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BatchNorm forward instance/external api/profiler/tests/client example (#511)

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* Update to device_batchnorm_forward base class to include all template parameters for problem description

* Add batchnorm forward instances and external api

* Add batchnorm forward profiler module which uses the external api

* Add some comments in batchnorm_forward example to explain the dimensions in lengths[]

* Replace the reference_batchnorm_forward_nhwc_c by generic reference_batchnorm_forward

* Improvement to the batchnorm infer base API

* Add batchnorm forward client example which shows using the batchnorm forward external API

* Add test for batchnorm forward

* Tuning the batchnorm profiler initialized values and error threshold

* Add support for bhalf_t in instances/external api/tests

* Add support for int8_t in instances/external api/tests

* Add support for double in instances/external api/tests

* Let ScaleDataType and BiasDataType be same as XDataType and YDataType when creating instances

* Checking before running best instance in batchnorm_fwd_nhwc client example

* Add checking for YElementwiseOp in batchnorm_forward external API

* Add more types in batchnorm forward profiler

* Add more test lengths

Co-authored-by: rocking5566 <ChunYu.Lai@amd.com>
This commit is contained in:
Qianfeng
2022-11-25 08:02:27 +08:00
committed by GitHub
parent 43a889b72e
commit 4e6a5575be
26 changed files with 2685 additions and 522 deletions
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31
include/ck/tensor_operation/gpu/device/device_batchnorm_forward.hpp
View File

@@ -13,7 +13,15 @@ namespace ck {
namespace tensor_operation {
namespace device {
template <index_t Rank, index_t NumBatchNormReduceDim, typename YElementwiseOp>
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
index_t Rank,
index_t NumBatchNormReduceDim>
struct DeviceBatchNormFwd : public BaseOperator
{
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
@@ -40,9 +48,24 @@ struct DeviceBatchNormFwd : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <index_t Rank, index_t NumBatchNormReduceDim, typename YElementwiseOp>
using DeviceBatchNormFwdPtr =
std::unique_ptr<DeviceBatchNormFwd<Rank, NumBatchNormReduceDim, YElementwiseOp>>;
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
index_t Rank,
index_t NumBatchNormReduceDim>
using DeviceBatchNormFwdPtr = std::unique_ptr<DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
Rank,
NumBatchNormReduceDim>>;
} // namespace device
} // namespace tensor_operation

32
include/ck/tensor_operation/gpu/device/device_batchnorm_infer.hpp
View File

@@ -13,13 +13,22 @@ namespace ck {
namespace tensor_operation {
namespace device {
template <index_t Rank, index_t NumBatchNormReduceDim>
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
index_t Rank,
index_t NumBatchNormReduceDim>
struct DeviceBatchNormInfer : public BaseOperator
{
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(
const std::array<index_t, Rank> xyLengths,
const std::array<index_t, Rank> xStrides,
const std::array<index_t, Rank> yStrides,
const std::array<int, NumBatchNormReduceDim> reduceDims,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleBiasMeanVarLengths,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnScaleStrides,
const std::array<index_t, Rank - NumBatchNormReduceDim> bnBiasStrides,
@@ -28,6 +37,7 @@ struct DeviceBatchNormInfer : public BaseOperator
const void* bnScale,
const void* bnBias,
double epsilon,
const YElementwiseOp y_elementwise_op,
const void* estimatedMean,
const void* estimatedInvVariance,
void* p_y) = 0;
@@ -35,8 +45,24 @@ struct DeviceBatchNormInfer : public BaseOperator
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <index_t Rank, index_t NumBatchNormReduceDim>
using DeviceBatchNormInferPtr = std::unique_ptr<DeviceBatchNormInfer<Rank, NumBatchNormReduceDim>>;
template <typename XDataType,
typename YDataType,
typename AccDataType,
typename ScaleDataType,
typename BiasDataType,
typename MeanVarDataType,
typename YElementwiseOp,
index_t Rank,
index_t NumBatchNormReduceDim>
using DeviceBatchNormInferPtr = std::unique_ptr<DeviceBatchNormInfer<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
Rank,
NumBatchNormReduceDim>>;
} // namespace device
} // namespace tensor_operation

11
include/ck/tensor_operation/gpu/device/impl/device_batchnorm_forward_impl.hpp
View File

@@ -42,8 +42,15 @@ template <typename XDataType,
index_t ScaleSrcVectorSize,
index_t BiasSrcVectorSize,
index_t MeanVarSrcDstVectorSize>
struct DeviceBatchNormFwdImpl
: public DeviceBatchNormFwd<Rank, NumBatchNormReduceDim, YElementwiseOp>
struct DeviceBatchNormFwdImpl : public DeviceBatchNormFwd<XDataType,
YDataType,
AccDataType,
ScaleDataType,
BiasDataType,
MeanVarDataType,
YElementwiseOp,
Rank,
NumBatchNormReduceDim>
{
static_assert(Rank <= 6, "Bigger Rank size is not supported!");
static_assert(BlockSize == MThreadClusterSize * KThreadClusterSize,