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Commit d8f1458f authored by Jing Zhang's avatar Jing Zhang
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Merge remote-tracking branch 'origin/develop' into grouped_gemm_args_const_buff

parents 6e983ba2 40b59a63
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Showing with 290 additions and 417 deletions
include/ck/utility/generic_memory_space_atomic.hpp
View file @ d8f1458f
......@@ -28,6 +28,12 @@ __device__ float atomic_add<float>(float* p_dst, const float& x)
return atomicAdd(p_dst, x);
}
template <>
__device__ double atomic_add<double>(double* p_dst, const double& x)
{
return atomicAdd(p_dst, x);
}
template <>
__device__ float2_t atomic_add<float2_t>(float2_t* p_dst, const float2_t& x)
{
......@@ -45,6 +51,23 @@ __device__ float2_t atomic_add<float2_t>(float2_t* p_dst, const float2_t& x)
return vy.template AsType<float2_t>()[I0];
}
template <>
__device__ double2_t atomic_add<double2_t>(double2_t* p_dst, const double2_t& x)
{
constexpr auto I0 = Number<0>{};
constexpr auto I1 = Number<1>{};
const vector_type<double, 2> vx{x};
vector_type<double, 2> vy{0};
vy.template AsType<double>()(I0) =
atomicAdd(c_style_pointer_cast<double*>(p_dst), vx.template AsType<double>()[I0]);
vy.template AsType<double>()(I1) =
atomicAdd(c_style_pointer_cast<double*>(p_dst) + 1, vx.template AsType<double>()[I1]);
return vy.template AsType<double2_t>()[I0];
}
// Caution: DO NOT REMOVE
// intentionally have only declaration but no definition to cause compilation failure when trying to
// instantiate this template. The purpose is to make the implementation of atomic_max explicit for
......
include/ck/utility/inner_product.hpp
View file @ d8f1458f
#ifndef CK_INNER_PRODUCT_HPP
#define CK_INNER_PRODUCT_HPP
#pragma once
#include "data_type.hpp"
namespace ck {
......@@ -138,7 +136,7 @@ template <>
__device__ void
inner_product<int8x4_t, int8x4_t, int32_t>(const int8x4_t& a, const int8x4_t& b, int32_t& c)
{
#if defined(CK_USE_DOT4_I32_I8)
#if defined(CK_USE_AMD_V_DOT4_I32_I8)
#if CK_USE_AMD_INNER_PRODUCT_INLINE_ASM
asm volatile("\n \
v_dot4_i32_i8 %0, %1, %2, %0\n \
......@@ -202,4 +200,3 @@ inner_product<int8x16_t, int8x16_t, int32_t>(const int8x16_t& a, const int8x16_t
}
} // namespace ck
#endif
include/ck/utility/reduction_operator.hpp
View file @ d8f1458f
......@@ -26,7 +26,8 @@
#ifndef CK_REDUCTION_OPERATOR_HPP
#define CK_REDUCTION_OPERATOR_HPP
#include "common_header.hpp"
#include "config.hpp"
#include "data_type.hpp"
namespace ck {
......@@ -41,12 +42,10 @@ namespace reduce {
// when operated against them, and the concept is similar to zero vector in
// vector space
// (http://pages.cs.wisc.edu/~matthewb/pages/notes/pdf/linearalgebra/VectorSpaces.pdf).
// 2) indexable -- boolean value indicating whether indices of the operated elements could be
// recorded. Usually, Min/Max operator could
// need to record the indices of elements. For operator like Add/Mul, no need to
// record the indices.
// 3) operator() -- the first argument of the operator must be both an input & output, and the
// corresponding variable usually stores
// 2) IsCompatibleInMemoryDataOperation() -- return true if the reduction task corresponding to this
// operator can use the InMemoryDataOperation to finalize, or else it return false 3) operator() --
// the first argument of the operator must be both an input & output, and the corresponding variable
// usually stores
// the accumulated result of many operator() calls; the second argument is only an
// input. For indexable binary
// operator, the second version of operator() has third argument (which is an
......@@ -62,6 +61,13 @@ struct Add
__host__ __device__ static constexpr T GetReductionZeroVal() { return static_cast<T>(0.0f); };
__device__ static constexpr bool
IsCompatibleInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
return operation == InMemoryDataOperationEnum::AtomicAdd ||
operation == InMemoryDataOperationEnum::Set;
};
__host__ __device__ inline constexpr void operator()(T& a, T b) const { a = a + b; }
};
......@@ -72,6 +78,12 @@ struct Mul
__host__ __device__ static constexpr T GetReductionZeroVal() { return static_cast<T>(1.0f); };
__device__ static constexpr bool
IsCompatibleInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
return operation == InMemoryDataOperationEnum::Set;
};
__host__ __device__ inline constexpr void operator()(T& a, T b) const { a = a * b; }
};
......@@ -85,6 +97,13 @@ struct Max
return NumericLimits<T>::Lowest();
};
__device__ static constexpr bool
IsCompatibleInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
// ToChange: atomic_max to be added
return operation == InMemoryDataOperationEnum::Set;
};
__host__ __device__ inline constexpr void operator()(T& a, T b) const
{
if(a < b)
......@@ -111,6 +130,13 @@ struct Min
return NumericLimits<T>::Max();
};
__device__ static constexpr bool
IsCompatibleInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
// ToChange: atomic_min to be added
return operation == InMemoryDataOperationEnum::Set;
};
__host__ __device__ inline constexpr void operator()(T& a, T b) const
{
if(a > b)
......@@ -134,6 +160,13 @@ struct AMax
__host__ __device__ static constexpr T GetReductionZeroVal() { return static_cast<T>(0.0f); };
__device__ static constexpr bool
IsCompatibleInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
// ToChange: atomic_max to be added
return operation == InMemoryDataOperationEnum::Set;
};
__host__ __device__ inline constexpr void operator()(T& a, T b) const
{
if(a < b)
......@@ -150,6 +183,17 @@ struct AMax
}
};
template <typename T>
T GetReductionZeroValueForInMemoryDataOperation(InMemoryDataOperationEnum operation)
{
T result = ck::type_convert<T>(0.0f);
if(operation == InMemoryDataOperationEnum::AtomicMax)
result = ck::NumericLimits<T>::Lowest();
return (result);
};
}; // end of namespace reduce
} // end of namespace ck
......
include/ck/utility/static_buffer.hpp
View file @ d8f1458f
......@@ -36,6 +36,11 @@ struct StaticBuffer : public StaticallyIndexedArray<T, N>
{
return base::operator()(i);
}
__host__ __device__ void Clear()
{
static_for<0, N, 1>{}([&](auto i) { operator()(i) = T{0}; });
}
};
// static buffer for vector
......@@ -146,9 +151,9 @@ struct StaticBufferTupleOfVector
__host__ __device__ void Clear()
{
const index_t numScalars = NumOfVector * ScalarPerVector;
constexpr index_t NumScalars = NumOfVector * ScalarPerVector;
static_for<0, Number<numScalars>{}, 1>{}([&](auto i) { SetAsType(i, S{0}); });
static_for<0, NumScalars, 1>{}([&](auto i) { SetAsType(i, S{0}); });
}
};
......
library/include/ck/library/host_tensor/host_common_util.hpp 0 → 100644
View file @ d8f1458f
/*******************************************************************************
*
* MIT License
*
* Copyright (c) 2020 Advanced Micro Devices, Inc.
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*
*******************************************************************************/
#ifndef GUARD_HOST_COMMON_UTIL_HPP
#define GUARD_HOST_COMMON_UTIL_HPP
#include <vector>
#include <iostream>
#include <fstream>
#include <string>
#include "config.hpp"
namespace ck {
namespace host_common {
template <typename T>
static inline void dumpBufferToFile(const char* fileName, T* data, size_t dataNumItems)
{
std::ofstream outFile(fileName, std::ios::binary);
if(outFile)
{
outFile.write(reinterpret_cast<char*>(data), dataNumItems * sizeof(T));
outFile.close();
std::cout << "Write output to file " << fileName << std::endl;
}
else
{
std::cout << "Could not open file " << fileName << " for writing" << std::endl;
}
};
template <typename T>
static inline T getSingleValueFromString(const std::string& valueStr)
{
std::istringstream iss(valueStr);
T val;
iss >> val;
return (val);
};
template <typename T>
static inline std::vector<T> getTypeValuesFromString(const char* cstr_values)
{
std::string valuesStr(cstr_values);
std::vector<T> values;
std::size_t pos = 0;
std::size_t new_pos;
new_pos = valuesStr.find(',', pos);
while(new_pos != std::string::npos)
{
const std::string sliceStr = valuesStr.substr(pos, new_pos - pos);
T val = getSingleValueFromString<T>(sliceStr);
values.push_back(val);
pos = new_pos + 1;
new_pos = valuesStr.find(',', pos);
};
std::string sliceStr = valuesStr.substr(pos);
T val = getSingleValueFromString<T>(sliceStr);
values.push_back(val);
return (values);
}
}; // namespace host_common
}; // namespace ck
#endif
library/include/ck/library/host_tensor/host_reduce_util.hpp
View file @ d8f1458f
......@@ -28,9 +28,7 @@
#include <limits>
#include <cmath>
#include <cassert>
#include <stdexcept>
#include <string>
#include <functional>
#include "reduction_enums.hpp"
#include "data_type.hpp"
......@@ -214,13 +212,13 @@ binop_with_nan_check(std::function<void(AccDataType&, AccDataType)> opReduce,
};
};
template <typename AccDataType, bool PropagateNan>
template <typename AccDataType, typename IndexDataType, bool PropagateNan>
__host__ static inline void
binop_with_nan_check2(std::function<void(AccDataType&, AccDataType, bool&)> opReduce,
AccDataType& accuVal,
AccDataType currVal,
int& accuIndex,
int currIndex)
binop_with_index_and_nan_check(std::function<void(AccDataType&, AccDataType, bool&)> opReduce,
AccDataType& accuVal,
AccDataType currVal,
IndexDataType& accuIndex,
IndexDataType currIndex)
{
using ck::math::isnan;
......@@ -254,16 +252,6 @@ binop_with_nan_check2(std::function<void(AccDataType&, AccDataType, bool&)> opRe
}; // namespace host_reduce
static inline std::vector<int> to_int_vector(const std::vector<size_t>& inData)
{
std::vector<int> outData;
for(auto elem : inData)
outData.push_back(static_cast<int>(elem));
return (outData);
};
}; // namespace ck
#endif
library/include/ck/library/host_tensor/host_reduction.hpp
View file @ d8f1458f
......@@ -34,6 +34,7 @@
#include "reduction_enums.hpp"
#include "reduction_common.hpp"
#include "host_reduce_util.hpp"
#include "host_common_util.hpp"
#include "host_tensor.hpp"
#include "data_type.hpp"
......@@ -200,7 +201,7 @@ struct ReductionHost
using ck::float_equal_one;
using ck::float_equal_zero;
using ck::type_convert;
using ck::host_reduce::binop_with_nan_check2;
using ck::host_reduce::binop_with_index_and_nan_check;
using ck::host_reduce::ReduceOpFn2;
using ck::host_reduce::ReduceOpZeroVal;
......@@ -211,8 +212,7 @@ struct ReductionHost
AccDataType accuVal = ReduceOpZeroVal<AccDataType, ReduceOpId>();
IndexDataType accuIndex = 0;
for(IndexDataType i = 0; i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size());
i++)
for(std::size_t i = 0; i < reduce_dim_indexes.size(); i++)
{
auto offset_reduce =
get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);
......@@ -221,9 +221,9 @@ struct ReductionHost
preUnaryOp(currVal);
auto currIndex = i;
auto currIndex = static_cast<IndexDataType>(i);
binop_with_nan_check2<AccDataType, PropagateNan>(
binop_with_index_and_nan_check<AccDataType, IndexDataType, PropagateNan>(
opReduce2, accuVal, currVal, accuIndex, currIndex);
};
......@@ -247,9 +247,7 @@ struct ReductionHost
auto offset_invariant =
get_offset_from_index<NumInvariantDim>(invariantStrides, invariant_index);
for(IndexDataType i = 0;
i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size());
i++)
for(std::size_t i = 0; i < reduce_dim_indexes.size(); i++)
{
auto offset_reduce =
get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);
......@@ -259,9 +257,9 @@ struct ReductionHost
preUnaryOp(currVal);
auto currIndex = i;
auto currIndex = static_cast<IndexDataType>(i);
binop_with_nan_check2<AccDataType, PropagateNan>(
binop_with_index_and_nan_check<AccDataType, IndexDataType, PropagateNan>(
opReduce2, accuVal, currVal, accuIndex, currIndex);
};
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance.hpp
View file @ d8f1458f
......@@ -9,26 +9,11 @@
#include "device_reduce_instance_blockwise_i8_i8_i8.hpp"
#include "device_reduce_instance_blockwise_i8_i32_i8.hpp"
#include "device_reduce_instance_blockwise_b16_f32_b16.hpp"
#include "device_reduce_instance_blockwise_second_call_f16_f16_f16.hpp"
#include "device_reduce_instance_blockwise_second_call_f32_f32_f16.hpp"
#include "device_reduce_instance_blockwise_second_call_f32_f32_f32.hpp"
#include "device_reduce_instance_blockwise_second_call_f64_f64_f32.hpp"
#include "device_reduce_instance_blockwise_second_call_f64_f64_f64.hpp"
#include "device_reduce_instance_blockwise_second_call_i8_i8_i8.hpp"
#include "device_reduce_instance_blockwise_second_call_i32_i32_i8.hpp"
#include "device_reduce_instance_blockwise_second_call_f32_f32_b16.hpp"
#include "device_reduce_instance_multiblock_atomic_add_f16_f32_f32.hpp"
#include "device_reduce_instance_multiblock_atomic_add_f32_f32_f32.hpp"
#include "device_reduce_instance_multiblock_atomic_add_f32_f64_f32.hpp"
#include "device_reduce_instance_multiblock_atomic_add_f64_f64_f64.hpp"
#include "device_reduce_instance_multiblock_atomic_add_b16_f32_f32.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_f16_f16_f16.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_f16_f32_f16.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_f32_f32_f32.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_f32_f64_f32.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_f64_f64_f64.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_i8_i8_i8.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_i8_i32_i8.hpp"
#include "device_reduce_instance_multiblock_partial_reduce_b16_f32_b16.hpp"
#include "device_reduce_instance_threadwise_f16_f16_f16.hpp"
#include "device_reduce_instance_threadwise_f16_f32_f16.hpp"
#include "device_reduce_instance_threadwise_f32_f32_f32.hpp"
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise.hpp
View file @ d8f1458f
......@@ -3,13 +3,27 @@
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_impl_common.hpp"
#include "device_reduce_blockwise.hpp"
#include "device_reduce_multiblock.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_reduce_instance {
using reduce_configuration_1_instances_blockwise = std::tuple<
// clang-format off
// BlockSize | MThreadClusterSize | KThreadClusterSize
ReductionConfiguration_1<256, 128, 2>,
ReductionConfiguration_1<256, 64, 4>,
ReductionConfiguration_1<256, 32, 8>,
ReductionConfiguration_1<256, 16, 16>,
ReductionConfiguration_1<256, 8, 32>,
ReductionConfiguration_1<256, 4, 64>,
ReductionConfiguration_1<256, 2, 128>,
ReductionConfiguration_1<256, 1, 256>
// clang-format on
>;
#ifdef QUICK_REDUCE_TEST
using reduce_configuration_2_instances_blockwise = std::tuple<
// clang-format off
......@@ -58,8 +72,8 @@ template <typename InDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
bool PropagateNan,
bool UseIndex>
void add_device_reduce_instance_blockwise(
std::vector<deviceReduceBlockWisePtrType<AccDataType, ReduceOpId>>& device_op_instances)
{
......@@ -73,92 +87,94 @@ void add_device_reduce_instance_blockwise(
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_for<0, std::tuple_size<reduce_configuration_1_instances>::value, 1>{}([&](auto i) {
using cfg1 =
remove_cvref_t<decltype(std::get<i.value>(reduce_configuration_1_instances{}))>;
static_for<0, std::tuple_size<reduce_configuration_2_instances_blockwise>::value, 1>{}(
[&](auto j) {
using cfg2 = remove_cvref_t<decltype(
std::get<j.value>(reduce_configuration_2_instances_blockwise{}))>;
using ReduceOpInstance = DeviceReduceBlockWise<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
NeedIndices,
cfg1::BlockSize_,
cfg1::MThreadClusterSize_,
cfg1::KThreadClusterSize_,
cfg2::MThreadSliceSize_,
cfg2::KThreadSliceSize_,
cfg2::InSrcVectorDim_,
cfg2::InSrcVectorSize_,
cfg2::OutDstVectorSize_>;
device_op_instances.push_back(
std::make_unique<ReduceOpInstance>(ReduceOpInstance{}));
});
});
constexpr bool OutputIndex = Indexable && UseIndex;
static_for<0, std::tuple_size<reduce_configuration_1_instances_blockwise>::value, 1>{}(
[&](auto i) {
using cfg1 = remove_cvref_t<decltype(
std::get<i.value>(reduce_configuration_1_instances_blockwise{}))>;
static_for<0, std::tuple_size<reduce_configuration_2_instances_blockwise>::value, 1>{}(
[&](auto j) {
using cfg2 = remove_cvref_t<decltype(
std::get<j.value>(reduce_configuration_2_instances_blockwise{}))>;
using ReduceOpInstance =
DeviceReduceMultiBlock<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
InMemoryDataOperationEnum::Set,
PropagateNan,
OutputIndex,
false, // HaveIndexInputIfOutputIndex
cfg1::BlockSize_,
cfg1::MThreadClusterSize_,
cfg1::KThreadClusterSize_,
cfg2::MThreadSliceSize_,
cfg2::KThreadSliceSize_,
cfg2::InSrcVectorDim_,
cfg2::InSrcVectorSize_,
cfg2::OutDstVectorSize_>;
device_op_instances.push_back(
std::make_unique<ReduceOpInstance>(ReduceOpInstance{}));
});
});
};
#define ADD_BLOCKWISE_INST_BY_TYPE( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
template void add_device_reduce_instance_blockwise<inT, \
compT, \
outT, \
Rank, \
NumReduceDim, \
ReduceOpId, \
NanOpt, \
IndicesOpt>( \
#define ADD_BLOCKWISE_INST_BY_TYPE( \
inT, compT, outT, ReduceOpId, PropagateNan, UseIndex, Rank, NumReduceDim) \
template void add_device_reduce_instance_blockwise<inT, \
compT, \
outT, \
Rank, \
NumReduceDim, \
ReduceOpId, \
PropagateNan, \
UseIndex>( \
std::vector<deviceReduceBlockWisePtrType<compT, ReduceOpId>> & device_op_instances)
#define ADD_BLOCKWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<bool>(NanOpt), \
static_cast<bool>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_BLOCKWISE_INST_REF_BY_TYPE( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
inT, compT, outT, ReduceOpId, PropagateNan, UseIndex, Rank, NumReduceDim) \
extern template void add_device_reduce_instance_blockwise<inT, \
compT, \
outT, \
Rank, \
NumReduceDim, \
ReduceOpId, \
NanOpt, \
IndicesOpt>( \
PropagateNan, \
UseIndex>( \
std::vector<DeviceReducePtr< \
typename reduce_unary_operator<compT, ReduceOpId, true, true>::InElementwiseOperation, \
typename reduce_unary_operator<compT, ReduceOpId, true, true>:: \
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_BLOCKWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
#define ADD_BLOCKWISE_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<bool>(NanOpt), \
static_cast<bool>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_b16_f32_b16.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_B16_F32_B16_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_B16_F32_B16_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "data_type.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f16_f16_f16.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_F16_F16_F16_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_F16_F16_F16_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "data_type.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f16_f32_f16.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_F16_F32_F16_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_F16_F32_F16_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "data_type.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f32_f32_f32.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_F32_F32_F32_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_F32_F32_F32_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f32_f64_f32.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_F32_F64_F32_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_F32_F64_F32_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_f64_f64_f64.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_F64_F64_F64_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_F64_F64_F64_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_i8_i32_i8.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_I8_I32_I8_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_I8_I32_I8_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_i8_i8_i8.hpp
View file @ d8f1458f
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_I8_I8_I8_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_I8_I8_I8_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise.hpp"
namespace ck {
......
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_second_call.hpp deleted 100644 → 0
View file @ 6e983ba2
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_HPP
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_impl_common.hpp"
#include "device_reduce_blockwise_second_call.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_reduce_instance {
#ifdef QUICK_REDUCE_TEST
using reduce_configuration_2_instances_blockwise_second_call = std::tuple<
// clang-format off
// InSrcVectorDim | InSrcVectorSize | OutDstVectorSize | MThreadSliceSize | KThreadSliceSize
ReductionConfiguration_2<1, 2, 1, 1, 2>,
ReductionConfiguration_2<1, 1, 1, 1, 3>
// clang-format on
>;
#else
using reduce_configuration_2_instances_blockwise_second_call = std::tuple<
// clang-format off
// InSrcVectorDim | InSrcVectorSize | OutDstVectorSize | MThreadSliceSize | KThreadSliceSize
ReductionConfiguration_2<1, 4, 1, 1, 8>,
ReductionConfiguration_2<1, 4, 1, 1, 4>,
ReductionConfiguration_2<1, 2, 1, 1, 2>,
ReductionConfiguration_2<1, 1, 1, 1, 3>,
ReductionConfiguration_2<1, 1, 1, 1, 5>,
ReductionConfiguration_2<1, 1, 1, 1, 7>,
ReductionConfiguration_2<1, 1, 1, 1, 11>
// clang-format on
>;
#endif
template <typename AccDataType, ReduceTensorOp ReduceOpId>
using deviceReduceBlockWiseSecondCallPtrType = DeviceReducePtr<
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::InElementwiseOperation,
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::AccElementwiseOperation>;
template <typename InDataType,
typename AccDataType,
typename OutDataType,
int Rank,
int NumReduceDim,
ReduceTensorOp ReduceOpId,
NanPropagation NanOpt,
ReduceTensorIndices IndicesOpt>
void add_device_reduce_instance_blockwise_second_call(
std::vector<deviceReduceBlockWiseSecondCallPtrType<AccDataType, ReduceOpId>>&
device_op_instances)
{
using ReduceOperation = typename reduce_binary_operator<AccDataType, ReduceOpId>::opType;
using InElementwiseOperation =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
InElementwiseOperation;
using AccElementwiseOperation =
typename reduce_unary_operator<AccDataType, ReduceOpId, false, true>::
AccElementwiseOperation;
constexpr bool Indexable =
(ReduceOpId == ReduceTensorOp::MIN || ReduceOpId == ReduceTensorOp::MAX ||
ReduceOpId == ReduceTensorOp::AMAX);
constexpr bool NeedIndices = Indexable && (IndicesOpt != ReduceTensorIndices::NO_INDICES);
constexpr bool PropagateNan = (NanOpt == NanPropagation::NOT_PROPAGATE_NAN) ? false : true;
static_assert(std::is_same<InDataType, AccDataType>::value,
"InDataType and AccDataType should be the same to use "
"add_device_reduce_instance_blockwise_second_call!");
static_for<0, std::tuple_size<reduce_configuration_1_instances>::value, 1>{}([&](auto i) {
using cfg1 =
remove_cvref_t<decltype(std::get<i.value>(reduce_configuration_1_instances{}))>;
static_for<0,
std::tuple_size<reduce_configuration_2_instances_blockwise_second_call>::value,
1>{}([&](auto j) {
using cfg2 = remove_cvref_t<decltype(
std::get<j.value>(reduce_configuration_2_instances_blockwise_second_call{}))>;
using ReduceOpInstance = DeviceReduceBlockWiseSecondCall<InDataType,
AccDataType,
OutDataType,
Rank,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
PropagateNan,
NeedIndices,
cfg1::BlockSize_,
cfg1::MThreadClusterSize_,
cfg1::KThreadClusterSize_,
cfg2::MThreadSliceSize_,
cfg2::KThreadSliceSize_,
cfg2::InSrcVectorDim_,
cfg2::InSrcVectorSize_,
cfg2::OutDstVectorSize_>;
device_op_instances.push_back(std::make_unique<ReduceOpInstance>(ReduceOpInstance{}));
});
});
};
#define ADD_BLOCKWISE_SECOND_CALL_INST_BY_TYPE( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
template void add_device_reduce_instance_blockwise_second_call<inT, \
compT, \
outT, \
Rank, \
NumReduceDim, \
ReduceOpId, \
NanOpt, \
IndicesOpt>( \
std::vector<deviceReduceBlockWiseSecondCallPtrType<compT, ReduceOpId>> & \
device_op_instances)
#define ADD_BLOCKWISE_SECOND_CALL_INST_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
#define ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_TYPE( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
extern template void add_device_reduce_instance_blockwise_second_call<inT, \
compT, \
outT, \
Rank, \
NumReduceDim, \
ReduceOpId, \
NanOpt, \
IndicesOpt>( \
std::vector< \
DeviceReducePtr<typename reduce_unary_operator<compT, ReduceOpId, false, true>:: \
InElementwiseOperation, \
typename reduce_unary_operator<compT, ReduceOpId, false, true>:: \
AccElementwiseOperation>> & \
device_op_instances)
#define ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID( \
inT, compT, outT, ReduceOpId, NanOpt, IndicesOpt, Rank, NumReduceDim) \
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_TYPE(inT, \
compT, \
outT, \
static_cast<ReduceTensorOp>(ReduceOpId), \
static_cast<NanPropagation>(NanOpt), \
static_cast<ReduceTensorIndices>(IndicesOpt), \
Rank, \
NumReduceDim)
} // namespace device_reduce_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_second_call_f16_f16_f16.hpp deleted 100644 → 0
View file @ 6e983ba2
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_F16_F16_F16_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_F16_F16_F16_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise_second_call.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_reduce_instance {
// clang-format off
// InDataType | AccDataType | OutDataType | ReduceOpId | NanPropaOpt | IndicesOpt | Rank | NumReduceDim
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 0, 4, 3); // for MIN
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 0, 4, 3); // for MAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 0, 4, 3); // for AMAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 1, 4, 3); // for MIN
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 2, 0, 1, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 1, 4, 3); // for MAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 3, 0, 1, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 1, 4, 3); // for AMAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(half_t, half_t, half_t, 4, 0, 1, 2, 1);
// clang-format on
} // namespace device_reduce_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
library/include/ck/library/tensor_operation_instance/gpu/reduce/device_reduce_instance_blockwise_second_call_f32_f32_b16.hpp deleted 100644 → 0
View file @ 6e983ba2
#ifndef DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_F32_F32_B16_HPP
#define DEVICE_REDUCE_INSTANCE_BLOCKWISE_SECOND_CALL_F32_F32_B16_HPP
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
#include "device_reduce_instance_blockwise_second_call.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace device_reduce_instance {
// clang-format off
// InDataType | AccDataType | OutDataType | ReduceOpId | NanPropaOpt | IndicesOpt | Rank | NumReduceDim
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 0, 0, 0, 4, 3); // for ADD
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 0, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 0, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 0, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 5, 0, 0, 4, 3); // for AVG
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 5, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 5, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 5, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 7, 0, 0, 4, 3); // for NORM2
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 7, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 7, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 7, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 0, 4, 3); // for MIN
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 0, 4, 3); // for MAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 0, 4, 3); // for AMAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 0, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 0, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 0, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 1, 4, 3); // for MIN
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 2, 0, 1, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 1, 4, 3); // for MAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 3, 0, 1, 2, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 1, 4, 3); // for AMAX
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 1, 4, 4);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 1, 4, 1);
ADD_BLOCKWISE_SECOND_CALL_INST_REF_BY_ID(float, float, bhalf_t, 4, 0, 1, 2, 1);
// clang-format on
} // namespace device_reduce_instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
#endif
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