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Commit 95479e67 authored by Bartlomiej Kocot's avatar Bartlomiej Kocot
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Introduce multiABD api and deprecate multiD

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include/ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp 0 → 100644
View file @ 95479e67
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <array>
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
#include "ck/utility/is_detected.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
template <typename T>
using is_tuple = decltype(std::declval<T&>().IsTuple());
/**
* \brief Grouped Convolution Forward
*
* \details
* input : input image A[G, N, C, Hi, Wi], A1[G, N, C, Hi, Wi]...
* input : weight B[G, K, C, Y, X], B1[G, K, C, Y, X]...
* input : D0[G, N, K, Ho, Wo], D1[G, N, K, Ho, Wo], ...
* output : output image E[G, N, K, Ho, Wo]
*
* C = a_op(A, A1...) * b_op(B, B1...)
* E = cde_op(C, D0, D1, ...)
*
* \tparam NDimSpatial Number of spatial dimensions.
* \tparam ALayout Input layout (also for a1, a2...).
* \tparam BLayout Weight layout (also for b1, b2...).
* \tparam DsLayout Ds layouts.
* \tparam ELayout Output layout.
* \tparam ADataType Input data type. Pass tuple if there is multiple A.
* \tparam BDataType Weight data type. Pass tuple if there is multiple B.
* \tparam DsDataType D data types.
* \tparam EDataType Output data type.
* \tparam AElementwiseOperation A elementwise operation.
* \tparam BElementwiseOperation B elementwise operation.
* \tparam CDEElementwiseOperation CDE elementwise operation.
* \tparam ComputeType Compute data type (default: ADataType, first if tuple passed).
*/
template <index_t NDimSpatial,
typename ALayout,
typename BLayout,
typename DsLayout,
typename ELayout,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CDEElementwiseOperation,
typename ComputeType =
decltype(UnpackDataType<is_detected<is_tuple, ADataType>::value,
Number<0>,
ADataType>())> // ComputeType is InputType by default (first
// in tuple for MultiAB), unpack if tuple was
// passed
struct DeviceGroupedConvFwdMultipleABD : public BaseOperator
{
static constexpr bool isMultiA = is_detected<is_tuple, ADataType>::value;
static constexpr bool isMultiB = is_detected<is_tuple, BDataType>::value;
static constexpr index_t NumATensor = GetNumABTensors<isMultiA, ADataType>();
static constexpr index_t NumBTensor = GetNumABTensors<isMultiB, BDataType>();
static constexpr index_t NumDTensor = DsDataType::Size();
static_assert(NumDTensor == DsLayout::Size(), "wrong! Inconsistent NumDTensor");
// If DataType is tuple, user has to pass std::array with pointers.
using APointers =
std::conditional_t<isMultiA, std::array<const void*, NumATensor>&, const void*>;
using BPointers =
std::conditional_t<isMultiB, std::array<const void*, NumBTensor>&, const void*>;
/**
* \brief Make argument pointer for grouped conv fwd.
*
* \param p_a A pointer to the input (std::array<const void*, NumA> with
pointers for multiple A).
* \param p_b A pointer to the weight (std::array<const void*, NumA> with
pointers for multiple B).
* \param p_ds A pointers to the Ds.
* \param p_e A pointers to the output.
* \param a_g_n_c_wis_lengths Input lengths [G, N, C, Spatial...] (for 3d).
* \param a_g_n_c_wis_strides Input strides [G, N, C, Spatial...] (for 3d).
* \param b_g_k_c_xs_lengths Weight lengths [G, K, C, Spatial...] (for 3d).
* \param b_g_k_c_xs_strides Weight strides [G, K, C, Spatial...] (for 3d).
* \param ds_g_n_k_wos_lengths Ds lengths [G, N, K, Spatial...] (for 3d).
* \param ds_g_n_k_wos_strides Ds strides [G, N, K, Spatial...] (for 3d).
* \param e_g_n_k_wos_lengths Output lengths [G, N, K, Spatial...] (for 3d).
* \param e_g_n_k_wos_strides Output strides [G, N, K, Spatial...] (for 3d).
* \param conv_filter_strides Convolution filter strides.
* \param conv_filter_dilations Convolution filter dilations.
* \param input_left_pads Input left paddings.
* \param input_right_pads Input right paddings.
* \param a_element_op A elementwise operation object.
* \param b_element_op B elementwise operation object.
* \param cde_element_op CDE elementwise operation object.
* \return Pointer to the argument.
*/
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
APointers p_a,
BPointers p_b,
const std::array<const void*, NumDTensor>& p_ds,
void* p_e,
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths,
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_n_k_wos_lengths,
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths,
const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CDEElementwiseOperation& cde_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
} // namespace device
} // namespace tensor_operation
} // namespace ck
include/ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d.hpp
View file @ 95479e67
...@@ -3,30 +3,18 @@ ...@@ -3,30 +3,18 @@
#pragma once #pragma once
#include <array> #include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_abd.hpp"
#include "ck/tensor_operation/gpu/device/device_base.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp" #include "ck/tensor_operation/gpu/device/impl/device_grouped_conv_utils.hpp"
#include "ck/utility/is_detected.hpp"
namespace ck { namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
template <typename T>
using is_tuple = decltype(std::declval<T&>().IsTuple());
/** /**
* \brief Grouped Convolution Forward * \brief Grouped Convolution Forward
* *
* \details * \deprecated This structure is deprecated (left for backwards compatibility). Please use
* input : input image A[G, N, C, Hi, Wi], A1[G, N, C, Hi, Wi]... * DeviceGroupedConvFwdMultipleABD.
* input : weight B[G, K, C, Y, X], B1[G, K, C, Y, X]...
* input : D0[G, N, K, Ho, Wo], D1[G, N, K, Ho, Wo], ...
* output : output image E[G, N, K, Ho, Wo]
*
* C = a_op(A, A1...) * b_op(B, B1...)
* E = cde_op(C, D0, D1, ...)
* *
* \tparam NDimSpatial Number of spatial dimensions. * \tparam NDimSpatial Number of spatial dimensions.
* \tparam ALayout Input layout (also for a1, a2...). * \tparam ALayout Input layout (also for a1, a2...).
...@@ -60,72 +48,19 @@ template <index_t NDimSpatial, ...@@ -60,72 +48,19 @@ template <index_t NDimSpatial,
ADataType>())> // ComputeType is InputType by default (first ADataType>())> // ComputeType is InputType by default (first
// in tuple for MultiAB), unpack if tuple was // in tuple for MultiAB), unpack if tuple was
// passed // passed
struct DeviceGroupedConvFwdMultipleD : public BaseOperator using DeviceGroupedConvFwdMultipleD = DeviceGroupedConvFwdMultipleABD<NDimSpatial,
{ ALayout,
static constexpr bool isMultiA = is_detected<is_tuple, ADataType>::value; BLayout,
static constexpr bool isMultiB = is_detected<is_tuple, BDataType>::value; DsLayout,
ELayout,
static constexpr index_t NumATensor = GetNumABTensors<isMultiA, ADataType>(); ADataType,
static constexpr index_t NumBTensor = GetNumABTensors<isMultiB, BDataType>(); BDataType,
static constexpr index_t NumDTensor = DsDataType::Size(); DsDataType,
EDataType,
static_assert(NumDTensor == DsLayout::Size(), "wrong! Inconsistent NumDTensor"); AElementwiseOperation,
BElementwiseOperation,
// If DataType is tuple, user has to pass std::array with pointers. CDEElementwiseOperation,
using APointers = ComputeType>;
std::conditional_t<isMultiA, std::array<const void*, NumATensor>&, const void*>;
using BPointers =
std::conditional_t<isMultiB, std::array<const void*, NumBTensor>&, const void*>;
/**
* \brief Make argument pointer for grouped conv fwd.
*
* \param p_a A pointer to the input (std::array<const void*, NumA> with
pointers for multiple A).
* \param p_b A pointer to the weight (std::array<const void*, NumA> with
pointers for multiple B).
* \param p_ds A pointers to the Ds.
* \param p_e A pointers to the output.
* \param a_g_n_c_wis_lengths Input lengths [G, N, C, Spatial...] (for 3d).
* \param a_g_n_c_wis_strides Input strides [G, N, C, Spatial...] (for 3d).
* \param b_g_k_c_xs_lengths Weight lengths [G, K, C, Spatial...] (for 3d).
* \param b_g_k_c_xs_strides Weight strides [G, K, C, Spatial...] (for 3d).
* \param ds_g_n_k_wos_lengths Ds lengths [G, N, K, Spatial...] (for 3d).
* \param ds_g_n_k_wos_strides Ds strides [G, N, K, Spatial...] (for 3d).
* \param e_g_n_k_wos_lengths Output lengths [G, N, K, Spatial...] (for 3d).
* \param e_g_n_k_wos_strides Output strides [G, N, K, Spatial...] (for 3d).
* \param conv_filter_strides Convolution filter strides.
* \param conv_filter_dilations Convolution filter dilations.
* \param input_left_pads Input left paddings.
* \param input_right_pads Input right paddings.
* \param a_element_op A elementwise operation object.
* \param b_element_op B elementwise operation object.
* \param cde_element_op CDE elementwise operation object.
* \return Pointer to the argument.
*/
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
APointers p_a,
BPointers p_b,
const std::array<const void*, NumDTensor>& p_ds,
void* p_e,
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_lengths,
const std::array<index_t, NDimSpatial + 3>& a_g_n_c_wis_strides,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_lengths,
const std::array<index_t, NDimSpatial + 3>& b_g_k_c_xs_strides,
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_n_k_wos_lengths,
const std::array<std::array<index_t, NDimSpatial + 3>, NumDTensor>& ds_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_lengths,
const std::array<index_t, NDimSpatial + 3>& e_g_n_k_wos_strides,
const std::array<index_t, NDimSpatial>& conv_filter_strides,
const std::array<index_t, NDimSpatial>& conv_filter_dilations,
const std::array<index_t, NDimSpatial>& input_left_pads,
const std::array<index_t, NDimSpatial>& input_right_pads,
const AElementwiseOperation& a_element_op,
const BElementwiseOperation& b_element_op,
const CDEElementwiseOperation& cde_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
......
test/grouped_convnd_fwd/CMakeLists.txt
View file @ 95479e67
...@@ -3,3 +3,6 @@ target_link_libraries(test_grouped_convnd_fwd PRIVATE utility device_grouped_con ...@@ -3,3 +3,6 @@ target_link_libraries(test_grouped_convnd_fwd PRIVATE utility device_grouped_con
add_gtest_executable(test_grouped_convnd_fwd_multi_ab_interface test_grouped_convnd_fwd_multi_ab_interface.cpp) add_gtest_executable(test_grouped_convnd_fwd_multi_ab_interface test_grouped_convnd_fwd_multi_ab_interface.cpp)
target_link_libraries(test_grouped_convnd_fwd_multi_ab_interface PRIVATE utility) target_link_libraries(test_grouped_convnd_fwd_multi_ab_interface PRIVATE utility)
add_gtest_executable(test_grouped_convnd_fwd_multi_d_interface_compatibility test_grouped_convnd_fwd_multi_d_interface_compatibility.cpp)
target_link_libraries(test_grouped_convnd_fwd_multi_d_interface_compatibility PRIVATE utility device_grouped_conv3d_fwd_instance)
test/grouped_convnd_fwd/test_grouped_convnd_fwd_multi_d_interface_compatibility.cpp 0 → 100644
View file @ 95479e67
// SPDX-License-Identifier: MIT
// Copyright (c) 2023, Advanced Micro Devices, Inc. All rights reserved.
#include <cstdlib>
#include <iostream>
#include <initializer_list>
#include <tuple>
#include <vector>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_conv_fwd_multiple_d.hpp"
#include <gtest/gtest.h>
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
template <typename DataType,
typename InDataTypes,
typename WeiDataTypes,
typename InElementOp,
typename WeiElementOp>
class TestGroupedConvndFwdMultiDInterfaceCompatibility : public ::testing::Test
{
protected:
static constexpr ck::index_t NDimSpatial = 3;
using InDataType = float;
using WeiDataType = float;
using OutDataType = float;
using InLayout = ck::tensor_layout::convolution::GNDHWC;
using WeiLayout = ck::tensor_layout::convolution::GKZYXC;
using OutLayout = ck::tensor_layout::convolution::GNDHWK;
using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<NDimSpatial,
InLayout,
WeiLayout,
ck::Tuple<>,
OutLayout,
InDataType,
WeiDataType,
ck::Tuple<>,
OutDataType,
PassThrough,
PassThrough,
PassThrough>;
bool Run()
{
const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
DeviceOp>::GetInstances();
return op_ptrs.size() != 0;
}
};
TEST_F(TestGroupedConvndFwdMultiDInterfaceCompatibility, CompatibilityTest)
{
EXPECT_TRUE(this->template Run());
}
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