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[PHI] traspose2 kernel migration (#47748)
* traspose2 kernel migrated * Got rid of mutable_data * x modification added * ops added in extra info file * Formatting fix * 2 fuse passes with tanpose2 commented * nr of outs changed in 2 passes, passes uncommented * Changes in passes reverted * transpose chnaged in operator.cc * MKLDNN check in operator.cc * Transpose fixes * Fix deleted from operato * template corrected Co-authored-by: Paulina Gacek <[email protected]>
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| // Copyright (c) 2022 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. | ||
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|
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| #include "paddle/phi/kernels/transpose_kernel.h" | ||
| #include "paddle/fluid/framework/tensor_util.h" | ||
| #include "paddle/phi/backends/onednn/onednn_reuse.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
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| namespace phi { | ||
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| void SetInMemDescWithSqueeze2FuseSupport( | ||
| const std::vector<int> fused_squeeze2_axes, | ||
| DenseTensor* in, | ||
| const dnnl::memory::desc& in_md) { | ||
| const std::set<int64_t> squeeze2_axes_set(fused_squeeze2_axes.begin(), | ||
| fused_squeeze2_axes.end()); | ||
| const std::vector<int64_t>& x_vec_dims = in_md.dims(); | ||
| std::vector<int64_t> squeezed_op_tz( | ||
| x_vec_dims.size() - fused_squeeze2_axes.size(), 0); | ||
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| int j = 0; | ||
| for (size_t i = 0; i < x_vec_dims.size(); ++i) { | ||
| if (squeeze2_axes_set.count(i) || | ||
| squeeze2_axes_set.count(i - x_vec_dims.size())) { | ||
| PADDLE_ENFORCE_EQ( | ||
| x_vec_dims[i], | ||
| 1, | ||
| errors::InvalidArgument( | ||
| "Squeeze2 input dim %d should be equal to one, but get %d.", | ||
| i, | ||
| x_vec_dims[i])); | ||
| continue; | ||
| } | ||
| squeezed_op_tz[j++] = x_vec_dims[i]; | ||
| } | ||
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| in->set_mem_desc(in_md.reshape(squeezed_op_tz)); | ||
| in->Resize(make_ddim(squeezed_op_tz)); | ||
| } | ||
|
|
||
| void SetInMemDescWithLogicalLayoutFusesSupport( | ||
| const OneDNNContext& dev_ctx, | ||
| DenseTensor* in, | ||
| const dnnl::memory::desc& in_md) { | ||
| const auto fused_squeeze2_axes = | ||
| dev_ctx.HasDnnAttr("fused_squeeze2_axes") | ||
| ? PADDLE_GET_CONST(std::vector<int>, | ||
| dev_ctx.GetDnnAttr("fused_squeeze2_axes")) | ||
| : std::vector<int>(); | ||
| if (fused_squeeze2_axes.empty()) { | ||
| in->set_mem_desc(in_md); | ||
| in->Resize(make_ddim(in_md.dims())); | ||
| } else { | ||
| SetInMemDescWithSqueeze2FuseSupport(fused_squeeze2_axes, in, in_md); | ||
| } | ||
| } | ||
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| template <typename T, typename Context> | ||
| void TransposeKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const std::vector<int>& axis, | ||
| DenseTensor* out) { | ||
| PADDLE_ENFORCE_EQ( | ||
| dev_ctx.GetPlace().GetType() == AllocationType::CPU, | ||
| true, | ||
| errors::PreconditionNotMet("oneDNN Transpose kernel must use CPUPlace")); | ||
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| SetInMemDescWithLogicalLayoutFusesSupport( | ||
| dev_ctx, const_cast<DenseTensor*>(&x), x.mem_desc()); | ||
|
|
||
| if (axis.size() == 1) { | ||
| paddle::framework::TensorCopy(x, x.place(), out); | ||
| out->set_mem_desc(x.mem_desc()); | ||
| return; | ||
| } | ||
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| auto x_vec_dims = vectorize(x.dims()); | ||
| auto x_type = funcs::ToOneDNNDataType(x.dtype()); | ||
| funcs::ReorderOneDNNHandler reorder_handler( | ||
| x_vec_dims, x.dtype(), x_type, dev_ctx.GetEngine()); | ||
| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| x.mem_desc(), funcs::to_void_cast(x.data<T>())); | ||
| auto dst_md = | ||
| dnnl::memory::desc(x_vec_dims, | ||
| x.mem_desc().data_type(), | ||
| funcs::GetPlainOneDNNFormat(x_vec_dims.size())); | ||
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| // a trick is used here to fake transpose of out_md, so later it will be | ||
| // "untransposed", leaving output data in plain format tag | ||
| std::vector<int64_t> fake_strides(axis.size()); | ||
| auto dims = dst_md.dims(); | ||
| int total_stride = 1; | ||
| for (int i = static_cast<int>(dims.size()) - 1; i >= 0; --i) { | ||
| fake_strides[axis[i]] = total_stride; | ||
| total_stride *= dims[axis[i]]; | ||
| } | ||
| dst_md = | ||
| dnnl::memory::desc(x_vec_dims, x.mem_desc().data_type(), fake_strides); | ||
| auto dst_data = dev_ctx.template Alloc<T>(out); | ||
| auto reorder_dst_memory_p = | ||
| std::make_shared<dnnl::memory>(dst_md, dev_ctx.GetEngine(), dst_data); | ||
| auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p, | ||
| reorder_src_memory_p); | ||
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| auto& astream = OneDNNContext::tls().get_stream(); | ||
| reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p); | ||
| astream.wait(); | ||
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| // it is needed because oneDNN's permute axis understand axes order in | ||
| // different way PaddlePaddle's transpose | ||
| std::vector<int> permute_axis(axis.size()); | ||
| for (size_t i = 0; i < axis.size(); ++i) { | ||
| permute_axis[axis[i]] = i; | ||
| } | ||
| funcs::SetOutMemDescWithLogicalLayoutFusesSupport( | ||
| dev_ctx, | ||
| out, | ||
| reorder_dst_memory_p->get_desc().permute_axes(permute_axis)); | ||
| } | ||
| } // namespace phi | ||
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| PD_REGISTER_KERNEL(transpose, | ||
| OneDNN, | ||
| ONEDNN, | ||
| phi::TransposeKernel, | ||
| float, | ||
| uint8_t, | ||
| int8_t, | ||
| phi::dtype::bfloat16) {} |