/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/fused/fusion_transpose_flatten_concat_op.h" #include #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/platform/cudnn_helper.h" namespace paddle { namespace operators { template using CudnnDataType = platform::CudnnDataType; template class TransposeFlattenConcatFusionKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto ins = ctx.MultiInput("X"); auto* out = ctx.Output("Out"); out->mutable_data(ctx.GetPlace()); auto odims = out->dims(); std::vector trans_axis = ctx.Attr>("trans_axis"); int flatten_axis = ctx.Attr("flatten_axis"); int concat_axis = ctx.Attr("concat_axis"); int rank = ins[0]->dims().size(); // use at least 4D in cudnnTransformTensor int max_dim = rank < 4 ? 4 : rank; std::vector stride_x(max_dim, 0); std::vector stride_y(max_dim, 0); std::vector dims_y(max_dim, 0); cudnnTensorDescriptor_t in_desc; cudnnTensorDescriptor_t out_desc; CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&in_desc)); CUDNN_ENFORCE(platform::dynload::cudnnCreateTensorDescriptor(&out_desc)); cudnnDataType_t cudnn_dtype = CudnnDataType::type; auto& dev_ctx = ctx.template device_context(); auto handle = dev_ctx.cudnn_handle(); T* odata = out->data(); for (size_t k = 0; k < ins.size(); ++k) { auto perm_shape = GetPermuteShape(trans_axis, ins[k]->dims()); int osize = 1; auto idims = ins[k]->dims(); for (int i = 0; i < rank; i++) { stride_x[i] = 1; for (int j = trans_axis[i] + 1; j < rank; j++) { stride_x[i] *= idims[j]; } dims_y[i] = perm_shape[i]; osize *= perm_shape[i]; } stride_y[rank - 1] = 1; for (int i = rank - 2; i >= 0; i--) { if (((i + 1) == flatten_axis) && (concat_axis == 1)) { stride_y[i] = odims[1]; } else { stride_y[i] = stride_y[i + 1] * perm_shape[i + 1]; } } // Since concat is aftern flatten, the output is 2D tensor. // If concat_axis is 0, each input's permutated tensor is continuous. // If concat_axis is 1, the stride of 0-th dim of each input's // permutated tensor is odims()[1]. for (int i = rank; i < max_dim; i++) { stride_x[i] = 1; stride_y[i] = 1; dims_y[i] = 1; } CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor( in_desc, cudnn_dtype, max_dim, dims_y.data(), stride_x.data())); CUDNN_ENFORCE(platform::dynload::cudnnSetTensorNdDescriptor( out_desc, cudnn_dtype, max_dim, dims_y.data(), stride_y.data())); CUDNN_ENFORCE(platform::dynload::cudnnTransformTensor( handle, CudnnDataType::kOne(), in_desc, static_cast(ins[k]->data()), CudnnDataType::kZero(), out_desc, static_cast(odata))); if (concat_axis == 0) { odata += osize; } else { auto flat_shape = GetFlattenShape(flatten_axis, perm_shape); odata += flat_shape[1]; } } CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(in_desc)); CUDNN_ENFORCE(platform::dynload::cudnnDestroyTensorDescriptor(out_desc)); } }; } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP_CUDA_KERNEL(fusion_transpose_flatten_concat, ops::TransposeFlattenConcatFusionKernel, ops::TransposeFlattenConcatFusionKernel);