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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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24
example/34_batchnorm/batchnorm_forward_nhwc.cpp
View File

@@ -15,7 +15,7 @@
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_forward_nhwc_c.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_forward.hpp"
#include "ck/tensor_operation/gpu/device/impl/device_batchnorm_forward_impl.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
@@ -142,6 +142,8 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
// when using lengths[] to create a tensor, lengths[0] is the length of highest dimension
// eg. N of NHWC, so lengths[3] is the dimension C length of NHWC
const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
// input data of the batchnorm forward algorithm
@@ -300,7 +302,7 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
i_inOutLengths,
i_inOutStrides,
i_inOutStrides,
{0, 1, 2},
{0, 1, 2}, // indicates physical indices of reduce dimensions in lengths[] and strides[]
i_scaleBiasMeanVarLengths,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
@@ -366,13 +368,15 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
{
using ReferenceBatchNormFwdInstance =
ck::tensor_operation::host::ReferenceBatchNormFwd_Input_N_H_W_C_Output_C<InOutDataType,
InOutDataType,
AccDataType,
AccDataType,
AccDataType,
AccDataType,
PassThroughOp>;
ck::tensor_operation::host::ReferenceBatchNormFwd<InOutDataType,
InOutDataType,
AccDataType,
AccDataType,
AccDataType,
AccDataType,
PassThroughOp,
Rank,
NumReduceDim>;
auto batchNormFwd_ref = ReferenceBatchNormFwdInstance{};
@@ -380,7 +384,7 @@ bool bnorm_fwd_nhwc_test(bool do_verification,
i_inOutLengths,
i_inOutStrides,
i_inOutStrides,
{0, 1, 2},
{0, 1, 2}, // indicates physical indices of reduce dimensions in lengths[] and strides[]
i_scaleBiasMeanVarLengths,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,

25
example/34_batchnorm/batchnorm_infer_nhwc.cpp
View File

@@ -15,7 +15,8 @@
#include "ck/library/utility/host_tensor.hpp"
#include "ck/library/utility/host_tensor_generator.hpp"
#include "ck/library/utility/host_common_util.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_infer_nhwc_c.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_infer.hpp"
#include "batchnorm_infer_impl.hpp"
@@ -124,6 +125,8 @@ bool bnorm_infer_nhwc_test(bool do_verification,
constexpr int Rank = 4;
constexpr int NumReduceDim = 3;
// when using lengths[] to create a tensor, lengths[0] is the length of highest dimension
// eg. N of NHWC, so lengths[3] is the dimension C length of NHWC
const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
// input data of the batchnorm forward algorithm
@@ -260,20 +263,25 @@ bool bnorm_infer_nhwc_test(bool do_verification,
if(do_verification)
{
using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
using ReferenceBatchNormInferInstance =
ck::tensor_operation::host::ReferenceBatchNormInfer_Input_N_H_W_C_Output_C<
InOutDataType,
InOutDataType,
AccDataType,
AccDataType,
AccDataType,
AccDataType>;
ck::tensor_operation::host::ReferenceBatchNormInfer<InOutDataType,
InOutDataType,
AccDataType,
AccDataType,
AccDataType,
AccDataType,
PassThroughOp,
Rank,
NumReduceDim>;
auto batchNormInfer_ref = ReferenceBatchNormInferInstance{};
auto argument_ptr_ref =
batchNormInfer_ref.MakeArgumentPointer(i_inOutLengths,
i_inOutStrides,
i_inOutStrides,
{0, 1, 2},
i_scaleBiasMeanVarLengths,
i_scaleBiasMeanVarStrides,
i_scaleBiasMeanVarStrides,
@@ -282,6 +290,7 @@ bool bnorm_infer_nhwc_test(bool do_verification,
bnScale.mData.data(),
bnBias.mData.data(),
epsilon,
PassThroughOp{},
estimatedMean.mData.data(),
estimatedVariance.mData.data(),
y_ref.mData.data());