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[PHI] transpose2_grad op migration (#46139)
* op migrated, Copy(OneDNNContext, ...) added * mutable_data & op registration in fluid removed * refactoring * OneDNNGetDataType to uppercase * missing cpu check added, handler moved to .h file * name changed to transpose_grad * Copy changed back to TensorCopy * Resizing corrected, Copy(OneDNNContext) removed
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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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#include "paddle/phi/kernels/transpose_grad_kernel.h" | ||
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#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 { | ||
template <typename T, typename Context> | ||
void TransposeGradKernel(const Context& dev_ctx, | ||
const DenseTensor& out_grad, | ||
const std::vector<int>& axis, | ||
DenseTensor* x_grad) { | ||
PADDLE_ENFORCE_EQ(dev_ctx.GetPlace().GetType() == phi::AllocationType::CPU, | ||
true, | ||
errors::PreconditionNotMet( | ||
"Operator DNNL TransposeGrad must use CPUPlace")); | ||
if (!x_grad) return; | ||
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const auto& onednn_engine = dev_ctx.GetEngine(); | ||
std::vector<int> reversed_axis(axis); | ||
if (axis.size() == 1) { | ||
paddle::framework::TensorCopy(out_grad, out_grad.place(), x_grad); | ||
x_grad->set_format(out_grad.format()); | ||
return; | ||
} | ||
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for (size_t i = 0; i < axis.size(); i++) { | ||
reversed_axis[axis[i]] = i; | ||
} | ||
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const T* out_grad_data = out_grad.data<T>(); | ||
dev_ctx.template Alloc<T>(x_grad); | ||
auto nchw_tz = vectorize<int64_t>(out_grad.dims()); | ||
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funcs::TransposeOneDNNHandler<T> handler( | ||
dev_ctx, nchw_tz, reversed_axis, onednn_engine); | ||
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auto transpose_src_memory_p = handler.AcquireSrcMemory( | ||
out_grad.format(), funcs::to_void_cast<T>(out_grad_data)); | ||
auto transpose_dst_memory_p = | ||
handler.AcquireDstMemory(x_grad, dev_ctx.GetPlace()); | ||
auto transpose_p = | ||
handler.AcquireTranspose(transpose_dst_memory_p, transpose_src_memory_p); | ||
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auto& astream = OneDNNContext::tls().get_stream(); | ||
transpose_p->execute( | ||
astream, *transpose_src_memory_p, *transpose_dst_memory_p); | ||
astream.wait(); | ||
} | ||
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} // namespace phi | ||
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PD_REGISTER_KERNEL( | ||
transpose_grad, OneDNN, ALL_LAYOUT, phi::TransposeGradKernel, float) {} |
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