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CUTLASS 2.10 (#615)

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Co-authored-by: Aniket Shivam <ashivam@nvidia.com>
This commit is contained in:
ANIKET SHIVAM
2022-09-03 15:48:46 -07:00
committed by GitHub
parent ca23ff7924
commit b72cbf957d
289 changed files with 43708 additions and 2513 deletions
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54
tools/library/scripts/pycutlass/src/cpp/test/conv/conv_problems.h Normal file
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief Bind convolution problems to python
*/
#pragma once
#include <pybind11/pybind11.h>
#include <pybind11/stl_bind.h>
#include "unit/conv/device/conv2d_problems.h"
#include "cutlass/conv/conv2d_problem_size.h"
namespace py = pybind11;
PYBIND11_MAKE_OPAQUE(std::vector<cutlass::conv::Conv2dProblemSize>);
void bind_conv_problem_size_test(py::module &m) {
py::bind_vector<std::vector<cutlass::conv::Conv2dProblemSize>>(m, "Conv2dProblemVector")
.def("size", &std::vector<cutlass::conv::Conv2dProblemSize>::size);
// Get Conv2d problem sizes
py::class_<test::conv::device::TestbedConv2dProblemSizes>(m, "TestbedConv2dProblemSizes")
.def(py::init<int>())
.def_readonly("conv2d_default_sizes", &test::conv::device::TestbedConv2dProblemSizes::conv2d_default_sizes);
}

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tools/library/scripts/pycutlass/src/cpp/test/conv/convolution.h Normal file
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief Bind convolution related types to python
*/
#pragma once
#include <pybind11/pybind11.h>
#include <pybind11/stl_bind.h>
#include "conv_problems.h"
#include "host.h"
namespace py = pybind11;
void bind_convolution_test(py::module &m) {
// Conv problem sizes
bind_conv_problem_size_test(m);
py::module_ host_submodule = m.def_submodule("host");
bind_conv_host_references(host_submodule);
}

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tools/library/scripts/pycutlass/src/cpp/test/conv/host.h Normal file
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief Bind Convolution host test helpers to python
*/
#pragma once
#include <pybind11/pybind11.h>
#include <pybind11/stl_bind.h>
#include "unit/conv/device/cache_testbed_output.h"
#include "cutlass/util/reference/host/convolution.h"
#include "cutlass/util/reference/host/tensor_compare.h"
namespace py = pybind11;
template<typename Ta, typename La, typename Tb, typename Lb, typename Tc, typename Lc, typename Tacc, typename Te>
void bind_conv2d_host(py::module &m) {
m.def("conv2d", \
&cutlass::reference::host::Conv2d< \
Ta, La, Tb, Lb, Tc, Lc, Te, Tacc>);
m.def("CreateCachedConv2dTestKey", &test::conv::device::CreateCachedConv2dTestKey<Ta, La, Tb, Lb, Tc, Lc, Tacc, Te>);
}
template<typename Ta, typename La, typename Tb, typename Lb, typename Tc, typename Lc, typename Tacc, typename Te>
void bind_conv2d_host_sat(py::module &m) {
m.def("conv2d", \
&cutlass::reference::host::Conv2d< \
Ta, La, Tb, Lb, Tc, Lc, Te, Tacc, cutlass::NumericConverterClamp<Tc, Te>>);
m.def("CreateCachedConv2dTestKey", &test::conv::device::CreateCachedConv2dTestKey<Ta, La, Tb, Lb, Tc, Lc, Tacc, Te>);
}
template<typename Ta, typename Tb, typename Tc, typename Tacc, typename Te>
void bind_conv2d_host_nhwc(py::module &m) {
bind_conv2d_host<
Ta, cutlass::layout::TensorNHWC,
Tb, cutlass::layout::TensorNHWC,
Tc, cutlass::layout::TensorNHWC,
Tacc, Te>(m);
}
template<typename Ta, typename Tb, typename Tc, typename Tacc, typename Te>
void bind_conv2d_host_nc32hw32(py::module &m) {
bind_conv2d_host_sat<
Ta, cutlass::layout::TensorNCxHWx<32>,
Tb, cutlass::layout::TensorCxRSKx<32>,
Tc, cutlass::layout::TensorNCxHWx<32>,
Tacc, Te>(m);
}
template<typename T, typename Layout>
void bind_tensor_equals(py::module &m) {
m.def("equals", py::overload_cast<
const cutlass::TensorView<T, Layout>&, const cutlass::TensorView<T, Layout>&>(
&cutlass::reference::host::TensorEquals<T, Layout>
));
}
#define BIND_TENSOR_HASH(Element, Layout) { \
m.def("TensorHash", &test::conv::device::TensorHash<Element, Layout>, py::arg("view"), py::arg("hash") = test::conv::device::CRC32(), py::arg("crc")=uint32_t()); \
}
void bind_conv_host_references(py::module &m) {
//
// Conv2d reference on host
// tools/util/include/cutlass/util/reference/host/convolution.h
/// double
bind_conv2d_host_nhwc<double, double, double, double, double>(m);
/// float
bind_conv2d_host_nhwc<float, float, float, float, float>(m);
/// half
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, cutlass::half_t, cutlass::half_t, cutlass::half_t>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, cutlass::half_t, float, cutlass::half_t>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, cutlass::half_t, float, float>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, cutlass::half_t, cutlass::half_t, float>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, float, cutlass::half_t, cutlass::half_t>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, float, float, cutlass::half_t>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, float, float, float>(m);
bind_conv2d_host_nhwc<cutlass::half_t, cutlass::half_t, float, cutlass::half_t, float>(m);
/// bfloat16
bind_conv2d_host_nhwc<cutlass::bfloat16_t, cutlass::bfloat16_t, cutlass::bfloat16_t, float, cutlass::bfloat16_t>(m);
bind_conv2d_host_nhwc<cutlass::bfloat16_t, cutlass::bfloat16_t, cutlass::bfloat16_t, float, float>(m);
bind_conv2d_host_nhwc<cutlass::bfloat16_t, cutlass::bfloat16_t, float, float, cutlass::bfloat16_t>(m);
bind_conv2d_host_nhwc<cutlass::bfloat16_t, cutlass::bfloat16_t, float, float, float>(m);
/// s8
bind_conv2d_host_nhwc<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_conv2d_host_nhwc<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_conv2d_host_nc32hw32<int8_t, int8_t, int32_t, int32_t, float>(m);
//
// Compare whether two tensors are equal
//
/// double
bind_tensor_equals<double, cutlass::layout::TensorNHWC>(m);
/// float
bind_tensor_equals<float, cutlass::layout::TensorNHWC>(m);
/// half
bind_tensor_equals<cutlass::half_t, cutlass::layout::TensorNHWC>(m);
/// bfloat16
bind_tensor_equals<cutlass::bfloat16_t, cutlass::layout::TensorNHWC>(m);
/// s32
bind_tensor_equals<int32_t, cutlass::layout::TensorNHWC>(m);
bind_tensor_equals<int32_t, cutlass::layout::TensorNCxHWx<32>>(m);
/// s8
bind_tensor_equals<int8_t, cutlass::layout::TensorNHWC>(m);
bind_tensor_equals<int8_t, cutlass::layout::TensorNCxHWx<32>>(m);
/// Cache
py::class_<test::conv::device::CachedTestKey>(m, "CachedTestKey")
.def(py::init<>())
.def(py::init<std::string, std::string, std::string, uint32_t, uint32_t, uint32_t>());
py::class_<test::conv::device::CachedTestResult>(m, "CachedTestResult")
.def(py::init<>())
.def(py::init<uint32_t>())
.def_readwrite("D", &test::conv::device::CachedTestResult::D);
py::class_<test::conv::device::CachedTestResultListing>(m, "CachedTestResultListing")
.def(py::init<const std::string &>())
.def("find", &test::conv::device::CachedTestResultListing::find)
.def("append", &test::conv::device::CachedTestResultListing::append)
.def("write", &test::conv::device::CachedTestResultListing::write);
py::class_<test::conv::device::CRC32>(m, "CRC32")
.def(py::init<>());
BIND_TENSOR_HASH(double, cutlass::layout::TensorNHWC)
BIND_TENSOR_HASH(float, cutlass::layout::TensorNHWC);
BIND_TENSOR_HASH(cutlass::half_t, cutlass::layout::TensorNHWC);
BIND_TENSOR_HASH(cutlass::bfloat16_t, cutlass::layout::TensorNHWC);
BIND_TENSOR_HASH(int32_t, cutlass::layout::TensorNHWC);
BIND_TENSOR_HASH(int8_t, cutlass::layout::TensorNCxHWx<32>);
}

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tools/library/scripts/pycutlass/src/cpp/test/gemm/gemm.h Normal file
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief Bind gemm test to python
*/
#pragma once
#include <pybind11/pybind11.h>
#include <pybind11/stl_bind.h>
#include "host.h"
namespace py = pybind11;
void bind_gemm_test(py::module &m) {
py::module_ host_submodule = m.def_submodule("host");
bind_gemm_host_reference(host_submodule);
}

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tools/library/scripts/pycutlass/src/cpp/test/gemm/host.h Normal file
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/***************************************************************************************************
* Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
*
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* 3. Neither the name of the copyright holder nor the names of its
* contributors may be used to endorse or promote products derived from
* this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
* AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
* FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
* DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
* SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
* OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
**************************************************************************************************/
/* \file
\brief Bind gemm test host functions to python
*/
#pragma once
#include <pybind11/pybind11.h>
#include <pybind11/stl_bind.h>
#include "cutlass/cutlass.h"
#include "cutlass/util/reference/host/gemm.h"
#include "cutlass/util/reference/host/tensor_compare.h"
#include "cutlass/util/host_reorder.h"
#include "cutlass/functional.h"
namespace py = pybind11;
template<
typename ElementA, typename LayoutA,
typename ElementB, typename LayoutB,
typename ElementC, typename LayoutC,
typename AccumulatorType, typename ComputeType,
typename InnerProductOp>
void bind_host_gemm_saturate(py::module &m) {
m.def("gemm_saturate", py::overload_cast<
cutlass::gemm::GemmCoord, ComputeType,
cutlass::TensorRef<ElementA, LayoutA>,
cutlass::TensorRef<ElementB, LayoutB>,
ComputeType,
cutlass::TensorRef<ElementC, LayoutC>,
cutlass::TensorRef<ElementC, LayoutC>,
AccumulatorType>(
&cutlass::reference::host::compute_gemm<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ComputeType,
AccumulatorType,
InnerProductOp,
cutlass::NumericConverterClamp<ElementC, AccumulatorType>>
));
}
template<
typename ElementA, typename LayoutA,
typename ElementB, typename LayoutB,
typename ElementC, typename LayoutC,
typename AccumulatorType, typename ComputeType,
typename InnerProductOp>
void bind_host_gemm(py::module &m) {
m.def("gemm", py::overload_cast<
cutlass::gemm::GemmCoord, ComputeType,
cutlass::TensorRef<ElementA, LayoutA>,
cutlass::TensorRef<ElementB, LayoutB>,
ComputeType,
cutlass::TensorRef<ElementC, LayoutC>,
cutlass::TensorRef<ElementC, LayoutC>,
AccumulatorType>(
&cutlass::reference::host::compute_gemm<
ElementA, LayoutA,
ElementB, LayoutB,
ElementC, LayoutC,
ComputeType,
AccumulatorType,
InnerProductOp,
cutlass::NumericConverter<ElementC, AccumulatorType>>
));
}
template<
typename ElementA, typename ElementB, typename ElementC,
typename AccumulatorType, typename ComputeType>
void bind_host_gemm_multiply_add(py::module &m) {
bind_host_gemm<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::RowMajor,
ComputeType, AccumulatorType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
}
template<
typename ElementA, typename ElementB, typename ElementC,
typename AccumulatorType, typename ComputeType>
void bind_host_gemm_multiply_add_saturate(py::module &m) {
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::RowMajor,
ComputeType, AccumulatorType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::RowMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::RowMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajor,
ElementB, cutlass::layout::ColumnMajor,
ElementC, cutlass::layout::ColumnMajor,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
}
template<
typename ElementA, typename ElementB, typename ElementC,
typename AccumulatorType, typename ComputeType>
void bind_host_gemm_multiply_add_interleaved(py::module &m) {
bind_host_gemm<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
ComputeType, AccumulatorType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
}
template<
typename ElementA, typename ElementB, typename ElementC,
typename AccumulatorType, typename ComputeType>
void bind_host_gemm_multiply_add_saturate_interleaved(py::module &m) {
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
ComputeType, AccumulatorType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::RowMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::RowMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::RowMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
bind_host_gemm_saturate<
ElementA, cutlass::layout::ColumnMajorInterleaved<32>,
ElementB, cutlass::layout::ColumnMajorInterleaved<32>,
ElementC, cutlass::layout::ColumnMajorInterleaved<32>,
AccumulatorType, ComputeType,
cutlass::multiply_add<AccumulatorType>>(m);
}
#define BIND_TENSOR_EQUAL(Element, Layout) { \
m.def("equals", py::overload_cast< \
const cutlass::TensorView<Element, Layout>&, const cutlass::TensorView<Element, Layout>&>( \
&cutlass::reference::host::TensorEquals<Element, Layout>)); \
}
void bind_gemm_host_reference(py::module &m) {
/// double
bind_host_gemm_multiply_add<double, double, double, double, double>(m);
/// float
bind_host_gemm_multiply_add<float, float, float, float, float>(m);
/// half_t
bind_host_gemm_multiply_add<cutlass::half_t, cutlass::half_t, cutlass::half_t, cutlass::half_t, cutlass::half_t>(m);
bind_host_gemm_multiply_add<cutlass::half_t, cutlass::half_t, cutlass::half_t, float, float>(m);
bind_host_gemm_multiply_add<cutlass::half_t, cutlass::half_t, float, cutlass::half_t, cutlass::half_t>(m);
bind_host_gemm_multiply_add<cutlass::half_t, cutlass::half_t, float, float, float>(m);
/// bfloat16
bind_host_gemm_multiply_add<cutlass::bfloat16_t, cutlass::bfloat16_t, cutlass::bfloat16_t, float, float>(m);
bind_host_gemm_multiply_add<cutlass::bfloat16_t, cutlass::bfloat16_t, float, float, float>(m);
/// s8
bind_host_gemm_multiply_add<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_interleaved<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int8_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int8_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int32_t, int32_t, int32_t>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int32_t, int32_t, int8_t>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int8_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int32_t, int32_t, float>(m);
bind_host_gemm_multiply_add_saturate_interleaved<int8_t, int8_t, int32_t, int32_t, float>(m);
// float
BIND_TENSOR_EQUAL(float, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(float, cutlass::layout::ColumnMajor);
// double
BIND_TENSOR_EQUAL(double, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(double, cutlass::layout::ColumnMajor);
// half_t
BIND_TENSOR_EQUAL(cutlass::half_t, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(cutlass::half_t, cutlass::layout::ColumnMajor);
// bfloat16
BIND_TENSOR_EQUAL(cutlass::bfloat16_t, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(cutlass::bfloat16_t, cutlass::layout::ColumnMajor);
// int32_t
BIND_TENSOR_EQUAL(int32_t, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(int32_t, cutlass::layout::ColumnMajor);
// int8_t
BIND_TENSOR_EQUAL(int8_t, cutlass::layout::RowMajor);
BIND_TENSOR_EQUAL(int8_t, cutlass::layout::ColumnMajor);
BIND_TENSOR_EQUAL(int8_t, cutlass::layout::RowMajorInterleaved<32>);
BIND_TENSOR_EQUAL(int8_t, cutlass::layout::ColumnMajorInterleaved<32>);
}