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Unverified Commit 58d75b7a authored by M.Emin Ozturk's avatar M.Emin Ozturk Committed by GitHub
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Merge branch 'develop' into gemm_bf16_sk_muozturk

parents 7ed95722 627a27bd
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.github/CONTRIBUTING.md 0 → 100644
View file @ 58d75b7a
We'd love for you to contribute to our source code!
Some helpful links:
- [Code of Conduct guidelines](https://www.contributor-covenant.org/version/2/1/code_of_conduct/code_of_conduct.txt)
- [New issue guidelines](https://github.com/rocm/composable_kernel/blob/develop/.github/ISSUE_TEMPLATE.md)
- [Submitting a pull request guidelines](https://github.com/rocm/composable_kernel/blob/develop/.github/PULL_REQUEST_TEMPLATE.md)
- [Maintainers](https://github.com/rocm/composable_kernel/blob/develop/CONTRIBUTORS.md)
- [General information](https://github.com/rocm/composable_kernel/blob/develop/README.md)
- [ROCm documentation](https://rocm.docs.amd.com/en/latest/how-to/llm-fine-tuning-optimization/optimizing-with-composable-kernel.html)
\ No newline at end of file
.github/ISSUE_TEMPLATE.md 0 → 100644
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When creating an issue, please check if a similar issue already exists.
### When reporting a bug, please include:
- [ ] A descriptive title
- [ ] An isolated way to reproduce the behavior (preferably a docker container with a repro)
- [ ] ROCm version, clang version, Composable Kernel commit pin
- [ ] Environment variables
- [ ] The behavior you expect to see, and the behavior you actually see
### When requesting a feature, please include:
- [ ] A descriptive title
- [ ] A detailed description of the problem you are trying to solve
- [ ] An overview of the suggested solution
- [ ] Explanation why the solution is an improvement
\ No newline at end of file
.github/PULL_REQUEST_TEMPLATE.md 0 → 100644
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## Proposed changes
Please describe the motivation behind the pull request, whether it enables a new feature or fixes a bug. If there are associated pull requests or issues, please link them to the pull request.
## Checklist
Please put an `x` into the boxes that apply. You can also fill these out after creating the PR. If you're not sure, please don't hesitate to ask.
- [ ] I have added tests relevant to the introduced functionality, and the unit tests are passing locally
- [ ] I have added inline documentation which enables the maintainers with understanding the motivation
- [ ] I have removed the stale documentation which is no longer relevant after this pull request
- [ ] (If this change is user-facing) I have added release notes which provide the end users with a brief summary of the improvement from this pull request
- [ ] I have run `clang-format` on all changed files
- [ ] Any dependent changes have been merged
## Discussion
If this is a relatively large or complex change, feel free to start a discussion by explaining why you chose the solution you did and what alternatives you considered
Dockerfile
View file @ 58d75b7a
FROM ubuntu:20.04
FROM ubuntu:22.04
ARG DEBIAN_FRONTEND=noninteractive
ARG ROCMVERSION=6.3
ARG compiler_version=""
......@@ -48,6 +48,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
libnuma-dev \
libpthread-stubs0-dev \
llvm-amdgpu \
mpich \
net-tools \
pkg-config \
python \
......@@ -63,6 +64,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
nano \
zlib1g-dev \
zip \
libzstd-dev \
openssh-server \
clang-format-12 \
kmod && \
......@@ -70,7 +72,7 @@ RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-
rm -rf /var/lib/apt/lists/* && \
rm -rf amdgpu-install* && \
# Remove unnecessary rocm components that take a lot of space
apt-get remove -y rocblas rocfft rocsparse composablekernel-dev
apt-get remove -y rocblas rocfft rocsparse composablekernel-dev hipblaslt
# Update the cmake to version 3.27.5
RUN pip install --upgrade cmake==3.27.5 && \
......@@ -92,7 +94,7 @@ RUN pip install --upgrade cmake==3.27.5 && \
dpkg -i dumb-init_*.deb && rm dumb-init_*.deb && \
# Install packages for processing the performance results
pip3 install --upgrade pip && \
pip3 install sqlalchemy==1.4.46 pymysql pandas==2.0.3 setuptools-rust sshtunnel==0.4.0 && \
pip3 install sqlalchemy==2.0.36 pymysql pandas==2.2.3 setuptools-rust sshtunnel==0.4.0 && \
# Add render group
groupadd -f render && \
# Install the new rocm-cmake version
......
Dockerfile.compiler
View file @ 58d75b7a
ARG BASE_DOCKER="rocm/composable_kernel:ck_ub20.04_rocm6.3"
ARG BASE_DOCKER="rocm/composable_kernel:ck_ub22.04_rocm6.3"
FROM $BASE_DOCKER
ARG compiler_version=""
ARG compiler_commit=""
......
Jenkinsfile
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This diff is collapsed.
codegen/test/rtc/include/rtc/hip.hpp
View file @ 58d75b7a
......@@ -4,6 +4,7 @@
#include <hip/hip_runtime_api.h>
#include <memory>
#include <string>
#include <stdexcept>
namespace rtc {
......
example/24_batched_gemm/batched_gemm_xdl_bf16_v3.cpp
View file @ 58d75b7a
......@@ -78,14 +78,14 @@ using DeviceGemmInstance = ck::tensor_operation::device::DeviceBatchedGemmMultiD
2, // ABlockTransferSrcVectorDim
8, // ABlockTransferSrcScalarPerVector
8, // ABlockTransferDstScalarPerVector_AK1
1, // ABlockLdsExtraM
0, // ABlockLdsExtraM
S<4, 64, 1>, // BBlockTransferThreadClusterLengths_BK0_N_BK1
S<1, 0, 2>, // BBlockTransferThreadClusterArrangeOrder
S<1, 0, 2>, // BBlockTransferSrcAccessOrder
2, // BBlockTransferSrcVectorDim
8, // BBlockTransferSrcScalarPerVector
8, // BBlockTransferDstScalarPerVector_BK1
1, // BBlockLdsExtraN
0, // BBlockLdsExtraN
1, // CShuffleMXdlPerWavePerShuffle
1, // CShuffleNXdlPerWavePerShuffle
S<1, 32, 1, 8>, // CShuffleBlockTransferClusterLengths_MBlock_MPerBlock_NBlock_NPerBlock
......
example/ck_tile/01_fmha/codegen/cpp_symbol_map.py
View file @ 58d75b7a
......@@ -2,10 +2,17 @@
# Copyright (c) 2018-2024, Advanced Micro Devices, Inc. All rights reserved.
# generate kernel instances to speed up compilation
DTYPE_MAP = {
"fp16": "ck_tile::fp16_t",
"bf16": "ck_tile::bf16_t",
"fp8" : "ck_tile::fp8_t"
FWD_DTYPE_MAP = {
"fp16" : "FmhaFwdFp16",
"bf16" : "FmhaFwdBf16",
"fp8" : "FmhaFwdFp8",
"fp8fp16": "FmhaFwdFp8Fp16",
"fp8bf16": "FmhaFwdFp8Bf16"
}
BWD_DTYPE_MAP = {
"fp16": "FmhaBwdFp16",
"bf16": "FmhaBwdBf16"
}
MASK_IMPL = {
......
example/ck_tile/01_fmha/codegen/ops/fmha_bwd.py
View file @ 58d75b7a
......@@ -283,7 +283,7 @@ class FmhaBwdApiPool:
inners = inners + FMHA_BWD_API_INNER_DISPATCH.format(F_if=if_k, F_mode=MODE_MAP[trait.mode], F_pipeline_enum=BWD_DQDKDV_PIPELINE_ENUM_MAP[trait.pipeline],
F_mask_check=get_mask_check_map(self.mask_impl)[trait.mask], F_mask=get_mask_map(self.mask_impl)[trait.mask], F_bias_check=BIAS_CHECK_MAP[trait.bias],
F_bias=BIAS_MAP[trait.bias], F_dbias=BOOL_MAP[trait.dbias], F_dropout_check=DROPOUT_CHECK_MAP[trait.dropout], F_dropout=DROPOUT_MAP[trait.dropout],
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype],
F_scheck=trait.scheck(spad1=spad1), F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_hdim=hdim, F_dtype=BWD_DTYPE_MAP[dtype],
F_spad0=BOOL_MAP[trait.spad], F_spad1=BOOL_MAP[spad1], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_deterministic=BOOL_MAP[trait.deterministic])
......@@ -360,7 +360,7 @@ class FmhaBwdDQDKDVKernel:
FMHA_BWD_DQ_DK_DV_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0,
......@@ -469,7 +469,7 @@ def get_bwd_dq_dk_dv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list()
api_pool = FmhaBwdApiPool(mask_impl)
for dtype in DTYPE_MAP.keys():
for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None:
continue
......@@ -585,7 +585,7 @@ class FmhaBwdOGradDotOKernel:
FMHA_BWD_DOT_DO_O_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_spad = BOOL_MAP[self.F_spad],
F_dvpad = BOOL_MAP[self.F_dvpad],
F_mode = MODE_MAP[self.F_mode],
......@@ -616,7 +616,7 @@ def get_bwd_dot_do_o_blobs() -> List[FmhaBwdOGradDotOKernel]:
gen = list()
for dtype in DTYPE_MAP.keys():
for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None:
continue
......@@ -716,7 +716,7 @@ class FmhaBwdConvertQGradKernel:
FMHA_BWD_CONVERT_DQ_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = BWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_bm0,
F_bn0 = self.F_bn0,
F_spad = BOOL_MAP[self.F_spad],
......@@ -751,7 +751,7 @@ def get_bwd_convert_dq_blobs() -> List[FmhaBwdConvertQGradKernel]:
gen = list()
for dtype in DTYPE_MAP.keys():
for dtype in BWD_DTYPE_MAP.keys():
d = get_fmha_bwd_dq_dk_dv_tile_ppl_dict_from_dtype(dtype)
if d == None:
continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd.py
View file @ 58d75b7a
......@@ -282,7 +282,7 @@ class FmhaFwdApiPool:
F_squant=BOOL_MAP[trait.squant], F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
......@@ -339,7 +339,7 @@ class FmhaFwdKernel:
FMHA_FWD_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0,
......@@ -462,6 +462,9 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm
# no need lse/dropout kernels
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(FmhaFwdPipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 'f', 'f', squant, mask))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else:
assert False
return pipelines
......@@ -469,7 +472,7 @@ def get_fwd_blobs(kernel_filter : Optional[str], receipt, mask_impl) -> Tuple[Fm
gen = list()
api_pool = FmhaFwdApiPool(mask_impl)
for dtype in DTYPE_MAP.keys():
for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_tile_dict_from_dtype(dtype)
if d == None:
continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_appendkv.py
View file @ 58d75b7a
......@@ -181,7 +181,7 @@ class FmhaFwdAppendKVApiPool:
inners = inners + FMHA_FWD_APPENDKV_API_INNER_DISPATCH.format(F_if=if_k, F_vlayout=LAYOUT_MAP[trait.vlayout],
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck, F_rope_check=ROPE_CHECK_MAP[trait.rope],
F_pagedkv=BOOL_MAP[trait.pagedkv], F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
F_rope=ROPE_MAP[trait.rope], F_bs=trait.bs, F_bsk=trait.bsk, F_bd=trait.bd, F_bdv=trait.bdv, F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
......@@ -216,7 +216,7 @@ class FmhaFwdAppendKVKernel:
FMHA_FWD_APPENDKV_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bs = self.F_tile.F_bs,
F_bsk = self.F_tile.F_bsk,
F_bd = self.F_tile.F_bd,
......@@ -301,6 +301,9 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
elif dtype in ['fp8', 'bf8']:
# rope/paged-kv is not supported
pipelines.append(FmhaFwdAppendKVPipeline('col', 't', 't', 't', 't', 'no', 'f'))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else:
assert False
return pipelines
......@@ -308,7 +311,7 @@ def get_fwd_appendkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list()
api_pool = FmhaFwdAppendKVApiPool(mask_impl)
for dtype in DTYPE_MAP.keys():
for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_appendkv_tile_dict_from_dtype(dtype)
if d == None:
continue
......
example/ck_tile/01_fmha/codegen/ops/fmha_fwd_splitkv.py
View file @ 58d75b7a
......@@ -435,7 +435,7 @@ class FmhaFwdSplitKVApiPool:
F_scheck=trait.scheck, F_skcheck=trait.skcheck, F_dcheck=trait.dcheck, F_dvcheck=trait.dvcheck,
F_spad=BOOL_MAP[trait.spad], F_skpad=BOOL_MAP[trait.skpad], F_dpad=BOOL_MAP[trait.dpad], F_dvpad=BOOL_MAP[trait.dvpad],
F_bm0=trait.bm0, F_bn0=trait.bn0, F_bk0=trait.bk0, F_bn1=trait.bn1, F_bk1=trait.bk1, F_bk0max=trait.bk0max,
F_hdim=hdim, F_dtype=DTYPE_MAP[dtype])
F_hdim=hdim, F_dtype=FWD_DTYPE_MAP[dtype])
if_j = 'if' if j == 0 else 'else if'
per_hdim_case = per_hdim_case + FMHA_FWD_API_PER_HDIM_CASE.format(F_if=if_j, F_hdim=hdim, F_inner_dispatch=inners)
if_i = 'if' if i == 0 else 'else if'
......@@ -472,7 +472,7 @@ class FmhaFwdSplitKVKernel:
FMHA_FWD_SPLITKV_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn0 = self.F_tile.F_bn0,
F_bk0 = self.F_tile.F_bk0,
......@@ -552,7 +552,7 @@ class FmhaFwdSplitKVCombineKernel:
FMHA_FWD_SPLITKV_COMBINE_KERNEL_BODY.format(
F_idx = self.F_idx,
F_hdim = self.F_hdim,
F_dtype = DTYPE_MAP[self.F_dtype],
F_dtype = FWD_DTYPE_MAP[self.F_dtype],
F_bm0 = self.F_tile.F_bm0,
F_bn1 = self.F_tile.F_bn1,
F_spad = BOOL_MAP[self.F_pipeline.F_spad],
......@@ -644,6 +644,9 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
elif dtype in ['fp8', 'bf8']:
for mask, bias in itertools.product(get_mask_map(mask_impl).keys(), BIAS_MAP.keys()):
pipelines.append(Pipeline('qr', 'col', 'f', 'f', 'f', 'f', bias, 't', squant, 'f', mask))
elif dtype in ['fp8fp16', 'fp8bf16']:
# TODO
None
else:
assert False
return pipelines
......@@ -651,7 +654,7 @@ def get_fwd_splitkv_blobs(kernel_filter : Optional[str], receipt, mask_impl) ->
gen = list()
api_pool = FmhaFwdSplitKVApiPool(mask_impl)
for dtype in DTYPE_MAP.keys():
for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_tile_dict_from_dtype(dtype)
if d == None:
continue
......@@ -711,7 +714,7 @@ def get_fwd_splitkv_combine_blobs(kernel_filter : Optional[str], receipt) -> Lis
gen = list()
for dtype in DTYPE_MAP.keys():
for dtype in FWD_DTYPE_MAP.keys():
d = get_fmha_fwd_splitkv_combine_tile_dict_from_dtype(dtype)
if d == None:
continue
......
example/ck_tile/01_fmha/fmha_bwd.cpp
View file @ 58d75b7a
......@@ -101,7 +101,7 @@ auto create_args(int argc, char* argv[])
}
// different threshold for different dtype
template <typename DataType>
template <typename DataTypeConfig>
auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/)
{
double rtol = 1e-2;
......@@ -110,7 +110,7 @@ auto get_elimit(ck_tile::index_t /*hdim_q*/, ck_tile::index_t /*hdim_v*/)
}
template <>
auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_v)
auto get_elimit<FmhaBwdBf16>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_v)
{
double rtol = 1e-2;
double atol = 1e-2;
......@@ -122,7 +122,7 @@ auto get_elimit<ck_tile::bf16_t>(ck_tile::index_t hdim_q, ck_tile::index_t hdim_
return ck_tile::make_tuple(rtol, atol);
}
template <typename DataType>
template <typename DataTypeConfig>
bool run(const ck_tile::ArgParser& arg_parser)
{
std::string data_type = arg_parser.get_str("prec");
......@@ -209,7 +209,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const auto seqstart_q_host = generate_seqstarts(mode, batch, seqlen_q);
const auto seqstart_k_host = generate_seqstarts(mode, batch, seqlen_k);
using TypeConfig = FmhaBwdTypeConfig<DataType>;
using TypeConfig = FmhaBwdTypeConfig<DataTypeConfig>;
using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType;
......@@ -933,7 +933,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(hdim_q, hdim_v);
auto [rtol, atol] = get_elimit<DataTypeConfig>(hdim_q, hdim_v);
bool dq_cur_pass = ck_tile::check_err(dq_host_result,
dq_host_ref,
std::string("Error: QGrad Incorrect results!"),
......@@ -986,11 +986,11 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16")
{
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
return run<FmhaBwdFp16>(arg_parser) ? 0 : -2;
}
else if(data_type == "bf16")
{
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
return run<FmhaBwdBf16>(arg_parser) ? 0 : -2;
}
return -3;
......
example/ck_tile/01_fmha/fmha_bwd.hpp
View file @ 58d75b7a
......@@ -14,11 +14,19 @@
#include <utility>
#include <variant>
struct FmhaBwdFp16
{
};
struct FmhaBwdBf16
{
};
template <typename DataType>
struct FmhaBwdTypeConfig;
template <>
struct FmhaBwdTypeConfig<ck_tile::half_t>
struct FmhaBwdTypeConfig<FmhaBwdFp16>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
......@@ -38,7 +46,7 @@ struct FmhaBwdTypeConfig<ck_tile::half_t>
};
template <>
struct FmhaBwdTypeConfig<ck_tile::bf16_t>
struct FmhaBwdTypeConfig<FmhaBwdBf16>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
......
example/ck_tile/01_fmha/fmha_fwd.cpp
View file @ 58d75b7a
......@@ -3,6 +3,7 @@
#include "fmha_fwd.hpp"
#include "ck_tile/host.hpp"
#include "ck_tile/ref/naive_attention.hpp"
#include "mask.hpp"
#include "rotary.hpp"
#include "utils.hpp"
......@@ -41,7 +42,7 @@ std::ostream& operator<<(std::ostream& os, const std::vector<T>& v)
auto create_args(int argc, char* argv[])
{
ck_tile::ArgParser arg_parser;
arg_parser.insert("v", "1", "weather do CPU validation or not")
arg_parser.insert("v", "1", "0:no validation, 2:cpu validation, 2:gpu validation(experimental)")
.insert("mode", "0", "kernel mode. 0:batch, 1:group")
.insert("b", "2", "batch size")
.insert("h", "8", "num of head, for q")
......@@ -142,7 +143,7 @@ auto create_args(int argc, char* argv[])
}
// different threshold for different dtype
template <typename DataType>
template <typename DataTypeConfig>
auto get_elimit(std::string /*init_method*/)
{
double rtol = 1e-3;
......@@ -151,7 +152,7 @@ auto get_elimit(std::string /*init_method*/)
}
template <>
auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
auto get_elimit<FmhaFwdBf16>(std::string /*init_method*/)
{
double rtol = 1e-2;
double atol = 1e-2;
......@@ -159,7 +160,7 @@ auto get_elimit<ck_tile::bf16_t>(std::string /*init_method*/)
}
template <>
auto get_elimit<ck_tile::fp8_t>(std::string init_method)
auto get_elimit<FmhaFwdFp8>(std::string init_method)
{
if(init_method == "ui" || init_method == "ni")
{
......@@ -261,7 +262,7 @@ int override_num_splits_if_necessary(
return num_splits;
}
template <typename DataType>
template <typename DataTypeConfig>
bool run(const ck_tile::ArgParser& arg_parser)
{
std::string data_type = arg_parser.get_str("prec");
......@@ -305,8 +306,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
ck_tile::index_t rotary_dim = arg_parser.get_int("rotary_dim");
if constexpr(!(std::is_same_v<DataType, ck_tile::fp16_t> ||
std::is_same_v<DataType, ck_tile::bf16_t>))
if constexpr(!(std::is_same_v<DataTypeConfig, FmhaFwdFp16> ||
std::is_same_v<DataTypeConfig, FmhaFwdBf16>))
{
if(0 < rotary_dim)
{
......@@ -428,25 +429,6 @@ bool run(const ck_tile::ArgParser& arg_parser)
return atoi(squant_str.c_str()) != 0 ? true : false;
}();
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<DataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / dtype_max) * (range_k / dtype_max);
scale_p = dtype_max / range_p;
// scale_p = [max(fp8_t)/range_o] * [range_p/max(fp8_t)] * [range_v/max(fp8_t)]
scale_o = range_p * range_v / range_o / dtype_max;
}
std::string vlayout = arg_parser.get_str("vlayout");
bool lse = arg_parser.get_bool("lse");
......@@ -466,7 +448,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
bool s_randval = false;
if(p_drop > 0.0f && do_validation)
if(p_drop > 0.0f && do_validation != 0)
{
s_randval = true;
}
......@@ -499,7 +481,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
const auto seqstart_k_host = to_seqstarts(seqlen_ks);
const auto seqstart_k_with_padding_host = to_seqstarts(seqlen_kpads);
using TypeConfig = FmhaFwdTypeConfig<DataType>;
using TypeConfig = FmhaFwdTypeConfig<DataTypeConfig>;
using QDataType = typename TypeConfig::QDataType;
using KDataType = typename TypeConfig::KDataType;
......@@ -513,6 +495,28 @@ bool run(const ck_tile::ArgParser& arg_parser)
using OaccDataType = typename TypeConfig::OaccDataType;
using ODataType = typename TypeConfig::ODataType;
float range_q = arg_parser.get_float("range_q");
float range_k = arg_parser.get_float("range_k");
float range_v = arg_parser.get_float("range_v");
float range_p = arg_parser.get_float("range_p");
float range_o = arg_parser.get_float("range_o");
float q_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<QDataType>::max());
float k_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<KDataType>::max());
float v_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<VDataType>::max());
float p_dtype_max = v_dtype_max; // assume p and v is the same type
float o_dtype_max = ck_tile::type_convert<float>(ck_tile::numeric<ODataType>::max());
float scale_p = 1.f;
float scale_o = 1.f;
if(squant)
{
scale_s = scale_s * (range_q / q_dtype_max) * (range_k / k_dtype_max);
scale_p = p_dtype_max / range_p;
scale_o = (o_dtype_max / range_o) * (range_p / p_dtype_max) * (range_v / v_dtype_max);
}
// accumulation numbers for performance evaluation
std::size_t flop = 0, num_byte = 0;
auto max_seqlen_q =
......@@ -709,14 +713,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
else if(init_method == "ufq" || init_method == "uf:q" ||
init_method == "3") // suitable for fp8 quantization
{
ck_tile::FillUniformDistribution<QDataType>{-dtype_max, dtype_max, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(k_host);
ck_tile::FillUniformDistribution<KDataType>{-dtype_max, dtype_max, seed}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(v_host);
ck_tile::FillUniformDistribution<VDataType>{-dtype_max, dtype_max, seed}(vnew_host);
ck_tile::FillUniformDistribution<QDataType>{-q_dtype_max, q_dtype_max, seed}(q_host);
ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(k_host);
ck_tile::FillUniformDistribution<KDataType>{-k_dtype_max, k_dtype_max, seed}(knew_host);
ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(v_host);
ck_tile::FillUniformDistribution<VDataType>{-v_dtype_max, v_dtype_max, seed}(vnew_host);
// bias_fp8 = qscale_bias * bias_fp32
float qscale_bias = (dtype_max / range_q) * (dtype_max / range_k);
float qscale_bias = (q_dtype_max / range_q) * (k_dtype_max / range_k);
// Assume bias is in [-1.f, 1.f] in original fp32
ck_tile::FillUniformDistribution<BiasDataType>{-qscale_bias, qscale_bias, seed}(bias_host);
}
......@@ -1118,25 +1122,75 @@ bool run(const ck_tile::ArgParser& arg_parser)
<< std::setprecision(2) << tflops << " TFlops, " << std::setprecision(2) << gb_per_sec
<< " GB/s" << std::flush;
if(!do_validation)
if(do_validation == 0)
{
std::cout << std::flush << std::endl;
return true;
}
if(do_validation == 2)
{
// NOTE: use gpu to do validation
ck_tile::naive_attention_fwd_traits naive_t;
naive_t.q_type = data_type;
naive_t.k_type = data_type;
naive_t.v_type = data_type;
naive_t.o_type = data_type;
naive_t.q_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.k_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.v_layout = i_perm == 1 ? "bhsd" : "bshd";
naive_t.o_layout = o_perm == 1 ? "bhsd" : "bshd";
naive_t.variation = 0; // TODO?
ck_tile::DeviceMem o_naive_buf(o_host.get_element_space_size_in_bytes());
ck_tile::naive_attention_fwd_args naive_a;
naive_a.q_ptr = q_buf.GetDeviceBuffer();
naive_a.k_ptr = k_buf.GetDeviceBuffer();
naive_a.v_ptr = v_buf.GetDeviceBuffer();
naive_a.o_ptr = o_naive_buf.GetDeviceBuffer();
naive_a.scale_s = scale_s;
naive_a.context_len_ptr = nullptr; // used when seqlen kv come from a pointer
naive_a.page_table_ptr =
nullptr; // [batch, num_blocks] seqlen_kv is in different block(paged attn)
naive_a.hdim = hdim_q;
naive_a.hdim_v = hdim_v; // could be cross-attn, where V and Q/K hdim are different
naive_a.batch_q = batch;
naive_a.batch_kv = batch;
naive_a.batch_ratio_kv = 1; // batch_q / batch_kv
naive_a.seqlen_q = seqlen_qs[0];
naive_a.seqlen_kv = seqlen_ks[0]; // if context_len_ptr is not nullptr, ignore this field
naive_a.nhead_q = nhead;
naive_a.nhead_kv = nhead_k;
naive_a.nhead_ratio_kv = naive_a.nhead_q / naive_a.nhead_kv; // nhead_q / nhead_kv
naive_a.page_size = 0; // if paged, the seqlen-kv for each block
ck_tile::stream_config naive_s{};
naive_attention_fwd(naive_t, naive_a, naive_s);
auto o_naive_ref = o_naive_buf.ToHost<ODataType>();
o_buf.FromDevice(o_host.data()); // TODO: ugly
auto [rtol_, atol_] = get_elimit<DataTypeConfig>(init_method);
bool pass_ = ck_tile::check_err(
o_host, o_naive_ref, std::string("OUT Error: Incorrect results!"), rtol_, atol_);
std::cout << ", valid:" << (pass_ ? "y" : "n") << std::flush << std::endl;
return pass_;
}
o_buf.FromDevice(o_host.data());
lse_buf.FromDevice(lse_host.data());
randval_buf.FromDevice(randval_host.data());
auto p_compute_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>)
if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::scales{scale_p};
else
return ck_tile::identity{};
}();
auto oacc_element_func = [&]() {
if constexpr(std::is_same_v<DataType, ck_tile::fp8_t>)
if constexpr(std::is_same_v<DataTypeConfig, ck_tile::fp8_t>)
return ck_tile::composes(ck_tile::saturates<ck_tile::fp8_t>{},
ck_tile::scales{scale_o});
else
......@@ -1458,7 +1512,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
else o_host_result.ForEach([&](auto& self, auto idx) { self(idx) = o_host(b_idx, idx[1] + query_offset, idx[0], idx[2]); });
// clang-format on
auto [rtol, atol] = get_elimit<DataType>(init_method);
auto [rtol, atol] = get_elimit<DataTypeConfig>(init_method);
bool cur_pass = ck_tile::check_err(
o_host_result, o_host_ref, std::string("OUT Error: Incorrect results!"), rtol, atol);
pass &= cur_pass;
......@@ -1515,15 +1569,15 @@ int main(int argc, char* argv[])
const std::string data_type = arg_parser.get_str("prec");
if(data_type == "fp16")
{
return run<ck_tile::half_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdFp16>(arg_parser) ? 0 : -2;
}
else if(data_type == "bf16")
{
return run<ck_tile::bf16_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdBf16>(arg_parser) ? 0 : -2;
}
else if(data_type == "fp8")
{
return run<ck_tile::fp8_t>(arg_parser) ? 0 : -2;
return run<FmhaFwdFp8>(arg_parser) ? 0 : -2;
}
return -3;
......
example/ck_tile/01_fmha/fmha_fwd.hpp
View file @ 58d75b7a
......@@ -16,11 +16,35 @@
#include <utility>
#include <variant>
struct FmhaFwdFp16
{
};
struct FmhaFwdBf16
{
};
struct FmhaFwdFp8
{
};
struct FmhaFwdBf8
{
};
struct FmhaFwdFp8Fp16
{
};
struct FmhaFwdFp8Bf16
{
};
template <typename DataType>
struct FmhaFwdTypeConfig;
template <>
struct FmhaFwdTypeConfig<ck_tile::half_t>
struct FmhaFwdTypeConfig<FmhaFwdFp16>
{
using QDataType = ck_tile::half_t;
using KDataType = ck_tile::half_t;
......@@ -36,7 +60,7 @@ struct FmhaFwdTypeConfig<ck_tile::half_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf16_t>
struct FmhaFwdTypeConfig<FmhaFwdBf16>
{
using QDataType = ck_tile::bf16_t;
using KDataType = ck_tile::bf16_t;
......@@ -52,7 +76,7 @@ struct FmhaFwdTypeConfig<ck_tile::bf16_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::fp8_t>
struct FmhaFwdTypeConfig<FmhaFwdFp8>
{
using QDataType = ck_tile::fp8_t;
using KDataType = ck_tile::fp8_t;
......@@ -68,7 +92,7 @@ struct FmhaFwdTypeConfig<ck_tile::fp8_t>
};
template <>
struct FmhaFwdTypeConfig<ck_tile::bf8_t>
struct FmhaFwdTypeConfig<FmhaFwdBf8>
{
using QDataType = ck_tile::bf8_t;
using KDataType = ck_tile::bf8_t;
......
example/ck_tile/12_smoothquant/example_smoothquant.cpp
View file @ 58d75b7a
......@@ -35,7 +35,8 @@ auto create_args(int argc, char* argv[])
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("stride", "-1", "stride per row, if -1 then equal to n")
.insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
.insert("e", "1e-5", "epsilon")
.insert("v", "1", "cpu validation or not")
.insert("prec", "fp16", "precision")
......@@ -51,9 +52,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t stride = arg_parser.get_int("stride");
if(stride < 0)
stride = n;
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
std::string data_type = arg_parser.get_str("prec");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
......@@ -68,14 +72,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
using ComputeDataType = float;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XScaleDataType> xscale_host({n});
ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
......@@ -116,7 +120,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.GetDeviceBuffer(),
m,
n,
stride};
x_stride,
y_stride};
auto kargs = Kernel::MakeKargs(args);
......@@ -133,7 +138,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
using YDataType = ComputeDataType;
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
// smooth outlier
{
auto f = [&](auto n_) {
......@@ -183,7 +188,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.FromDevice(qy_host_dev.data());
auto [rtol, atol] = get_elimit<QYDataType>();
if(stride == n)
if(y_stride == n)
{
pass = ck_tile::check_err(qy_host_dev,
qy_host_ref,
......@@ -195,10 +200,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
qy_host_dev.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
qy_host_ref.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
qy_host_dev.begin() + i_r * y_stride +
n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
qy_host_ref.begin() + i_r * y_stride +
n);
pass &= ck_tile::check_err(qy_host_dev_row,
qy_host_ref_row,
std::string("qy[") + std::to_string(i_r) +
......@@ -210,8 +217,9 @@ bool run(const ck_tile::ArgParser& arg_parser)
}
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride
<< ", valid:" << (pass ? "y" : "n") << std::flush << std::endl;
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride
<< ", y_stride:" << y_stride << ", valid:" << (pass ? "y" : "n") << std::flush
<< std::endl;
}
return pass;
......
example/ck_tile/12_smoothquant/smoothquant.cpp
View file @ 58d75b7a
......@@ -33,7 +33,8 @@ auto create_args(int argc, char* argv[])
ck_tile::ArgParser arg_parser;
arg_parser.insert("m", "3328", "m dimension")
.insert("n", "4096", "n dimension")
.insert("stride", "-1", "stride per row, if -1 then equal to n")
.insert("x_stride", "-1", "input stride per row, if -1 then equal to n")
.insert("y_stride", "-1", "output stride per row, if -1 then equal to n")
.insert("v", "1", "cpu validation or not")
.insert("kname", "1", "print kernel name or not")
.insert("prec", "fp16", "precision")
......@@ -49,16 +50,19 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
ck_tile::index_t m = arg_parser.get_int("m");
ck_tile::index_t n = arg_parser.get_int("n");
ck_tile::index_t stride = arg_parser.get_int("stride");
if(stride < 0)
stride = n;
ck_tile::index_t x_stride = arg_parser.get_int("x_stride");
if(x_stride < 0)
x_stride = n;
ck_tile::index_t y_stride = arg_parser.get_int("y_stride");
if(y_stride < 0)
y_stride = n;
std::string data_type = arg_parser.get_str("prec");
int kname = arg_parser.get_int("kname");
int do_validation = arg_parser.get_int("v");
int warmup = arg_parser.get_int("warmup");
int repeat = arg_parser.get_int("repeat");
assert(stride >= n);
assert(x_stride >= n);
using TypeConfig = SmoothquantTypeConfig<DataType>;
......@@ -69,14 +73,14 @@ bool run(const ck_tile::ArgParser& arg_parser)
using ComputeDataType = typename TypeConfig::ComputeDataType;
// host verify
ck_tile::HostTensor<XDataType> x_host({m, n}, {stride, 1});
ck_tile::HostTensor<XDataType> x_host({m, n}, {x_stride, 1});
ck_tile::HostTensor<XScaleDataType> xscale_host({n});
ck_tile::HostTensor<YScaleDataType> yscale_host_ref({m}, {1});
ck_tile::HostTensor<YScaleDataType> yscale_host_dev({m}, {1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_ref({m, n}, {y_stride, 1});
ck_tile::HostTensor<QYDataType> qy_host_dev({m, n}, {y_stride, 1});
ck_tile::FillUniformDistribution<XDataType>{-.5f, .5f}(x_host);
ck_tile::FillUniformDistribution<XScaleDataType>{1e-3, .5f}(xscale_host);
......@@ -90,7 +94,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
xscale_buf.ToDevice(xscale_host.data());
std::cout << "[" << data_type << "]"
<< " m:" << m << ", n:" << n << ", stride:" << stride << std::flush;
<< " m:" << m << ", n:" << n << ", x_stride:" << x_stride << ", y_stride:" << y_stride
<< std::flush;
smoothquant_traits traits{data_type};
......@@ -100,7 +105,8 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.GetDeviceBuffer(),
m,
n,
stride};
x_stride,
y_stride};
float ave_time = smoothquant(
traits, args, ck_tile::stream_config{nullptr, true, kname ? 1 : 0, warmup, repeat});
......@@ -116,7 +122,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
if(do_validation)
{
using YDataType = ComputeDataType;
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {stride, 1});
ck_tile::HostTensor<ComputeDataType> y_host({m, n}, {y_stride, 1});
// smooth outlier
{
auto f = [&](auto n_) {
......@@ -166,7 +172,7 @@ bool run(const ck_tile::ArgParser& arg_parser)
qy_buf.FromDevice(qy_host_dev.data());
auto [rtol, atol] = get_elimit<QYDataType>();
if(stride == n)
if(y_stride == n)
{
pass = ck_tile::check_err(qy_host_dev,
qy_host_ref,
......@@ -178,10 +184,12 @@ bool run(const ck_tile::ArgParser& arg_parser)
{
for(int i_r = 0; i_r < m; i_r++)
{
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * stride,
qy_host_dev.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * stride,
qy_host_ref.begin() + i_r * stride + n);
std::vector<QYDataType> qy_host_dev_row(qy_host_dev.begin() + i_r * y_stride,
qy_host_dev.begin() + i_r * y_stride +
n);
std::vector<QYDataType> qy_host_ref_row(qy_host_ref.begin() + i_r * y_stride,
qy_host_ref.begin() + i_r * y_stride +
n);
pass &= ck_tile::check_err(qy_host_dev_row,
qy_host_ref_row,
std::string("qy[") + std::to_string(i_r) +
......
include/ck/tensor_operation/gpu/device/device_base.hpp
View file @ 58d75b7a
......@@ -5,6 +5,8 @@
#include <string>
#include <sstream>
#include <regex>
#include <optional>
#include "ck/stream_config.hpp"
......@@ -12,6 +14,34 @@ namespace ck {
namespace tensor_operation {
namespace device {
#define GET_OBJECT_NAME_IMLP \
std::optional<std::string> GetObjectName() const override \
{ \
std::string str = __PRETTY_FUNCTION__; \
static std::regex obj_name_expr{"<std::string> (.*)::GetObjectName"}; \
std::smatch match; \
if(!std::regex_search(str, match, obj_name_expr)) \
{ \
return str; \
} \
return std::string(match[1]) + ';'; \
}
#define GET_TEMPLATE_INFO_IMPL \
std::optional<std::string> GetTemplateInfo() const override \
{ \
std::string str = __PRETTY_FUNCTION__; \
static std::regex template_expr{"\\[(.*)\\]"}; \
std::smatch match; \
if(!std::regex_search(str, match, template_expr)) \
{ \
return std::nullopt; \
} \
return std::string(match[1]); \
}
#define REGISTER_EXTRA_PRINTING_METHODS GET_OBJECT_NAME_IMLP GET_TEMPLATE_INFO_IMPL
struct BaseArgument
{
BaseArgument() = default;
......@@ -48,6 +78,10 @@ struct BaseOperator
virtual std::string GetTypeIdName() const { return typeid(*this).name(); }
virtual std::optional<std::string> GetObjectName() const { return std::nullopt; }
virtual std::optional<std::string> GetTemplateInfo() const { return std::nullopt; }
virtual std::string GetTypeIdHashCode() const
{
std::ostringstream oss;
......
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