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未验证 提交 ba8414d3 编写于 作者: C Chen Weihang 提交者： GitHub
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replace CUDNN_ENFORCE with PADDLE_ENFORCE_CUDA_SUCCESS, test=develop (#22109)

上级 cce9af0e
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Showing with 510 addition and 421 deletion
paddle/fluid/operators/batch_norm_op.cu
浏览文件 @ ba8414d3
......@@ -94,8 +94,9 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
cudnnTensorDescriptor_t bn_param_desc_;
cudnnBatchNormMode_t mode_;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&bn_param_desc_));
if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
......@@ -124,12 +125,14 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
dims = {N, C, H, W, D};
strides = {H * W * D * C, 1, W * D * C, D * C, C};
}
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
// Note: PERSISTENT not implemented for inference
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_, data_desc_, is_test ? CUDNN_BATCHNORM_SPATIAL : mode_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_, data_desc_,
is_test ? CUDNN_BATCHNORM_SPATIAL : mode_));
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
......@@ -149,17 +152,18 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
PADDLE_ENFORCE_EQ(est_mean->dims()[0], C);
PADDLE_ENFORCE_EQ(est_var->dims()[0], C);
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationForwardInference(
handle,
// Note: PERSISTENT not implemented for inference
CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
transformed_x.template data<T>(), data_desc_,
transformed_y.template mutable_data<T>(ctx.GetPlace()),
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
bias->template data<BatchNormParamType<T>>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(), epsilon));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardInference(
handle,
// Note: PERSISTENT not implemented for inference
CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
transformed_x.template data<T>(), data_desc_,
transformed_y.template mutable_data<T>(ctx.GetPlace()),
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
bias->template data<BatchNormParamType<T>>(),
est_mean->template data<BatchNormParamType<T>>(),
est_var->template data<BatchNormParamType<T>>(), epsilon));
} else {
// if MomentumTensor is set, use MomentumTensor value, momentum
// is only used in this training branch
......@@ -214,7 +218,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
"The argument ReserveSpace of batch_norm op is not found."));
// --------------- cudnn batchnorm workspace ---------------
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize(
/*handle=*/handle,
......@@ -228,7 +232,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
/*sizeInBytes=*/&workspace_size));
// -------------- cudnn batchnorm reserve space --------------
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
cudnnGetBatchNormalizationTrainingExReserveSpaceSize(
/*handle=*/handle,
......@@ -242,7 +246,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
ctx.GetPlace(), transformed_x.type(), reserve_space_size);
workspace_ptr = workspace_tensor.mutable_data(
ctx.GetPlace(), transformed_x.type(), workspace_size);
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardTrainingEx(
handle, mode_, CUDNN_BATCHNORM_OPS_BN,
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
......@@ -264,7 +268,7 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
}
#endif
if (!called) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardTraining(
handle, mode_, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
......@@ -292,8 +296,9 @@ class BatchNormKernel<platform::CUDADeviceContext, T>
ctx, &transformed_y, y);
}
// clean when exit.
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(bn_param_desc_));
}
};
......@@ -516,9 +521,9 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
cudnnTensorDescriptor_t bn_param_desc_;
cudnnBatchNormMode_t mode_;
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&bn_param_desc_));
if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
LOG(ERROR) << "Provided epsilon is smaller than "
......@@ -536,11 +541,12 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
mode_ = CUDNN_BATCHNORM_SPATIAL;
#endif
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
bn_param_desc_, data_desc_, mode_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDeriveBNTensorDescriptor(bn_param_desc_,
data_desc_, mode_));
const auto *saved_mean = ctx.Input<Tensor>("SavedMean");
const auto *saved_var = ctx.Input<Tensor>("SavedVariance");
......@@ -559,76 +565,79 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
Tensor workspace_tensor;
auto reserve_space_size = reserve_space->memory_size();
// --------------- cudnn batchnorm workspace ---------------
CUDNN_ENFORCE(platform::dynload::
cudnnGetBatchNormalizationBackwardExWorkspaceSize(
/*handle=*/dev_ctx.cudnn_handle(),
/*mode=*/mode_,
/*bnIps=*/CUDNN_BATCHNORM_OPS_BN,
/*xDesc=*/data_desc_,
/*yDesc=*/data_desc_,
/*dyDesc=*/data_desc_,
/*dzDesc=*/nullptr,
/*dxDesc=*/data_desc_,
/*bnScaleBiasMeanVarDesc=*/bn_param_desc_,
/*activationDesc=*/nullptr,
/*sizeInBytes=*/&workspace_size));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
cudnnGetBatchNormalizationBackwardExWorkspaceSize(
/*handle=*/dev_ctx.cudnn_handle(),
/*mode=*/mode_,
/*bnIps=*/CUDNN_BATCHNORM_OPS_BN,
/*xDesc=*/data_desc_,
/*yDesc=*/data_desc_,
/*dyDesc=*/data_desc_,
/*dzDesc=*/nullptr,
/*dxDesc=*/data_desc_,
/*bnScaleBiasMeanVarDesc=*/bn_param_desc_,
/*activationDesc=*/nullptr,
/*sizeInBytes=*/&workspace_size));
workspace_ptr = workspace_tensor.mutable_data(
ctx.GetPlace(), transformed_x.type(), workspace_size);
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackwardEx(
/*handle=*/dev_ctx.cudnn_handle(),
/*mode=*/mode_,
/*bnOps=*/CUDNN_BATCHNORM_OPS_BN,
/*alphaDataDiff=*/CudnnDataType<T>::kOne(),
/*betaDataDiff=*/CudnnDataType<T>::kZero(),
/*alphaParamDiff=*/CudnnDataType<T>::kOne(),
/*betaParamDiff=*/CudnnDataType<T>::kZero(),
/*xDesc=*/data_desc_,
/*xData=*/transformed_x.template data<T>(),
/*yDesc=*/nullptr,
/*yData=*/nullptr,
/*dyDesc=*/data_desc_,
/*dyData=*/transformed_d_y.template data<T>(),
/*dzDesc=*/nullptr,
/*dzData=*/nullptr,
/*dxDesc=*/data_desc_,
/*dxData=*/transformed_d_x.template mutable_data<T>(
ctx.GetPlace()),
/*dBnScaleBiasDesc=*/bn_param_desc_,
/*bnScaleData=*/scale->template data<BatchNormParamType<T>>(),
/*bnBiasData=*/nullptr,
/*dBnScaleData=*/d_scale
->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
/*dBnBiasData=*/d_bias
->template mutable_data<BatchNormParamType<T>>(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationBackwardEx(
/*handle=*/dev_ctx.cudnn_handle(),
/*mode=*/mode_,
/*bnOps=*/CUDNN_BATCHNORM_OPS_BN,
/*alphaDataDiff=*/CudnnDataType<T>::kOne(),
/*betaDataDiff=*/CudnnDataType<T>::kZero(),
/*alphaParamDiff=*/CudnnDataType<T>::kOne(),
/*betaParamDiff=*/CudnnDataType<T>::kZero(),
/*xDesc=*/data_desc_,
/*xData=*/transformed_x.template data<T>(),
/*yDesc=*/nullptr,
/*yData=*/nullptr,
/*dyDesc=*/data_desc_,
/*dyData=*/transformed_d_y.template data<T>(),
/*dzDesc=*/nullptr,
/*dzData=*/nullptr,
/*dxDesc=*/data_desc_,
/*dxData=*/transformed_d_x.template mutable_data<T>(
ctx.GetPlace()),
/*epsilon=*/epsilon,
/*savedMean=*/saved_mean_data,
/*savedInvVariance=*/saved_var_data,
/*activationDesc=*/nullptr,
/*workspace=*/workspace_ptr,
/*workSpaceSizeInBytes=*/workspace_size,
/*reserveSpace=*/const_cast<T *>(
reserve_space->template data<T>()),
/*reserveSpaceSizeInBytes=*/reserve_space_size));
/*dBnScaleBiasDesc=*/bn_param_desc_,
/*bnScaleData=*/scale->template data<BatchNormParamType<T>>(),
/*bnBiasData=*/nullptr,
/*dBnScaleData=*/d_scale
->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
/*dBnBiasData=*/d_bias
->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
/*epsilon=*/epsilon,
/*savedMean=*/saved_mean_data,
/*savedInvVariance=*/saved_var_data,
/*activationDesc=*/nullptr,
/*workspace=*/workspace_ptr,
/*workSpaceSizeInBytes=*/workspace_size,
/*reserveSpace=*/const_cast<T *>(
reserve_space->template data<T>()),
/*reserveSpaceSizeInBytes=*/reserve_space_size));
}
#endif
if (!called) {
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
transformed_x.template data<T>(), data_desc_,
transformed_d_y.template data<T>(), data_desc_,
transformed_d_x.template mutable_data<T>(ctx.GetPlace()),
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
d_scale->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
d_bias->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
epsilon, saved_mean_data, saved_var_data));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationBackward(
dev_ctx.cudnn_handle(), mode_, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_,
transformed_x.template data<T>(), data_desc_,
transformed_d_y.template data<T>(), data_desc_,
transformed_d_x.template mutable_data<T>(ctx.GetPlace()),
bn_param_desc_, scale->template data<BatchNormParamType<T>>(),
d_scale->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
d_bias->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
epsilon, saved_mean_data, saved_var_data));
}
if (data_layout == DataLayout::kNHWC &&
......@@ -658,9 +667,9 @@ class BatchNormGradKernel<platform::CUDADeviceContext, T>
}
// clean when exit.
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(bn_param_desc_));
} else {
const auto *running_mean = ctx.Input<Tensor>("Mean");
......
paddle/fluid/operators/conv_cudnn_helper.h
浏览文件 @ ba8414d3
......@@ -133,12 +133,14 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_TENSOR_OP_MATH));
VLOG(5) << "use cudnn_tensor_op_math";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_DEFAULT_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math";
}
#endif
......@@ -148,10 +150,11 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
int perf_count;
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(new perf_t[kNUM_CUDNN_FWD_ALGS]);
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
args.handle, args.idesc.desc(), args.wdesc.desc(), args.cdesc.desc(),
args.odesc.desc(), kNUM_CUDNN_FWD_ALGS, &perf_count,
perf_results.get()));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm_v7(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(), kNUM_CUDNN_FWD_ALGS,
&perf_count, perf_results.get()));
algo = (perf_results.get())[best_algo_idx].algo;
workspace_size = GetWorkspaceSize(args, algo);
......@@ -163,17 +166,20 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, workspace_size_limit,
&algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
}
#else
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(), args.cdesc.desc(),
args.odesc.desc(), CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(),
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
#endif
VLOG(3) << "choose algo " << algo;
} else {
......@@ -197,7 +203,7 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
args.handle, args.idesc.desc(), args.x->data<T>(),
args.wdesc.desc(), args.w->data<T>(), args.cdesc.desc(),
......@@ -223,9 +229,10 @@ struct SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t> {
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
size_t workspace_size = 0;
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
args.handle, args.idesc.desc(), args.wdesc.desc(), args.cdesc.desc(),
args.odesc.desc(), algo, &workspace_size));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
args.handle, args.idesc.desc(), args.wdesc.desc(),
args.cdesc.desc(), args.odesc.desc(), algo, &workspace_size));
return workspace_size;
}
};
......@@ -249,12 +256,14 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_TENSOR_OP_MATH));
VLOG(5) << "use cudnn_tensor_op_math";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_DEFAULT_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math";
}
#endif
......@@ -265,7 +274,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(
new perf_t[kNUM_CUDNN_BWD_DATA_ALGS]);
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm_v7(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(), kNUM_CUDNN_BWD_DATA_ALGS,
......@@ -294,7 +303,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
......@@ -302,10 +311,12 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
workspace_size_limit, &algo));
}
#else
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
args.handle, args.wdesc.desc(), args.odesc.desc(), args.cdesc.desc(),
args.idesc.desc(), CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(),
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
#endif
} else if (deterministic) {
return CUDNN_CONVOLUTION_BWD_DATA_ALGO_1;
......@@ -330,7 +341,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
cudnnFindConvolutionBackwardDataAlgorithmEx(
args.handle, args.wdesc.desc(), args.w->data<T>(),
......@@ -359,7 +370,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t> {
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
size_t workspace_size = 0;
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
args.handle, args.wdesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.idesc.desc(), algo, &workspace_size));
......@@ -385,12 +396,14 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
if (dev_ctx.GetComputeCapability() >= 70 && dtype == CUDNN_DATA_HALF) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_TENSOR_OP_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_TENSOR_OP_MATH));
VLOG(5) << "use cudnn_tensor_op_math";
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
args.cdesc.desc(), CUDNN_DEFAULT_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(args.cdesc.desc(),
CUDNN_DEFAULT_MATH));
VLOG(5) << "NOT use cudnn_tensor_op_math";
}
#endif
......@@ -403,7 +416,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
int best_algo_idx = 0;
std::unique_ptr<perf_t[]> perf_results(
new perf_t[kNUM_CUDNN_BWD_FILTER_ALGS]);
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm_v7(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(), kNUM_CUDNN_BWD_FILTER_ALGS,
......@@ -418,7 +431,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
<< workspace_size_limit << ")";
}
if (!has_got_workspace_size) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
......@@ -426,7 +439,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
workspace_size_limit, &algo));
}
#else
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(),
......@@ -455,7 +468,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
int returned_algo_count;
std::array<perf_t, kNUM_CUDNN_FWD_ALGS> perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace_ptr) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::
cudnnFindConvolutionBackwardFilterAlgorithmEx(
args.handle, args.idesc.desc(), args.x->data<T>(),
......@@ -483,7 +496,7 @@ struct SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t> {
static size_t GetWorkspaceSize(const ConvArgs& args, algo_t algo) {
size_t workspace_size = 0;
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
args.handle, args.idesc.desc(), args.odesc.desc(),
args.cdesc.desc(), args.wdesc.desc(), algo, &workspace_size));
......
paddle/fluid/operators/conv_cudnn_op.cu
浏览文件 @ ba8414d3
......@@ -237,8 +237,9 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
// cudnn 7 can support groups, no need to do it manually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
args.cdesc.desc(), groups));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionGroupCount(args.cdesc.desc(),
groups));
groups = 1;
#endif
args.idesc.set(transformed_input, layout_format);
......@@ -276,12 +277,13 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
workspace_handle.RunFunc(
[&](void* workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, args.idesc.desc(),
input_data + i * group_offset_in, args.wdesc.desc(),
filter_data + i * group_offset_filter, args.cdesc.desc(), algo,
workspace_ptr, workspace_size, &beta, args.odesc.desc(),
output_data + i * group_offset_out));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionForward(
handle, &alpha, args.idesc.desc(),
input_data + i * group_offset_in, args.wdesc.desc(),
filter_data + i * group_offset_filter, args.cdesc.desc(),
algo, workspace_ptr, workspace_size, &beta,
args.odesc.desc(), output_data + i * group_offset_out));
},
workspace_size);
}
......@@ -596,13 +598,14 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args1.wdesc.desc(),
filter_data + i * group_offset_filter, args1.odesc.desc(),
output_grad_data + i * group_offset_out, args1.cdesc.desc(),
data_algo, cudnn_workspace_ptr, workspace_size, &beta,
args1.idesc.desc(),
transformed_input_grad_data + i * group_offset_in));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args1.wdesc.desc(),
filter_data + i * group_offset_filter, args1.odesc.desc(),
output_grad_data + i * group_offset_out,
args1.cdesc.desc(), data_algo, cudnn_workspace_ptr,
workspace_size, &beta, args1.idesc.desc(),
transformed_input_grad_data + i * group_offset_in));
},
workspace_size);
}
......@@ -639,13 +642,14 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
workspace_handle.RunFunc(
[&](void* cudnn_workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args2.idesc.desc(),
input_data + i * group_offset_in, args2.odesc.desc(),
output_grad_data + i * group_offset_out, args2.cdesc.desc(),
filter_algo, cudnn_workspace_ptr, workspace_size, &beta,
args2.wdesc.desc(),
filter_grad_data + i * group_offset_filter));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args2.idesc.desc(),
input_data + i * group_offset_in, args2.odesc.desc(),
output_grad_data + i * group_offset_out,
args2.cdesc.desc(), filter_algo, cudnn_workspace_ptr,
workspace_size, &beta, args2.wdesc.desc(),
filter_grad_data + i * group_offset_filter));
},
workspace_size);
}
......@@ -993,12 +997,14 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, args1.idesc.desc(),
ddx + i * group_offset_in, args1.wdesc.desc(),
w + i * group_offset_filter, args1.cdesc.desc(), fwd_algo1,
workspace_ptr, workspace_size, &beta, args1.odesc.desc(),
transformed_ddy_channel + i * group_offset_out));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionForward(
handle, &alpha, args1.idesc.desc(),
ddx + i * group_offset_in, args1.wdesc.desc(),
w + i * group_offset_filter, args1.cdesc.desc(),
fwd_algo1, workspace_ptr, workspace_size, &beta,
args1.odesc.desc(),
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
}
......@@ -1007,12 +1013,14 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, args2.idesc.desc(), x + i * group_offset_in,
args2.wdesc.desc(), ddw + i * group_offset_filter,
args2.cdesc.desc(), fwd_algo2, workspace_ptr,
workspace_size, &alpha, args2.odesc.desc(),
transformed_ddy_channel + i * group_offset_out));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionForward(
handle, &alpha, args2.idesc.desc(),
x + i * group_offset_in, args2.wdesc.desc(),
ddw + i * group_offset_filter, args2.cdesc.desc(),
fwd_algo2, workspace_ptr, workspace_size, &alpha,
args2.odesc.desc(),
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
}
......@@ -1028,13 +1036,14 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args3.idesc.desc(), ddx + i * group_offset_in,
args3.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.cdesc.desc(), filter_algo, workspace_ptr,
workspace_size, &beta, args3.wdesc.desc(),
dw + i * group_offset_filter));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args3.idesc.desc(),
ddx + i * group_offset_in, args3.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.cdesc.desc(), filter_algo, workspace_ptr,
workspace_size, &beta, args3.wdesc.desc(),
dw + i * group_offset_filter));
},
workspace_size);
}
......@@ -1045,13 +1054,14 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < groups; i++) {
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args4.wdesc.desc(),
ddw + i * group_offset_filter, args4.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.cdesc.desc(), data_algo, workspace_ptr, workspace_size,
&beta, args4.idesc.desc(),
transformed_dx + i * group_offset_in));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args4.wdesc.desc(),
ddw + i * group_offset_filter, args4.odesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.cdesc.desc(), data_algo, workspace_ptr,
workspace_size, &beta, args4.idesc.desc(),
transformed_dx + i * group_offset_in));
},
workspace_size);
}
......
paddle/fluid/operators/conv_transpose_cudnn_op.cu
浏览文件 @ ba8414d3
......@@ -236,19 +236,21 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
// Get the algorithm
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_output_desc, CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_output_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
if (algo == 0 && FLAGS_cudnn_deterministic) {
algo = static_cast<cudnnConvolutionBwdDataAlgo_t>(1);
}
// get workspace size able to allocate
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
......@@ -263,11 +265,14 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc, filter_data + filter_offset * g,
cudnn_input_desc, input_data + input_offset * g, cudnn_conv_desc,
algo, cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_output_desc, transformed_output_data + output_offset * g));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_output_desc,
transformed_output_data + output_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
......@@ -466,19 +471,21 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle();
if (input_grad) {
// choose backward algorithm for data
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, data_algo, &fwd_ws_size));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_output_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_input_desc, data_algo, &fwd_ws_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, fwd_ws_size);
}
if (filter_grad) {
// choose backward algorithm for filter
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc,
......@@ -486,7 +493,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
workspace_size_limit, &filter_algo));
// get workspace for backwards filter algorithm
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize(
handle, cudnn_output_desc, cudnn_input_desc, cudnn_conv_desc,
cudnn_filter_desc, filter_algo, &bwd_filter_ws_size));
......@@ -507,12 +514,13 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Because beta is zero, it is unnecessary to reset input_grad.
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data + input_offset * g));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_filter_desc,
filter_data + filter_offset * g, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_input_desc, input_grad_data + input_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
......@@ -543,12 +551,13 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Gradient with respect to the filter
for (int g = 0; g < groups; g++) {
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_filter_desc, filter_grad_data + filter_offset * g));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_output_desc,
output_grad_data + output_grad_offset * g, cudnn_input_desc,
input_data + input_offset * g, cudnn_conv_desc, filter_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_filter_desc, filter_grad_data + filter_offset * g));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
......
paddle/fluid/operators/cudnn_lstm_op.cu.cc
浏览文件 @ ba8414d3
......@@ -91,7 +91,7 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
if (is_test) {
// for inference
CUDNN_ENFORCE(platform::dynload::cudnnRNNForwardInference(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardInference(
handle, cudnn_rnn_cache->rnn_desc_, run_seq_len,
cudnn_rnn_cache->x_desc_, x_data, cudnn_rnn_cache->hx_desc_,
init_h_data, cudnn_rnn_cache->cx_desc_, init_c_data,
......@@ -101,7 +101,7 @@ class CudnnLSTMGPUKernel : public framework::OpKernel<T> {
cudnn_rnn_cache->workspace_size_));
} else {
// for train
CUDNN_ENFORCE(platform::dynload::cudnnRNNForwardTraining(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNForwardTraining(
handle, cudnn_rnn_cache->rnn_desc_, run_seq_len,
cudnn_rnn_cache->x_desc_, x_data, cudnn_rnn_cache->hx_desc_,
init_h_data, cudnn_rnn_cache->cx_desc_, init_c_data,
......@@ -230,7 +230,7 @@ class CudnnLSTMGPUGradKernel : public framework::OpKernel<T> {
auto run_seq_len = input_dims[0];
PADDLE_ENFORCE_LE((size_t)run_seq_len, cudnn_rnn_cache->max_length_,
"cudnn running seq_len CAN not greater max_lengh");
CUDNN_ENFORCE(platform::dynload::cudnnRNNBackwardData(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardData(
handle, cudnn_rnn_cache->rnn_desc_, run_seq_len,
cudnn_rnn_cache->y_desc_, out_data, cudnn_rnn_cache->dy_desc_,
out_grad_data, cudnn_rnn_cache->dhy_desc_, last_h_grad_data,
......@@ -242,7 +242,7 @@ class CudnnLSTMGPUGradKernel : public framework::OpKernel<T> {
cudnn_rnn_cache->workspace_size_, reserve_data,
cudnn_rnn_cache->reserve_size_));
CUDNN_ENFORCE(platform::dynload::cudnnRNNBackwardWeights(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnRNNBackwardWeights(
handle, cudnn_rnn_cache->rnn_desc_, run_seq_len,
cudnn_rnn_cache->x_desc_, input->data<T>(), cudnn_rnn_cache->hx_desc_,
init_h->data<T>(), cudnn_rnn_cache->y_desc_, out->data<T>(),
......
paddle/fluid/operators/cudnn_rnn_cache.h
浏览文件 @ ba8414d3
......@@ -92,13 +92,13 @@ struct CudnnRNNCache {
int stride_a[3];
for (size_t i = 0; i < max_length_; ++i) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&x_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&y_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dx_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dy_desc_[i]));
dim_a[0] = batch_size_;
dim_a[1] = input_size_;
......@@ -107,9 +107,9 @@ struct CudnnRNNCache {
stride_a[0] = dim_a[2] * dim_a[1];
stride_a[1] = dim_a[2];
stride_a[2] = 1;
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
x_desc_[i], CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dx_desc_[i], CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
dim_a[0] = batch_size_;
......@@ -120,9 +120,9 @@ struct CudnnRNNCache {
stride_a[1] = dim_a[2];
stride_a[2] = 1;
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
y_desc_[i], CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dy_desc_[i], CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
}
......@@ -134,63 +134,74 @@ struct CudnnRNNCache {
stride_a[1] = dim_a[2];
stride_a[2] = 1;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&hx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&cx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&hy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&cy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&dhx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&dcx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&dhy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&dcy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&hx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&cx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&hy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&cy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dhx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dcx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dhy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&dcy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
hx_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
cx_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
hy_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
cy_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dhx_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dcx_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dhy_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
dcy_desc_, CUDNN_DATA_FLOAT, 3, dim_a, stride_a));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateDropoutDescriptor(&dropout_desc_));
size_t state_size;
CUDNN_ENFORCE(
platform::dynload::cudnnDropoutGetStatesSize(handle, &state_size);
dropout_state_.Resize({static_cast<int64_t>(state_size)}));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDropoutGetStatesSize(handle, &state_size));
dropout_state_.Resize({static_cast<int64_t>(state_size)});
auto *dropout_state_data = dropout_state_.mutable_data<uint8_t>(place);
CUDNN_ENFORCE(platform::dynload::cudnnSetDropoutDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetDropoutDescriptor(
dropout_desc_, handle, dropout_prob_, dropout_state_data, state_size,
seed_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateRNNDescriptor(&rnn_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateRNNDescriptor(&rnn_desc_));
#if CUDNN_VERSION >= 6000
CUDNN_ENFORCE(platform::dynload::cudnnSetRNNDescriptor_v6(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor_v6(
handle, rnn_desc_, hidden_size_, num_layers_, dropout_desc_,
CUDNN_LINEAR_INPUT,
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
CUDNN_RNN_ALGO_STANDARD, CUDNN_DATA_FLOAT));
#else
CUDNN_ENFORCE(platform::dynload::cudnnSetRNNDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNDescriptor(
rnn_desc_, hidden_size_, num_layers_, dropout_desc_, CUDNN_LINEAR_INPUT,
is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
CUDNN_DATA_FLOAT));
#endif
CUDNN_ENFORCE(platform::dynload::cudnnCreateFilterDescriptor(&w_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnCreateFilterDescriptor(&dw_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateFilterDescriptor(&w_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateFilterDescriptor(&dw_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnGetRNNParamsSize(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNParamsSize(
handle, rnn_desc_, x_desc_[0], &weights_size_, CUDNN_DATA_FLOAT));
PADDLE_ENFORCE_EQ(weights_size_, sizeof(float) * weight_numel,
......@@ -199,15 +210,16 @@ struct CudnnRNNCache {
dim_w[0] = weights_size_ / sizeof(float);
dim_w[1] = 1;
dim_w[2] = 1;
CUDNN_ENFORCE(platform::dynload::cudnnSetFilterNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetFilterNdDescriptor(
w_desc_, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 3, dim_w));
CUDNN_ENFORCE(platform::dynload::cudnnSetFilterNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetFilterNdDescriptor(
dw_desc_, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, 3, dim_w));
CUDNN_ENFORCE(platform::dynload::cudnnGetRNNWorkspaceSize(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnGetRNNWorkspaceSize(
handle, rnn_desc_, max_length_, x_desc_, &workspace_size_));
CUDNN_ENFORCE(platform::dynload::cudnnGetRNNTrainingReserveSize(
handle, rnn_desc_, max_length_, x_desc_, &reserve_size_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetRNNTrainingReserveSize(
handle, rnn_desc_, max_length_, x_desc_, &reserve_size_));
reserve_data_.Resize({static_cast<int64_t>(reserve_size_)});
reserve_data_.mutable_data<uint8_t>(place);
......@@ -218,13 +230,13 @@ struct CudnnRNNCache {
void release() {
for (size_t i = 0; i < max_length_; ++i) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(x_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(y_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dx_desc_[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dy_desc_[i]));
}
......@@ -233,21 +245,32 @@ struct CudnnRNNCache {
delete[] dx_desc_;
delete[] dy_desc_;
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(hx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(cx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(hy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(cy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(dhx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(dcx_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(dhy_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(dcy_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(hx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(cx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(hy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(cy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dhx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dcx_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dhy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(dcy_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyDropoutDescriptor(dropout_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyRNNDescriptor(rnn_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyRNNDescriptor(rnn_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyFilterDescriptor(w_desc_));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyFilterDescriptor(dw_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyFilterDescriptor(w_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyFilterDescriptor(dw_desc_));
}
};
......
paddle/fluid/operators/fused/conv_fusion_op.cu
浏览文件 @ ba8414d3
......@@ -164,8 +164,9 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(padding_common, strides, dilations);
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionGroupCount(cudnn_conv_desc,
groups));
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize<int>(transformed_input.dims()));
......@@ -196,16 +197,17 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
auto handle = dev_ctx.cudnn_handle();
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
auto x_dims = framework::vectorize(transformed_input.dims());
auto f_dims = framework::vectorize(filter->dims());
if (!exhaustive_search) {
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
VLOG(3) << "cuDNN forward algo " << algo;
} else {
auto search_func = [&]() {
......@@ -213,7 +215,7 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
std::array<cudnnConvolutionFwdAlgoPerf_t, kNUM_CUDNN_FWD_ALGS>
fwd_perf_stat;
auto cudnn_find_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnFindConvolutionForwardAlgorithmEx(
handle, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, cudnn_output_desc, output_data,
......@@ -248,9 +250,10 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
VLOG(3) << "choose algo " << algo;
}
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
......@@ -262,13 +265,13 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
// ------------- cudnn conv forward and bias add ---------------------
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionForward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnConvolutionForward(
handle, &alpha, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_output_desc, output_data));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
CUDNN_ENFORCE(platform::dynload::cudnnAddTensor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnAddTensor(
handle, &alpha, cudnn_bias_desc, bias_data, &alpha, cudnn_output_desc,
output_data));
} else {
......@@ -279,12 +282,13 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
ScalingParamType<T> alpha1 = 1.0f;
ScalingParamType<T> alpha2 = residual ? 1.0f : 0.0f;
auto cudnn_func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha1, cudnn_input_desc, input_data, cudnn_filter_desc,
filter_data, cudnn_conv_desc, algo, cudnn_workspace,
workspace_size_in_bytes, &alpha2, cudnn_output_desc, residual_data,
cudnn_bias_desc, bias_data, cudnn_act_desc, cudnn_output_desc,
output_data));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha1, cudnn_input_desc, input_data,
cudnn_filter_desc, filter_data, cudnn_conv_desc, algo,
cudnn_workspace, workspace_size_in_bytes, &alpha2,
cudnn_output_desc, residual_data, cudnn_bias_desc, bias_data,
cudnn_act_desc, cudnn_output_desc, output_data));
};
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
......
paddle/fluid/operators/fused/fusion_conv_inception_op.cu
浏览文件 @ ba8414d3
......@@ -95,15 +95,15 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
cudnnConvolutionDescriptor_t* conv_desc =
new cudnnConvolutionDescriptor_t[4];
for (int i = 0; i < 4; ++i) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateFilterDescriptor(&filter_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&bias_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&in_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&out_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateConvolutionDescriptor(&conv_desc[i]));
}
......@@ -127,11 +127,11 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < 4; ++i) {
filter_dims.push_back(framework::vectorize<int>(filters[i]->dims()));
CUDNN_ENFORCE(platform::dynload::cudnnSetFilterNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetFilterNdDescriptor(
filter_desc[i], cudnn_dtype, format, 4, filter_dims[i].data()));
bias_dims.push_back({1, filter_dims[i][0], 1, 1});
bias_strides.push_back({filter_dims[i][0], 1, 1, 1});
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
bias_desc[i], cudnn_dtype, 4, bias_dims[i].data(),
bias_strides[i].data()));
in_dims.push_back({n, filter_dims[i][1], h, w});
......@@ -140,22 +140,25 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
out_strides.push_back({oc * h * w, h * w, w, 1});
if (i < 2) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionNdDescriptor(
conv_desc[i], 2, k0x0.data(), k1x1.data(), k1x1.data(),
CUDNN_CROSS_CORRELATION, compute_type));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionNdDescriptor(
conv_desc[i], 2, k0x0.data(), k1x1.data(), k1x1.data(),
CUDNN_CROSS_CORRELATION, compute_type));
} else {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionNdDescriptor(
conv_desc[i], 2, k1x1.data(), k1x1.data(), k1x1.data(),
CUDNN_CROSS_CORRELATION, compute_type));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionNdDescriptor(
conv_desc[i], 2, k1x1.data(), k1x1.data(), k1x1.data(),
CUDNN_CROSS_CORRELATION, compute_type));
}
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
conv_desc[i], CUDNN_DEFAULT_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(conv_desc[i],
CUDNN_DEFAULT_MATH));
}
in_dims[2][1] *= 2;
in_strides[2][0] = oc * h * w;
out_strides[2][0] = filter_dims[2][0] * h * w; // this out is continuous.
in_strides[3][0] = filter_dims[2][0] * h * w;
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionGroupCount(conv_desc[2], 2));
cudnnConvolutionFwdAlgo_t algo[4];
......@@ -171,19 +174,21 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
}
for (int i = 0; i < 4; ++i) {
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
in_desc[i], cudnn_dtype, 4, in_dims[i].data(), in_strides[i].data()));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
out_desc[i], cudnn_dtype, 4, out_dims[i].data(),
out_strides[i].data()));
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, workspace_size_limit,
&algo[i]));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardAlgorithm(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo[i]));
size_t tmp_size = 0;
CUDNN_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
algo[i], &tmp_size));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, in_desc[i], filter_desc[i], conv_desc[i], out_desc[i],
algo[i], &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
cudnnActivationDescriptor_t cudnn_act_desc =
......@@ -196,7 +201,7 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
// branch1: pool + 1x1 conv
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
CUDNN_ENFORCE(platform::dynload::cudnnPoolingForward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnPoolingForward(
handle, cudnn_pool_desc, &alpha, cudnn_input_desc, input_data, &beta,
pool_out_desc, temp_data));
......@@ -218,13 +223,14 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
for (int i = 0; i < 4; ++i) {
auto func = [&](void* cudnn_workspace) {
CUDNN_ENFORCE(platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha, in_desc[i], in_datas[i], filter_desc[i],
static_cast<const void*>(filters[i]->data<T>()), conv_desc[i],
algo[i], cudnn_workspace, workspace_size_in_bytes, &beta,
out_desc[i], out_datas[i], bias_desc[i],
static_cast<const void*>(bias[i]->data<T>()), cudnn_act_desc,
out_desc[i], out_datas[i]));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnConvolutionBiasActivationForward(
handle, &alpha, in_desc[i], in_datas[i], filter_desc[i],
static_cast<const void*>(filters[i]->data<T>()), conv_desc[i],
algo[i], cudnn_workspace, workspace_size_in_bytes, &beta,
out_desc[i], out_datas[i], bias_desc[i],
static_cast<const void*>(bias[i]->data<T>()), cudnn_act_desc,
out_desc[i], out_datas[i]));
};
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
workspace_handle.RunFunc(func, workspace_size_in_bytes);
......@@ -232,31 +238,35 @@ class CUDNNConvInceptionFusionOpKernel : public framework::OpKernel<T> {
cudnnTensorDescriptor_t x_desc;
cudnnTensorDescriptor_t y_desc;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&x_desc));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&y_desc));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&x_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&y_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
x_desc, cudnn_dtype, 4, out_dims[3].data(), out_strides[2].data()));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
y_desc, cudnn_dtype, 4, out_dims[3].data(), out_strides[3].data()));
CUDNN_ENFORCE(platform::dynload::cudnnTransformTensor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnTransformTensor(
handle, CudnnDataType<T>::kOne(), x_desc,
static_cast<const void*>(out_datas[2]), CudnnDataType<T>::kZero(),
y_desc, static_cast<void*>(output_data + (oc0 + oc1) * h * w)));
for (int i = 0; i < 4; ++i) {
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(in_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(out_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyFilterDescriptor(filter_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(bias_desc[i]));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyConvolutionDescriptor(conv_desc[i]));
}
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(x_desc));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(y_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(x_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(y_desc));
}
};
#endif
......
paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.cu.cc
浏览文件 @ ba8414d3
......@@ -45,8 +45,10 @@ class TransposeFlattenConcatFusionKernel : public framework::OpKernel<T> {
cudnnTensorDescriptor_t in_desc;
cudnnTensorDescriptor_t out_desc;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&in_desc));
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&out_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&in_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&out_desc));
cudnnDataType_t cudnn_dtype = CudnnDataType<T>::type;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
......@@ -85,12 +87,12 @@ class TransposeFlattenConcatFusionKernel : public framework::OpKernel<T> {
dims_y[i] = 1;
}
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
in_desc, cudnn_dtype, max_dim, dims_y.data(), stride_x.data()));
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
out_desc, cudnn_dtype, max_dim, dims_y.data(), stride_y.data()));
CUDNN_ENFORCE(platform::dynload::cudnnTransformTensor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnTransformTensor(
handle, CudnnDataType<T>::kOne(), in_desc,
static_cast<const void*>(ins[k]->data<T>()),
CudnnDataType<T>::kZero(), out_desc, static_cast<void*>(odata)));
......@@ -101,8 +103,10 @@ class TransposeFlattenConcatFusionKernel : public framework::OpKernel<T> {
odata += flat_shape[1];
}
}
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(in_desc));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(out_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(in_desc));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(out_desc));
}
};
......
paddle/fluid/operators/grid_sampler_cudnn_op.cu.cc
浏览文件 @ ba8414d3
......@@ -59,7 +59,7 @@ class CUDNNGridSampleOpKernel : public framework::OpKernel<T> {
cudnnTensorDescriptor_t cudnn_output_desc = output_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize<int>(output->dims()));
CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerForward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSpatialTfSamplerForward(
handle, cudnn_st_desc, CudnnDataType<T>::kOne(), cudnn_input_desc,
input_data, grid_data, CudnnDataType<T>::kZero(), cudnn_output_desc,
output_data));
......@@ -111,12 +111,13 @@ class CUDNNGridSampleGradOpKernel : public framework::OpKernel<T> {
output_grad_desc.descriptor<T>(
DataLayout::kNCHW, framework::vectorize<int>(output_grad->dims()));
CUDNN_ENFORCE(platform::dynload::cudnnSpatialTfSamplerBackward(
handle, cudnn_st_dest, CudnnDataType<T>::kOne(), cudnn_input_desc,
input_data, CudnnDataType<T>::kZero(), cudnn_input_grad_desc,
input_grad_data, CudnnDataType<T>::kOne(), cudnn_output_grad_desc,
output_grad_data, grid_data, CudnnDataType<T>::kZero(),
grid_grad_data));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSpatialTfSamplerBackward(
handle, cudnn_st_dest, CudnnDataType<T>::kOne(), cudnn_input_desc,
input_data, CudnnDataType<T>::kZero(), cudnn_input_grad_desc,
input_grad_data, CudnnDataType<T>::kOne(), cudnn_output_grad_desc,
output_grad_data, grid_data, CudnnDataType<T>::kZero(),
grid_grad_data));
}
};
......
paddle/fluid/operators/instance_norm_op.cu
浏览文件 @ ba8414d3
......@@ -94,8 +94,9 @@ class InstanceNormKernel<platform::CUDADeviceContext, T>
cudnnTensorDescriptor_t data_desc_;
cudnnTensorDescriptor_t in_param_desc_;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&in_param_desc_));
if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
......@@ -113,11 +114,12 @@ class InstanceNormKernel<platform::CUDADeviceContext, T>
auto &dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
in_param_desc_, data_desc_, CUDNN_BATCHNORM_SPATIAL));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDeriveBNTensorDescriptor(
in_param_desc_, data_desc_, CUDNN_BATCHNORM_SPATIAL));
const auto *scale = ctx.Input<Tensor>("Scale");
const auto *bias = ctx.Input<Tensor>("Bias");
......@@ -152,19 +154,22 @@ class InstanceNormKernel<platform::CUDADeviceContext, T>
functor(dev_ctx, saved_mean, static_cast<BatchNormParamType<T>>(0));
functor(dev_ctx, saved_variance, static_cast<BatchNormParamType<T>>(0));
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationForwardTraining(
handle, CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_, x_tmp.template data<T>(),
data_desc_, y->template mutable_data<T>(ctx.GetPlace()), in_param_desc_,
scale_tmp.template data<BatchNormParamType<T>>(),
bias_tmp.template data<BatchNormParamType<T>>(), 0, nullptr, nullptr,
epsilon, saved_mean->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
saved_variance->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace())));
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationForwardTraining(
handle, CUDNN_BATCHNORM_SPATIAL, CudnnDataType<T>::kOne(),
CudnnDataType<T>::kZero(), data_desc_, x_tmp.template data<T>(),
data_desc_, y->template mutable_data<T>(ctx.GetPlace()),
in_param_desc_, scale_tmp.template data<BatchNormParamType<T>>(),
bias_tmp.template data<BatchNormParamType<T>>(), 0, nullptr,
nullptr, epsilon,
saved_mean->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
saved_variance->template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace())));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(in_param_desc_));
}
};
......@@ -291,8 +296,9 @@ class InstanceNormGradKernel<platform::CUDADeviceContext, T>
cudnnTensorDescriptor_t data_desc_;
cudnnTensorDescriptor_t in_param_desc_;
CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnCreateTensorDescriptor(&in_param_desc_));
if (epsilon <= CUDNN_BN_MIN_EPSILON - FLT_EPSILON) {
LOG(ERROR) << "Provided epsilon is smaller than "
......@@ -301,11 +307,12 @@ class InstanceNormGradKernel<platform::CUDADeviceContext, T>
}
epsilon = std::max(epsilon, CUDNN_BN_MIN_EPSILON);
CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetTensorNdDescriptor(
data_desc_, CudnnDataType<T>::type,
x_dims.size() > 3 ? x_dims.size() : 4, dims.data(), strides.data()));
CUDNN_ENFORCE(platform::dynload::cudnnDeriveBNTensorDescriptor(
in_param_desc_, data_desc_, CUDNN_BATCHNORM_SPATIAL));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDeriveBNTensorDescriptor(
in_param_desc_, data_desc_, CUDNN_BATCHNORM_SPATIAL));
const auto *saved_mean = ctx.Input<Tensor>("SavedMean");
const auto *saved_var = ctx.Input<Tensor>("SavedVariance");
......@@ -314,18 +321,19 @@ class InstanceNormGradKernel<platform::CUDADeviceContext, T>
const auto *saved_var_data =
saved_var->template data<BatchNormParamType<T>>();
if (d_scale && d_bias) {
CUDNN_ENFORCE(platform::dynload::cudnnBatchNormalizationBackward(
dev_ctx.cudnn_handle(), CUDNN_BATCHNORM_SPATIAL,
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(), data_desc_,
x_tmp.template data<T>(), data_desc_, d_y_tmp.template data<T>(),
data_desc_, d_x->template mutable_data<T>(ctx.GetPlace()),
in_param_desc_, scale_tmp.template data<BatchNormParamType<T>>(),
d_scale_tmp.template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
d_bias_tmp.template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
epsilon, saved_mean_data, saved_var_data));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnBatchNormalizationBackward(
dev_ctx.cudnn_handle(), CUDNN_BATCHNORM_SPATIAL,
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(),
CudnnDataType<T>::kOne(), CudnnDataType<T>::kZero(), data_desc_,
x_tmp.template data<T>(), data_desc_, d_y_tmp.template data<T>(),
data_desc_, d_x->template mutable_data<T>(ctx.GetPlace()),
in_param_desc_, scale_tmp.template data<BatchNormParamType<T>>(),
d_scale_tmp.template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
d_bias_tmp.template mutable_data<BatchNormParamType<T>>(
ctx.GetPlace()),
epsilon, saved_mean_data, saved_var_data));
} else {
if (d_x) {
GradComputeDX<T, block><<<NxC, block, 0, dev_ctx.stream()>>>(
......@@ -342,8 +350,9 @@ class InstanceNormGradKernel<platform::CUDADeviceContext, T>
d_bias_tmp.data<T>(), d_bias->data<T>(), N, C);
}
CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(data_desc_));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnDestroyTensorDescriptor(in_param_desc_));
}
};
......
paddle/fluid/operators/math/softmax.cu
浏览文件 @ ba8414d3
......@@ -49,7 +49,7 @@ void SoftmaxCUDNNFunctor<T>::operator()(
xDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_y_desc =
xDesc.descriptor<T>(layout, cudnn_tensor_dims);
CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxForward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSoftmaxForward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_x_desc,
X->data<T>(), CudnnDataType<T>::kZero(), cudnn_y_desc,
......@@ -80,7 +80,7 @@ void SoftmaxGradCUDNNFunctor<T>::operator()(
dxDesc.descriptor<T>(layout, cudnn_tensor_dims);
cudnnTensorDescriptor_t cudnn_ygrad_desc =
dyDesc.descriptor<T>(layout, cudnn_tensor_dims);
CUDNN_ENFORCE(platform::dynload::cudnnSoftmaxBackward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSoftmaxBackward(
context.cudnn_handle(), CUDNN_SOFTMAX_ACCURATE,
CUDNN_SOFTMAX_MODE_INSTANCE, CudnnDataType<T>::kOne(), cudnn_y_desc,
Y->data<T>(), cudnn_ygrad_desc, YGrad->data<T>(),
......
paddle/fluid/operators/pool_cudnn_op.cu.cc
浏览文件 @ ba8414d3
......@@ -156,7 +156,7 @@ class PoolCUDNNOpKernel : public framework::OpKernel<T> {
auto handle = ctx.cuda_device_context().cudnn_handle();
ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
CUDNN_ENFORCE(platform::dynload::cudnnPoolingForward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnPoolingForward(
handle, cudnn_pool_desc, &alpha, cudnn_input_desc,
tranformed_input_data, &beta, cudnn_output_desc,
tranformed_output_data));
......@@ -312,7 +312,7 @@ class PoolCUDNNGradOpKernel : public framework::OpKernel<T> {
T *input_grad_data = transformed_input_grad.mutable_data<T>(
transformed_input_grad.dims(), ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset input_grad.
CUDNN_ENFORCE(platform::dynload::cudnnPoolingBackward(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnPoolingBackward(
handle, cudnn_pool_desc, &alpha, cudnn_output_desc, output_data,
cudnn_output_desc, output_grad_data, cudnn_input_desc, input_data,
&beta, cudnn_input_desc, input_grad_data));
......
paddle/fluid/platform/cudnn_desc.h
浏览文件 @ ba8414d3
......@@ -83,19 +83,21 @@ class ActivationDescriptor {
struct Deleter {
void operator()(T* t) {
if (t != nullptr) {
CUDNN_ENFORCE(dynload::cudnnDestroyActivationDescriptor(t));
PADDLE_ENFORCE_CUDA_SUCCESS(
dynload::cudnnDestroyActivationDescriptor(t));
t = nullptr;
}
}
};
ActivationDescriptor() {
T* raw_ptr;
CUDNN_ENFORCE(dynload::cudnnCreateActivationDescriptor(&raw_ptr));
PADDLE_ENFORCE_CUDA_SUCCESS(
dynload::cudnnCreateActivationDescriptor(&raw_ptr));
desc_.reset(raw_ptr);
}
template <typename T>
void set(cudnnActivationMode_t mode, const T& coef) {
CUDNN_ENFORCE(dynload::cudnnSetActivationDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetActivationDescriptor(
desc_.get(), mode, CUDNN_NOT_PROPAGATE_NAN, static_cast<double>(coef)));
}
......@@ -112,14 +114,14 @@ class TensorDescriptor {
struct Deleter {
void operator()(T* t) {
if (t != nullptr) {
CUDNN_ENFORCE(dynload::cudnnDestroyTensorDescriptor(t));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnDestroyTensorDescriptor(t));
t = nullptr;
}
}
};
TensorDescriptor() {
T* raw_ptr;
CUDNN_ENFORCE(dynload::cudnnCreateTensorDescriptor(&raw_ptr));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnCreateTensorDescriptor(&raw_ptr));
desc_.reset(raw_ptr);
}
T* desc() { return desc_.get(); }
......@@ -135,7 +137,7 @@ class TensorDescriptor {
if (groups > 1) {
dims_with_group[1] = dims_with_group[1] / groups;
}
CUDNN_ENFORCE(dynload::cudnnSetTensorNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetTensorNdDescriptor(
desc_.get(), ToCudnnDataType(tensor.type()), dims_with_group.size(),
dims_with_group.data(), strides.data()));
}
......@@ -148,7 +150,7 @@ class TensorDescriptor {
} else {
transformed_dims = dims;
}
CUDNN_ENFORCE(dynload::cudnnSetTensorNdDescriptorEx(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetTensorNdDescriptorEx(
desc_.get(), format, ToCudnnDataType(tensor.type()),
transformed_dims.size(), transformed_dims.data()));
}
......@@ -163,14 +165,14 @@ class FilterDescriptor {
struct Deleter {
void operator()(T* t) {
if (t != nullptr) {
CUDNN_ENFORCE(dynload::cudnnDestroyFilterDescriptor(t));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnDestroyFilterDescriptor(t));
t = nullptr;
}
}
};
FilterDescriptor() {
T* raw_ptr;
CUDNN_ENFORCE(dynload::cudnnCreateFilterDescriptor(&raw_ptr));
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnCreateFilterDescriptor(&raw_ptr));
desc_.reset(raw_ptr);
}
T* desc() { return desc_.get(); }
......@@ -188,7 +190,7 @@ class FilterDescriptor {
if (groups > 1) {
transformed_dims[1] = transformed_dims[1] / groups;
}
CUDNN_ENFORCE(dynload::cudnnSetFilterNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetFilterNdDescriptor(
desc_.get(), ToCudnnDataType(tensor.type()), format,
transformed_dims.size(), transformed_dims.data()));
}
......@@ -203,14 +205,16 @@ class ConvolutionDescriptor {
struct Deleter {
void operator()(T* t) {
if (t != nullptr) {
CUDNN_ENFORCE(dynload::cudnnDestroyConvolutionDescriptor(t));
PADDLE_ENFORCE_CUDA_SUCCESS(
dynload::cudnnDestroyConvolutionDescriptor(t));
t = nullptr;
}
}
};
ConvolutionDescriptor() {
T* raw_ptr;
CUDNN_ENFORCE(dynload::cudnnCreateConvolutionDescriptor(&raw_ptr));
PADDLE_ENFORCE_CUDA_SUCCESS(
dynload::cudnnCreateConvolutionDescriptor(&raw_ptr));
desc_.reset(raw_ptr);
}
T* desc() { return desc_.get(); }
......@@ -222,18 +226,19 @@ class ConvolutionDescriptor {
cudnnDataType_t compute_type =
(dtype == CUDNN_DATA_DOUBLE) ? CUDNN_DATA_DOUBLE : CUDNN_DATA_FLOAT;
T* desc = desc_.get();
CUDNN_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetConvolutionNdDescriptor(
desc, pads.size(), pads.data(), strides.data(), dilations.data(),
CUDNN_CROSS_CORRELATION, compute_type));
#if CUDNN_VERSION_MIN(7, 0, 1)
CUDNN_ENFORCE(
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionGroupCount(desc, groups));
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetConvolutionMathType(
desc, CUDNN_DEFAULT_MATH));
if (dtype == CUDNN_DATA_HALF) {
CUDNN_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
desc, CUDNN_TENSOR_OP_MATH));
PADDLE_ENFORCE_CUDA_SUCCESS(
platform::dynload::cudnnSetConvolutionMathType(desc,
CUDNN_TENSOR_OP_MATH));
}
#endif
#endif
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ ba8414d3
......@@ -60,14 +60,6 @@ inline const char* cudnnGetErrorString(cudnnStatus_t status) {
#define CUDNN_VERSION_MIN(major, minor, patch) \
(CUDNN_VERSION >= ((major)*1000 + (minor)*100 + (patch)))
#define CUDNN_ENFORCE(condition) \
do { \
auto status = condition; \
if (UNLIKELY(status != CUDNN_STATUS_SUCCESS)) { \
PADDLE_THROW(::paddle::platform::cudnnGetErrorString(status)); \
} \
} while (false)
enum class DataLayout { // Not use
kNHWC,
kNCHW,
......@@ -467,7 +459,7 @@ class ScopedActivationDescriptor {
PADDLE_THROW("unrecognized activation mode: %d .",
static_cast<int>(activation_mode));
}
CUDNN_ENFORCE(dynload::cudnnSetActivationDescriptor(
PADDLE_ENFORCE_CUDA_SUCCESS(dynload::cudnnSetActivationDescriptor(
desc_, mode, CUDNN_NOT_PROPAGATE_NAN, relu_ceiling));
return desc_;
}
......
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