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Pr82 followup (#115)

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* Use thread cluster descriptor and explicit M_K 2d descriptor to simply Blockwise Reduction

* Change by replacing ReduceDims by NumReduceDims as Device Reduce interface template parameter

* Rename the folder name for the pool2d and reduce examples

* Update to reduction test scripts

* Add Readme for pool2d_fwd and reduce_blockwise examples

* Tiny fix in reduce profiler and tiny update in reduce testing scripts

* Tiny fix in testing script profile_reduce_no_index.sh

* Tiny change in script/profile_reduce_with_index.sh

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming and refining in Reduction profiler/device layer/examples

* Renaming all NumReduceDims to NumReduceDim
This commit is contained in:
Qianfeng
2022-03-11 00:14:43 +08:00
committed by GitHub
parent 5d37d7bff4
commit 827301d95a
70 changed files with 1704 additions and 1576 deletions
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49
example/12_reduce/reduce_blockwise.cpp
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@@ -14,6 +14,7 @@
#include "device_reduce_blockwise.hpp"
#include "host_reduce_util.hpp"
#include "host_generic_reduction.hpp"
#include "reduction_enums.hpp"
#include "reduction_operator_mapping.hpp"
@@ -28,8 +29,8 @@ using kInDataType = ck::half_t;
using kOutDataType = ck::half_t;
using kAccDataType = float;
constexpr int Rank = 4;
using ReduceDims_ = ck::Sequence<0, 1, 2>;
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
constexpr ReduceTensorOp_t ReduceOpId = ReduceTensorOp_t::NORM2;
constexpr NanPropagation_t NanOpt = NanPropagation_t::PROPAGATE_NAN;
@@ -46,7 +47,7 @@ using DeviceReduceInstance = DeviceReduceBlockWise<kInDataType,
kAccDataType,
kOutDataType,
Rank,
ReduceDims_,
NumReduceDim,
ReduceOperation,
InElementwiseOperation,
AccElementwiseOperation,
@@ -192,39 +193,13 @@ class SimpleAppArgs
};
};
template <int Rank, typename ReduceDims>
static std::vector<int> get_reduce_dims()
{
std::vector<int> resDims;
static_for<0, ReduceDims::Size(), 1>{}([&](auto i) { resDims.push_back(ReduceDims::At(i)); });
return (resDims);
};
template <int Rank, typename ReduceDims>
static std::vector<int> get_invariant_dims()
{
std::vector<int> resDims;
unsigned int incFlag = 0;
static_for<0, ReduceDims::Size(), 1>{}(
[&](auto i) { incFlag = incFlag | (0x1 << ReduceDims::At(i)); });
for(int dim = 0; dim < Rank; dim++)
{
if(incFlag & (0x1 << dim))
continue;
resDims.push_back(dim);
};
return (resDims);
};
int main(int argc, char* argv[])
{
using namespace ck::host_reduce;
const std::vector<int> reduceDims{0, 1, 2};
const std::vector<int> invariantDims{3};
SimpleAppArgs args;
if(args.processArgs(argc, argv) < 0)
@@ -260,15 +235,12 @@ int main(int argc, char* argv[])
Tensor<InDataType> in(args.inLengths);
const std::vector<int> InvariantDims = get_invariant_dims<Rank, ReduceDims_>();
const std::vector<int> ReduceDims = get_reduce_dims<Rank, ReduceDims_>();
std::vector<size_t> outLengths;
if(InvariantDims.empty())
if(invariantDims.empty())
outLengths.push_back(1);
else
for(auto dim : InvariantDims)
for(auto dim : invariantDims)
outLengths.push_back(args.inLengths[dim]);
Tensor<OutDataType> out_ref(outLengths);
@@ -328,7 +300,7 @@ int main(int argc, char* argv[])
if(args.do_verification)
{
ReductionHost<InDataType, AccDataType, OutDataType, ReduceOpId, PropagateNan, NeedIndices>
hostReduce(in.mDesc, out_ref.mDesc, InvariantDims, ReduceDims);
hostReduce(in.mDesc, out_ref.mDesc, invariantDims, reduceDims);
hostReduce.Run(
alpha, in.mData.data(), beta, out_ref.mData.data(), out_indices_ref.mData.data());
@@ -350,6 +322,7 @@ int main(int argc, char* argv[])
i_inStrides,
i_outLengths,
i_outStrides,
reduceDims,
alpha,
beta,
in_dev.GetDeviceBuffer(),