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[PHI] Migrate squeeze and squeeze_grad kernels (#48634)
* squeeze kernel * squeze fwd * whitespace
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| Original file line number | Diff line number | Diff line change |
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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/squeeze_grad_kernel.h" | ||
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| #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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| template <typename T, typename Context> | ||
| void SqueezeGradKernel(const Context& dev_ctx, | ||
| const DenseTensor& xshape, | ||
| const DenseTensor& dout, | ||
| const IntArray& axes, | ||
| DenseTensor* dx) { | ||
| auto dout_vec_dims = vectorize(dout.dims()); | ||
| auto dout_type = funcs::ToOneDNNDataType(dout.dtype()); | ||
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| funcs::ReorderOneDNNHandler reorder_handler( | ||
| dout_vec_dims, dout.dtype(), dout_type, dev_ctx.GetEngine()); | ||
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| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| dout.mem_desc(), funcs::to_void_cast(dout.data<T>())); | ||
| auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory( | ||
| dx, | ||
| funcs::GetPlainOneDNNFormat(dout_vec_dims.size()), | ||
| dev_ctx.GetPlace()); | ||
| 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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| auto dx_dims = slice_ddim(xshape.dims(), 1, xshape.dims().size()); | ||
| dx->Resize(dx_dims); | ||
| reorder_dst_memory_p->get_desc().reshape(vectorize(dx_dims)); | ||
| } | ||
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| } // namespace phi | ||
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| PD_REGISTER_KERNEL(squeeze_grad, | ||
| OneDNN, | ||
| ONEDNN, | ||
| phi::SqueezeGradKernel, | ||
| float, | ||
| phi::dtype::bfloat16) {} |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,85 @@ | ||
| // 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/squeeze_kernel.h" | ||
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| #include "paddle/phi/backends/onednn/onednn_reuse.h" | ||
| #include "paddle/phi/core/kernel_registry.h" | ||
| #include "paddle/phi/kernels/funcs/unsqueeze.h" | ||
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| namespace phi { | ||
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| template <typename T, typename Context> | ||
| void ExecuteSqueeze(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const DDim& x_dims, | ||
| const DDim& out_dims, | ||
| DenseTensor* out) { | ||
| auto x_vec_dims = vectorize(x_dims); | ||
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| funcs::ReorderOneDNNHandler reorder_handler( | ||
| x_vec_dims, | ||
| x.dtype(), | ||
| funcs::ToOneDNNDataType(x.dtype()), | ||
| dev_ctx.GetEngine()); | ||
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| auto reorder_src_memory_p = reorder_handler.AcquireSrcMemory( | ||
| x.mem_desc(), funcs::to_void_cast(x.data<T>())); | ||
| out->Resize(x_dims); // to match x numel, format is changed later | ||
| // reorder is done into a plain tag to allow usage with blocked formats | ||
| auto reorder_dst_memory_p = reorder_handler.AcquireDstMemory( | ||
| out, funcs::GetPlainOneDNNFormat(x_dims.size()), dev_ctx.GetPlace()); | ||
| auto reorder_p = reorder_handler.AcquireReorder(reorder_dst_memory_p, | ||
| reorder_src_memory_p); | ||
| auto& astream = OneDNNContext::tls().get_stream(); | ||
| reorder_p->execute(astream, *reorder_src_memory_p, *reorder_dst_memory_p); | ||
| astream.wait(); | ||
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| out->Resize(out_dims); | ||
| out->set_mem_desc( | ||
| reorder_dst_memory_p->get_desc().reshape(vectorize(out_dims))); | ||
| } | ||
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| template <typename T, typename Context> | ||
| void SqueezeKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& axes, | ||
| DenseTensor* out) { | ||
| auto x_dims = x.dims(); | ||
| std::vector<int32_t> tmp(axes.GetData().begin(), axes.GetData().end()); | ||
| auto out_dims = funcs::GetOutputSqueezeShape(tmp, x_dims, true); | ||
| ExecuteSqueeze<T, Context>(dev_ctx, x, x_dims, out_dims, out); | ||
| } | ||
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| template <typename T, typename Context> | ||
| void SqueezeWithXShapeKernel(const Context& dev_ctx, | ||
| const DenseTensor& x, | ||
| const IntArray& axes, | ||
| DenseTensor* out, | ||
| DenseTensor* xshape) { | ||
| auto x_dims = slice_ddim(xshape->dims(), 1, xshape->dims().size()); | ||
| auto out_dims = out->dims(); | ||
| ExecuteSqueeze<T, Context>(dev_ctx, x, x_dims, out_dims, out); | ||
| } | ||
| } // namespace phi | ||
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| PD_REGISTER_KERNEL( | ||
| squeeze, OneDNN, ONEDNN, phi::SqueezeKernel, float, phi::dtype::bfloat16) {} | ||
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| PD_REGISTER_KERNEL(squeeze_with_xshape, | ||
| OneDNN, | ||
| ONEDNN, | ||
| phi::SqueezeWithXShapeKernel, | ||
| float, | ||
| phi::dtype::bfloat16) {} |