wehub-resource-sync
130 lines
5.1 KiB
C++
130 lines
5.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/funcs/data_layout_transform.h"
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#include "glog/logging.h"
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#include "paddle/common/layout.h"
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#include "paddle/phi/backends/context_pool.h"
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#include "paddle/phi/backends/onednn/onednn_context.h"
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#include "paddle/phi/backends/onednn/onednn_helper.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/common/place.h"
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#include "paddle/phi/core/dense_tensor.h"
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namespace phi::funcs {
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#ifdef PADDLE_WITH_DNNL
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void* GetDataFromTensor(const DenseTensor& tensor,
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dnnl::memory::data_type type) {
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switch (type) {
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case dnnl::memory::data_type::f32:
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return to_void_cast(tensor.data<float>());
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case dnnl::memory::data_type::s8:
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return to_void_cast(tensor.data<int8_t>());
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case dnnl::memory::data_type::u8:
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return to_void_cast(tensor.data<unsigned char>());
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case dnnl::memory::data_type::s32:
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return to_void_cast(tensor.data<int32_t>());
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case dnnl::memory::data_type::bf16:
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return to_void_cast(tensor.data<dtype::bfloat16>());
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default:
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PADDLE_THROW(errors::InvalidArgument("Wrong oneDNN type provided."));
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}
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}
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// This helper function is used to construct a dnnl memory descriptor from a
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// reference dense tensor and a target layout. For 0-D tensor case, we will
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// construct a 1-D memory descriptor with shape [1], since oneDNN didn't support
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// 0-D now.
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dnnl::memory::desc make_memory_desc(const DenseTensor& ref_tensor,
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DataLayout target_layout) {
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auto ref_dims = vectorize<int64_t>(ref_tensor.dims());
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auto ref_type = ToOneDNNDataType(ref_tensor.dtype());
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PADDLE_ENFORCE_NE(ref_type,
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OneDNNDataType::undef,
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errors::InvalidArgument(
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"Ref tensor type (%s) is not supported by oneDNN.",
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ref_tensor.dtype()));
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auto md_dims = !ref_dims.empty() ? ref_dims : std::vector<int64_t>{1};
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auto md_format =
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OneDNNFormatForSize(md_dims.size(), ToOneDNNFormat(target_layout));
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dnnl::memory::desc md(md_dims, ref_type, md_format);
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return md;
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}
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void TransDataLayoutFromOneDNN(DataLayout in_layout,
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DataLayout out_layout,
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const DenseTensor& in,
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DenseTensor* out,
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Place place,
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bool always_copy) {
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// Set default as NCHW in case not specified
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out_layout = out_layout == DataLayout::ANY ? DataLayout::NCHW : out_layout;
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auto& pool = DeviceContextPool::Instance();
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auto* dev_ctx = dynamic_cast<OneDNNContext*>(pool.Get(place));
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auto& cpu_engine = dev_ctx->GetEngine();
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auto in_dims = vectorize<int64_t>(in.dims());
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auto md_dims = !in_dims.empty() ? in_dims : std::vector<int64_t>{1};
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const auto src_mem_desc =
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!in_dims.empty() ? in.mem_desc()
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: dnnl::memory::desc(md_dims,
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ToOneDNNDataType(in.dtype()),
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dnnl::memory::format_tag::x);
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dnnl::memory::desc out_mem_desc = make_memory_desc(in, out_layout);
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// output tensor has the same dims as input. Reorder don't change dims
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out->set_mem_desc(out_mem_desc);
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out->Resize(in.dims());
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// Note(0x45f): Using initialized() to support slice Tensors
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// with shapes like [0, 0, 0].
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if (in.initialized() && ((in.mem_desc() != out->mem_desc()) || always_copy)) {
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auto in_tz = vectorize<int64_t>(in.dims());
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auto in_type = ToOneDNNDataType(in.dtype());
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void* in_data = GetDataFromTensor(in, in_type);
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ReorderOneDNNHandler handler(in_tz, in.dtype(), in_type, cpu_engine);
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auto reorder_src_memory_p = handler.AcquireSrcMemory(src_mem_desc, in_data);
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auto reorder_dst_memory_p =
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handler.AcquireDstMemory(out, out->mem_desc(), place);
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auto reorder_p =
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handler.AcquireReorder(reorder_dst_memory_p, reorder_src_memory_p);
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auto& astream = OneDNNContext::tls().get_stream();
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reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p);
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astream.wait();
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} else {
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out->ShareDataWith(in);
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}
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// For expected NHWC data format we need to reshape the Output tensor
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// As MKL-DNN description was in NCHW and paddle is expecting NHWC
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MatchShapeToLayout(out, in_layout, out_layout);
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out->set_layout(DataLayout::NCHW);
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VLOG(10) << "out->layout: " << out->layout() << " in->dims: " << in.dims()
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<< " out->dims: " << out->dims();
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}
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#endif
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} // namespace phi::funcs
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