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#include "caffe2/operators/order_switch_ops.h"
#include <algorithm>
#include <functional>
#include <vector>
#include "caffe2/core/context_gpu.h"
#include "caffe2/core/cudnn_wrappers.h"
#include "caffe2/core/types.h"
namespace caffe2 {
namespace {
class CuDNNOrderSwithOpBase : public Operator<CUDAContext> {
public:
USE_OPERATOR_FUNCTIONS(CUDAContext);
template <class... Args>
explicit CuDNNOrderSwithOpBase(Args&&... args)
: Operator<CUDAContext>(std::forward<Args>(args)...),
cudnn_wrapper_(&context_) {
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&X_desc_));
CUDNN_ENFORCE(cudnnCreateTensorDescriptor(&Y_desc_));
}
~CuDNNOrderSwithOpBase() override {
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(X_desc_));
CUDNN_ENFORCE(cudnnDestroyTensorDescriptor(Y_desc_));
}
protected:
// TODO: std::vector<int> -> std::vector<int64_t>
void SetTensorDescriptor(
const cudnnDataType_t data_type,
const StorageOrder order,
const std::vector<int>& data_dims,
cudnnTensorDescriptor_t data_desc) const {
const int ndim = data_dims.size();
const int N = data_dims[0];
const int C = order == StorageOrder::NCHW ? data_dims[1] : data_dims.back();
if (ndim == 3) {
const int H = 1;
const int W = order == StorageOrder::NCHW ? data_dims[2] : data_dims[1];
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc, GetCudnnTensorFormat(order), data_type, N, C, H, W));
} else if (ndim == 4) {
const int H = order == StorageOrder::NCHW ? data_dims[2] : data_dims[1];
const int W = order == StorageOrder::NCHW ? data_dims[3] : data_dims[2];
CUDNN_ENFORCE(cudnnSetTensor4dDescriptor(
data_desc, GetCudnnTensorFormat(order), data_type, N, C, H, W));
} else {
const int H = order == StorageOrder::NCHW ? data_dims[2] : data_dims[1];
const int W = order == StorageOrder::NCHW ? data_dims[3] : data_dims[2];
const auto l_iter = order == StorageOrder::NCHW ? data_dims.cbegin() + 4
: data_dims.cbegin() + 3;
const auto r_iter =
order == StorageOrder::NCHW ? data_dims.cend() : data_dims.cend() - 1;
const int D = std::accumulate(l_iter, r_iter, 1, std::multiplies<int>());
const std::array<int, 5> dims = {N, C, H, W, D};
const std::array<int, 5> strides = order == StorageOrder::NCHW
? std::array<int, 5>{C * H * W * D, H * W * D, W * D, D, 1}
: std::array<int, 5>{C * H * W * D, 1, W * D * C, D * C, C};
CUDNN_ENFORCE(cudnnSetTensorNdDescriptor(
data_desc, data_type, 5, dims.data(), strides.data()));
}
}
CuDNNWrapper cudnn_wrapper_;
cudnnTensorDescriptor_t X_desc_;
cudnnTensorDescriptor_t Y_desc_;
std::vector<int> cached_X_dims_;
};
class CuDNNNHWC2NCHWOp final : public CuDNNOrderSwithOpBase {
public:
template <class... Args>
explicit CuDNNNHWC2NCHWOp(Args&&... args)
: CuDNNOrderSwithOpBase(std::forward<Args>(args)...) {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<float, at::Half>>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& X = Input(0);
const int ndim = X.dim();
const int N = X.dim32(0);
const int C = X.dim32(ndim - 1);
const std::vector<int> X_dims(X.sizes().cbegin(), X.sizes().cend());
std::vector<int> Y_dims(ndim);
Y_dims[0] = N;
Y_dims[1] = C;
std::copy(X_dims.cbegin() + 1, X_dims.cend() - 1, Y_dims.begin() + 2);
std::vector<int64_t> Y_dims_64;
std::copy(Y_dims.cbegin(), Y_dims.cend(), std::back_inserter(Y_dims_64));
auto* Y = Output(0, Y_dims_64, at::dtype<T>());
if (cached_X_dims_ != X_dims) {
cached_X_dims_ = X_dims;
SetTensorDescriptor(
cudnnTypeWrapper<T>::type, StorageOrder::NHWC, X_dims, X_desc_);
SetTensorDescriptor(
cudnnTypeWrapper<T>::type, StorageOrder::NCHW, Y_dims, Y_desc_);
}
CUDNN_ENFORCE(cudnnTransformTensor(
cudnn_wrapper_.inline_cudnn_handle(),
cudnnTypeWrapper<T>::kOne(),
X_desc_,
X.template data<T>(),
cudnnTypeWrapper<T>::kZero(),
Y_desc_,
Y->template mutable_data<T>()));
return true;
}
};
class CuDNNNCHW2NHWCOp final : public CuDNNOrderSwithOpBase {
public:
template <class... Args>
explicit CuDNNNCHW2NHWCOp(Args&&... args)
: CuDNNOrderSwithOpBase(std::forward<Args>(args)...) {}
bool RunOnDevice() override {
return DispatchHelper<TensorTypes<float, at::Half>>::call(this, Input(0));
}
template <typename T>
bool DoRunWithType() {
const auto& X = Input(0);
const int ndim = X.dim();
const int N = X.dim32(0);
const int C = X.dim32(1);
const std::vector<int> X_dims(X.sizes().cbegin(), X.sizes().cend());
std::vector<int> Y_dims(ndim);
Y_dims[0] = N;
Y_dims[ndim - 1] = C;
std::copy(X_dims.cbegin() + 2, X_dims.cend(), Y_dims.begin() + 1);
std::vector<int64_t> Y_dims_64;
std::copy(Y_dims.cbegin(), Y_dims.cend(), std::back_inserter(Y_dims_64));
auto* Y = Output(0, Y_dims_64, at::dtype<T>());
if (cached_X_dims_ != X_dims) {
cached_X_dims_ = X_dims;
SetTensorDescriptor(
cudnnTypeWrapper<T>::type, StorageOrder::NCHW, X_dims, X_desc_);
SetTensorDescriptor(
cudnnTypeWrapper<T>::type, StorageOrder::NHWC, Y_dims, Y_desc_);
}
CUDNN_ENFORCE(cudnnTransformTensor(
cudnn_wrapper_.inline_cudnn_handle(),
cudnnTypeWrapper<T>::kOne(),
X_desc_,
X.template data<T>(),
cudnnTypeWrapper<T>::kZero(),
Y_desc_,
Y->template mutable_data<T>()));
return true;
}
};
} // namespace
REGISTER_CUDNN_OPERATOR(NHWC2NCHW, CuDNNNHWC2NCHWOp);
REGISTER_CUDNN_OPERATOR(NCHW2NHWC, CuDNNNCHW2NHWCOp);
} // namespace caffe2
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