wehub-resource-sync
117 lines
4.0 KiB
C++
117 lines
4.0 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/expand_kernel.h"
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#include "paddle/phi/backends/onednn/onednn_reuse.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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std::vector<int64_t> GetExtendedXDims(const std::vector<int64_t>& x_vec_dims,
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int new_size) {
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std::vector<int64_t> extended_x_dims(new_size, 1);
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std::copy(x_vec_dims.begin(),
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x_vec_dims.end(),
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extended_x_dims.begin() + new_size - x_vec_dims.size()); // NOLINT
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return extended_x_dims;
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}
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template <typename T, typename Context>
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void ExpandKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& shape,
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DenseTensor* out) {
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const auto& onednn_engine = dev_ctx.GetEngine();
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auto x_vec_dims = vectorize(x.dims());
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auto out_new_dims = shape.GetData();
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bool has_zero_size = false;
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for (size_t i = 0; i < out_new_dims.size(); ++i) {
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out_new_dims[i] = out_new_dims[i] >= 0 ? out_new_dims[i] : x_vec_dims[i];
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}
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if (x_vec_dims.size() != out_new_dims.size()) {
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x_vec_dims = GetExtendedXDims(x_vec_dims, out_new_dims.size()); // NOLINT
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}
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for (size_t i = 0; i < x_vec_dims.size(); ++i) {
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PADDLE_ENFORCE_GE(
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out_new_dims[i],
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0,
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common::errors::InvalidArgument(
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"The expanded size (%d) for non-existing dimensions must be "
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"positive for expand_v2 op.",
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out_new_dims[i]));
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PADDLE_ENFORCE_GE(
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x_vec_dims[i],
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0,
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common::errors::InvalidArgument(
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"The expanded size (%d) for non-existing dimensions must be "
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"positive for expand_v2 op.",
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x_vec_dims[i]));
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PADDLE_ENFORCE_EQ(
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x_vec_dims[i] == 1 || x_vec_dims[i] == out_new_dims[i],
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true,
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common::errors::InvalidArgument(
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"The value (%d) of the non-singleton dimension does not match"
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" the corresponding value (%d) in shape for expand_v2 op.",
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x_vec_dims[i],
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out_new_dims[i]));
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if (out_new_dims[i] == 0) {
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has_zero_size = true;
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}
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}
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out->Resize(out_new_dims);
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if (has_zero_size) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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funcs::BroadcastDataOneDNNHandler<T> handler(dnnl::algorithm::binary_add,
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onednn_engine,
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dev_ctx.GetPlace(),
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&x,
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out,
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0.0f,
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1.0f,
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x_vec_dims);
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auto src_memory_p = handler.AcquireSrcMemory(&x);
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auto dst_memory_p = handler.AcquireZeroedDstMemory(out);
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auto binary_p = handler.AcquireForwardPrimitive();
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const std::unordered_map<int, dnnl::memory> args = {
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{DNNL_ARG_SRC_0, *dst_memory_p},
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{DNNL_ARG_SRC_1, *src_memory_p},
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{DNNL_ARG_DST, *dst_memory_p},
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{DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC_0, handler.Get_Scale_Memory(0.0f)},
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{DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC_1, handler.Get_Scale_Memory(1.0f)}};
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auto& astream = OneDNNContext::tls().get_stream();
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binary_p->execute(astream, args);
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astream.wait();
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out->set_mem_desc(dst_memory_p->get_desc());
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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expand, OneDNN, ONEDNN, phi::ExpandKernel, float, phi::bfloat16) {}
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