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https://github.com/ROCm/composable_kernel.git
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BatchNorm backward instance/external API/profiler/tests (#519)
* Refine the device batchnorm-backward base API templates and data type assignments * Remove duplicated kernel file * Add batchnorm backward instances and external API * Add batchnorm-backward profiler and tests * Add client example which uses batchnorm backward external API * Merge test/batchnorm_fwd and test/batchnorm_bwd into one directory * Loose the threshold for batchnorm-backward check_err()
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Qianfeng
@@ -11,7 +11,7 @@
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#include "ck/library/utility/host_tensor.hpp"
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#include "ck/library/utility/host_tensor_generator.hpp"
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#include "ck/library/utility/host_common_util.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward_nhwc_c.hpp"
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#include "ck/library/reference_tensor_operation/cpu/reference_batchnorm_backward.hpp"
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#include "ck/tensor_operation/gpu/device/impl/device_batchnorm_backward_impl.hpp"
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static struct option long_options[] = {{"inOutLengths", required_argument, nullptr, 'D'},
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@@ -106,7 +106,7 @@ class BatchNormBwdArg
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using namespace ck;
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template <typename InOutDataType, typename AccDataType, bool UseMultiblockInK>
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template <typename XDataType, typename AccDataType, bool UseMultiblockInK>
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bool bnorm_bwd_nhwc_test(bool do_verification,
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int init_method,
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bool time_kernel,
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@@ -118,13 +118,15 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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constexpr index_t Rank = 4;
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constexpr index_t NumReduceDim = 3;
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using ScaleDataType = XDataType;
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const std::vector<size_t> scaleBiasMeanVarLengths = {inOutLengths[3]};
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// input data of the batchnorm backward algorithm
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Tensor<InOutDataType> x(inOutLengths);
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Tensor<InOutDataType> dy(inOutLengths);
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Tensor<XDataType> x(inOutLengths);
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Tensor<AccDataType> dy(inOutLengths);
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Tensor<AccDataType> bnScale(scaleBiasMeanVarLengths);
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Tensor<ScaleDataType> bnScale(scaleBiasMeanVarLengths);
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Tensor<AccDataType> savedMean(scaleBiasMeanVarLengths);
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Tensor<AccDataType> savedInvVar(scaleBiasMeanVarLengths);
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@@ -132,8 +134,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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Tensor<AccDataType> savedVariance(scaleBiasMeanVarLengths);
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// output data of the batchnorm backward algorithm
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Tensor<InOutDataType> dx_ref(inOutLengths);
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Tensor<InOutDataType> dx(inOutLengths);
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Tensor<AccDataType> dx_ref(inOutLengths);
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Tensor<AccDataType> dx(inOutLengths);
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Tensor<AccDataType> dscale(scaleBiasMeanVarLengths);
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Tensor<AccDataType> dbias(scaleBiasMeanVarLengths);
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@@ -153,7 +155,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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const float noise_stddev = 0.0001f;
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// input data in normal distribution
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x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
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x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
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// initialize the savedMean to be values with tiny variation to the mean of the x values
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savedMean.GenerateTensorValue(GeneratorTensor_4<AccDataType>{x_mean, noise_stddev},
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@@ -182,7 +184,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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const float x_stddev = 1.0f;
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// input data in normal distribution
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x.GenerateTensorValue(GeneratorTensor_4<InOutDataType>{x_mean, x_stddev}, num_thread);
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x.GenerateTensorValue(GeneratorTensor_4<XDataType>{x_mean, x_stddev}, num_thread);
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};
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if(do_verification)
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@@ -190,34 +192,34 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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switch(init_method)
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{
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case 0:
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dy.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_0<InOutDataType>{}, num_thread);
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dy.GenerateTensorValue(GeneratorTensor_0<AccDataType>{}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_0<ScaleDataType>{}, num_thread);
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break;
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case 1:
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dy.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_1<InOutDataType>{1}, num_thread);
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dy.GenerateTensorValue(GeneratorTensor_1<AccDataType>{1}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_1<ScaleDataType>{1}, num_thread);
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break;
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case 2:
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dy.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_2<InOutDataType>{-5, 5}, num_thread);
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dy.GenerateTensorValue(GeneratorTensor_2<AccDataType>{-2, 2}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_2<ScaleDataType>{-5, 5}, num_thread);
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break;
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default:
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dy.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.2f, 0.2f}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_3<InOutDataType>{-0.5f, 0.5f}, num_thread);
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dy.GenerateTensorValue(GeneratorTensor_3<AccDataType>{-0.2f, 0.2f}, num_thread);
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bnScale.GenerateTensorValue(GeneratorTensor_3<ScaleDataType>{-0.5f, 0.5f}, num_thread);
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}
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};
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// input data of the batchnorm backward algorithm
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DeviceMem x_dev(sizeof(InOutDataType) * x.mDesc.GetElementSpaceSize());
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DeviceMem dy_dev(sizeof(InOutDataType) * dy.mDesc.GetElementSpaceSize());
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DeviceMem x_dev(sizeof(XDataType) * x.mDesc.GetElementSpaceSize());
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DeviceMem dy_dev(sizeof(AccDataType) * dy.mDesc.GetElementSpaceSize());
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DeviceMem bnScale_dev(sizeof(AccDataType) * bnScale.mDesc.GetElementSpaceSize());
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DeviceMem bnScale_dev(sizeof(ScaleDataType) * bnScale.mDesc.GetElementSpaceSize());
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DeviceMem savedMean_dev(sizeof(AccDataType) * savedMean.mDesc.GetElementSpaceSize());
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DeviceMem savedInvVar_dev(sizeof(AccDataType) * savedInvVar.mDesc.GetElementSpaceSize());
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// output data of the batchnorm backward algorithm
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DeviceMem dx_dev(sizeof(InOutDataType) * dx.mDesc.GetElementSpaceSize());
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DeviceMem dx_dev(sizeof(AccDataType) * dx.mDesc.GetElementSpaceSize());
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DeviceMem dscale_dev(sizeof(AccDataType) * dscale.mDesc.GetElementSpaceSize());
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DeviceMem dbias_dev(sizeof(AccDataType) * dbias.mDesc.GetElementSpaceSize());
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@@ -249,13 +251,13 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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using PassThroughOp = ck::tensor_operation::element_wise::PassThrough;
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using DeviceBatchNormBwdInstance =
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ck::tensor_operation::device::DeviceBatchNormBwdImpl<InOutDataType,
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InOutDataType,
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InOutDataType,
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ck::tensor_operation::device::DeviceBatchNormBwdImpl<XDataType,
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AccDataType,
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AccDataType, // ScaleDataType
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AccDataType, // BiasDataType
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AccDataType, // MeanVarDataType
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AccDataType,
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AccDataType,
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ScaleDataType, // ScaleDataType
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AccDataType, // DscaleDbiasDataType
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AccDataType, // MeanVarDataType
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PassThroughOp,
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Rank,
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NumReduceDim,
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@@ -269,8 +271,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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1, // XSrcVectorSize
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1, // DySrcVectorSize
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1, // DxDstVectorSize
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1, // ScaleSrcDstVectorSize
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1, // BiasDstVectorSize
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1, // ScaleSrcVectorSize
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1, // DscaleDbiasDstVectorSize
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1>; // MeanVarSrcVectorSize
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auto batchnorm_bwd = DeviceBatchNormBwdInstance{};
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@@ -324,7 +326,7 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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// inputing of x, dy, scale, outputing of dx, dscale, dbias
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num_bytes +=
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total_length * sizeof(InOutDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
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total_length * sizeof(XDataType) * 3 + invariant_length * sizeof(AccDataType) * 3;
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// outputing of mean, inv-variance
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num_bytes += haveSavedMeanInvVar ? invariant_length * sizeof(AccDataType) * 2 : 0;
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@@ -341,14 +343,16 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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if(do_verification)
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{
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using ReferenceBatchNormBwdInstance =
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ck::tensor_operation::host::ReferenceBatchNormBwd_Input_N_H_W_C_Output_C<InOutDataType,
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InOutDataType,
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InOutDataType,
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AccDataType,
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AccDataType,
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AccDataType,
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AccDataType,
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PassThroughOp>;
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ck::tensor_operation::host::ReferenceBatchNormBwd<XDataType,
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AccDataType,
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AccDataType,
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AccDataType,
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ScaleDataType, // ScaleDataType
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AccDataType,
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AccDataType,
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PassThroughOp,
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Rank,
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NumReduceDim>;
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auto batchNormBwd_ref = ReferenceBatchNormBwdInstance{};
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@@ -390,8 +394,8 @@ bool bnorm_bwd_nhwc_test(bool do_verification,
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dbias_dev.FromDevice(dbias.data());
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// clang-format off
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pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 1e-5, 1e-5);
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pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 1e-5, 2e-4);
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pass = pass && ck::utils::check_err(dbias.mData, dbias_ref.mData, "dBias result:", 2e-4, 2e-4);
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pass = pass && ck::utils::check_err(dscale.mData, dscale_ref.mData, "dScale result:", 2e-4, 2e-4);
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pass = pass && ck::utils::check_err(dx.mData, dx_ref.mData, "dx result:");
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// clang-format on
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};
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