executable TEST_NAME) endfunction(add_test_executable TEST_NAME)
include(GoogleTest) include(GoogleTest)
...@@ -91,7 +91,7 @@ function(add_gtest_executable TEST_NAME) ...@@ -91,7 +91,7 @@ function(add_gtest_executable TEST_NAME)
set(test 0) set(test 0)
break() break()
elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR elseif((source MATCHES "fp8" OR source MATCHES "fp32" OR source MATCHES "fp64" OR source MATCHES "bf16" OR source MATCHES "int8" OR source MATCHES "fp16" OR
source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND source MATCHES "_f8" OR source MATCHES "_f32" OR source MATCHES "_f64" OR source MATCHES "_i8" OR source MATCHES "_f16" OR source MATCHES "_b16") AND
NOT(source MATCHES type OR source MATCHES type1)) NOT(source MATCHES type OR source MATCHES type1))
#if filename contains a type which doesn't match any selected type, mark it for removal #if filename contains a type which doesn't match any selected type, mark it for removal
set(test 1) set(test 1)
...@@ -123,7 +123,7 @@ function(add_gtest_executable TEST_NAME) ...@@ -123,7 +123,7 @@ function(add_gtest_executable TEST_NAME)
set(result 0) set(result 0)
endif() endif()
#message("add_gtest returns ${result}") #message("add_gtest returns ${result}")
return(PROPAGATE result) set(result ${result} PARENT_SCOPE)
endfunction(add_gtest_executable TEST_NAME) endfunction(add_gtest_executable TEST_NAME)
add_subdirectory(magic_number_division) add_subdirectory(magic_number_division)
......
test/contraction/test_contraction.cpp
View file @ 082cf643
...@@ -10,12 +10,9 @@ ...@@ -10,12 +10,9 @@
#include <gtest/gtest.h> #include <gtest/gtest.h>
#include "profiler/profile_contraction_impl.hpp" #include "profiler/profile_contraction_impl.hpp"
#include "profiler/profile_contraction_utils.hpp"
using F16 = ck::half_t; using F32 = float;
using BF16 = ck::bhalf_t; using F64 = double;
using F32 = float;
using F64 = double;
using Row = ck::tensor_layout::gemm::RowMajor; using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor; using Col = ck::tensor_layout::gemm::ColumnMajor;
...@@ -23,49 +20,49 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -23,49 +20,49 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using Bilinear = ck::tensor_operation::element_wise::Bilinear; using Bilinear = ck::tensor_operation::element_wise::Bilinear;
using Scale = ck::tensor_operation::element_wise::Scale; using Scale = ck::tensor_operation::element_wise::Scale;
struct Dimensions struct MemoryParams
{ {
std::vector<ck::index_t> M; std::vector<ck::index_t> M;
std::vector<ck::index_t> N; std::vector<ck::index_t> N;
std::vector<ck::index_t> K; std::vector<ck::index_t> K;
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
}; };
template <typename Tuple> template <typename Tuple>
class TestContraction : public ::testing::Test class TestContraction : public ::testing::Test
{ {
protected: protected:
using ALayout = std::tuple_element_t<0, Tuple>; using ALayout = std::tuple_element_t<0, Tuple>;
using BLayout = std::tuple_element_t<1, Tuple>; using BLayout = std::tuple_element_t<1, Tuple>;
using CDLayout = std::tuple_element_t<2, Tuple>; using CDLayout = std::tuple_element_t<2, Tuple>;
using DataType = std::tuple_element_t<3, Tuple>; using DataType = std::tuple_element_t<3, Tuple>;
using DTupleDataType = std::tuple_element_t<4, Tuple>; using DTupleDataType = std::tuple_element_t<4, Tuple>;
using ComputeDataType = std::tuple_element_t<5, Tuple>; using CDElementOp = std::tuple_element_t<5, Tuple>;
using CDElementOp = std::tuple_element_t<6, Tuple>;
std::vector<MemoryParams> list_of_memory_params = {{{32, 32},
std::vector<Dimensions> dimension_list = {{{32, 32}, {32, 32}, {32, 32}}, {32, 32},
{{16, 16}, {32, 32}, {16, 16}}}; {32, 32},
{32768, 1024, 32, 1},
std::vector<ck::index_t> init_methods = {1, 2}; {32768, 1024, 32, 1},
{32768, 1024, 32, 1},
{32768, 1024, 32, 1}},
{{16, 16},
{32, 32},
{16, 16},
{4096, 256, 16, 1},
{16, 1, 8192, 256},
{16384, 1024, 32, 1},
{16384, 1024, 32, 1}}};
std::vector<ck::index_t> init_methods = {0, 1, 2};
std::unique_ptr<CDElementOp> p_cd_element_op; std::unique_ptr<CDElementOp> p_cd_element_op;
void Run() void Run()
{ {
for(auto& dimension_params : dimension_list) for(auto& memory_params : list_of_memory_params)
{ {
std::vector<ck::index_t> StridesA;
std::vector<ck::index_t> StridesB;
std::vector<ck::index_t> StridesC;
std::vector<ck::index_t> StridesD;
const auto& M = dimension_params.M;
const auto& N = dimension_params.N;
const auto& K = dimension_params.K;
assign_default_strides(ALayout{}, StridesA, {M[0], M[1], K[0], K[1]});
assign_default_strides(BLayout{}, StridesB, {N[0], N[1], K[0], K[1]});
assign_default_strides(CDLayout{}, StridesC, {M[0], M[1], N[0], N[1]});
assign_default_strides(CDLayout{}, StridesD, {M[0], M[1], N[0], N[1]});
for(const ck::index_t init_method : init_methods) for(const ck::index_t init_method : init_methods)
{ {
bool pass = bool pass =
...@@ -73,20 +70,19 @@ class TestContraction : public ::testing::Test ...@@ -73,20 +70,19 @@ class TestContraction : public ::testing::Test
BLayout, BLayout,
CDLayout, CDLayout,
DataType, DataType,
ComputeDataType,
DTupleDataType, DTupleDataType,
CDElementOp>(true /*do_verification*/, CDElementOp>(true /*do_verification*/,
init_method, init_method,
false /*do_logs*/, false /*do_logs*/,
false /*time_kernel*/, false /*time_kernel*/,
*p_cd_element_op, *p_cd_element_op,
dimension_params.M, memory_params.M,
dimension_params.N, memory_params.N,
dimension_params.K, memory_params.K,
StridesA, memory_params.StridesA,
StridesB, memory_params.StridesB,
StridesC, memory_params.StridesC,
StridesD); memory_params.StridesD);
EXPECT_TRUE(pass); EXPECT_TRUE(pass);
} }
} }
...@@ -103,18 +99,24 @@ class TestContractionBilinear : public TestContraction<Tuple> ...@@ -103,18 +99,24 @@ class TestContractionBilinear : public TestContraction<Tuple>
{ {
}; };
#define ALL_LAYOUT_COMBINATIONS(dt, tuple_dt, compute_dt, op) \
std::tuple<Row, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Row, Col, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Row, Row, dt, tuple_dt, compute_dt, op>, \
std::tuple<Col, Col, Row, dt, tuple_dt, compute_dt, op>
using BilinearKernelTypes = using BilinearKernelTypes =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F32, Bilinear), ::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F64, Bilinear)>; std::tuple<Row, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F32, ck::Tuple<F32>, Bilinear>,
using ScaleKernelTypes = ::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F32, Scale), std::tuple<Col, Col, Row, F32, ck::Tuple<F32>, Bilinear>,
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F64, Scale)>; std::tuple<Row, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Row, Col, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Row, Row, F64, ck::Tuple<F32>, Bilinear>,
std::tuple<Col, Col, Row, F64, ck::Tuple<F32>, Bilinear>>;
using ScaleKernelTypes = ::testing::Types<std::tuple<Row, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F32, ck::Tuple<>, Scale>,
std::tuple<Row, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Row, Col, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Row, Row, F64, ck::Tuple<>, Scale>,
std::tuple<Col, Col, Row, F64, ck::Tuple<>, Scale>>;
TYPED_TEST_SUITE(TestContractionBilinear, BilinearKernelTypes); TYPED_TEST_SUITE(TestContractionBilinear, BilinearKernelTypes);
TYPED_TEST_SUITE(TestContractionScale, ScaleKernelTypes); TYPED_TEST_SUITE(TestContractionScale, ScaleKernelTypes);
...@@ -134,46 +136,3 @@ TYPED_TEST(TestContractionScale, scale) ...@@ -134,46 +136,3 @@ TYPED_TEST(TestContractionScale, scale)
this->p_cd_element_op = std::make_unique<Scale>(0.5f); this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run(); this->Run();
} }
template <typename Tuple>
class TestContractionScaleMixedPrecision : public TestContraction<Tuple>
{
};
template <typename Tuple>
class TestContractionBilinearMixedPrecision : public TestContraction<Tuple>
{
};
using BilinearKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, F16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<F32>, BF16, Bilinear),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<F64>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<F16>, F32, Bilinear),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<BF16>, F32, Bilinear)>;
using ScaleKernelTypesMixedPrecision =
::testing::Types<ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, F16, Scale),
ALL_LAYOUT_COMBINATIONS(F32, ck::Tuple<>, BF16, Scale),
ALL_LAYOUT_COMBINATIONS(F64, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(F16, ck::Tuple<>, F32, Scale),
ALL_LAYOUT_COMBINATIONS(BF16, ck::Tuple<>, F32, Scale)>;
TYPED_TEST_SUITE(TestContractionBilinearMixedPrecision, BilinearKernelTypesMixedPrecision);
TYPED_TEST_SUITE(TestContractionScaleMixedPrecision, ScaleKernelTypesMixedPrecision);
TYPED_TEST(TestContractionBilinearMixedPrecision, bilinear)
{
this->p_cd_element_op = std::make_unique<Bilinear>(1.f, 1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Bilinear>(-0.5f, 0.5f);
this->Run();
}
TYPED_TEST(TestContractionScaleMixedPrecision, scale)
{
this->p_cd_element_op = std::make_unique<Scale>(1.f);
this->Run();
this->p_cd_element_op = std::make_unique<Scale>(0.5f);
this->Run();
}
test/contraction/test_contraction_interface.cpp
View file @ 082cf643
...@@ -34,11 +34,11 @@ class ContractionInstanceWrapper ...@@ -34,11 +34,11 @@ class ContractionInstanceWrapper
static constexpr ck::index_t NumDim = 2; static constexpr ck::index_t NumDim = 2;
// clang-format off // clang-format off
using ContractionDeviceInstance = ck::tensor_operation::device:: using ContractionDeviceInstance = ck::tensor_operation::device::
//#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| Compute| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer| //#####################################| NumDimM| NumDimN| NumDimK| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#####################################| | | | Type| Type| Type| DataType| Type| Type| Data| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| 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_MWaveMPerXdl| ScalarPerVector| //#####################################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| 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_MWaveMPerXdl| ScalarPerVector|
//#####################################| | | | | | | | | | Type| Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl| //#####################################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //#####################################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector>; DeviceContractionMultipleD_Xdl_CShuffle< NumDim, NumDim, NumDim, F32, F32, F32, F32, ck::Tuple<F32>, F32, Pass, Pass, Bilinear, GemmSpec, 1, 256, 256, 128, 16, 4, 4, 32, 32, 4, 2, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, ABlockTransferSrcVectorDim, 4, 4, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, BBlockTransferSrcVectorDim, 4, 4, 1, 1, 1, S<1, 16, 1, 16>, CDEBlockTransferScalarPerVector>;
// clang-format on // clang-format on
bool isSupported(std::vector<ck::index_t>& ADims, bool isSupported(std::vector<ck::index_t>& ADims,
......
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