86 lines
3.1 KiB
C++
86 lines
3.1 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/nonzero_kernel.h"
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#include "glog/logging.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/common/memory_utils.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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template <typename T, typename Context>
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void NonZeroKernel(const Context& dev_ctx,
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const DenseTensor& condition,
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DenseTensor* out) {
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auto numel = condition.numel();
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auto dims = condition.dims();
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const int64_t rank = dims.size();
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using XPUType = typename XPUTypeTrait<T>::Type;
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if (numel == 0) {
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dev_ctx.template Alloc<int64_t>(out);
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return;
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}
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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int64_t* true_num = RAII_GUARD.alloc_l3_or_gm<int64_t>(1);
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int64_t* workspace =
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RAII_GUARD.alloc_l3_or_gm<int64_t>(dev_ctx.x_context()->ncluster() * 64);
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auto cond_data = reinterpret_cast<const XPUType*>(condition.data<T>());
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int ret = xpu::nonzero_count<XPUType, int64_t>(
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dev_ctx.x_context(), cond_data, true_num, numel, workspace);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "nonzero_count");
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int64_t true_num_cpu;
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memory_utils::Copy(CPUPlace(),
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static_cast<void*>(&true_num_cpu),
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dev_ctx.GetPlace(),
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static_cast<void*>(true_num),
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sizeof(int64_t));
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if (std::getenv("XPUSIM_SKIP_RUN") &&
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std::strcmp(std::getenv("XPUSIM_SKIP_RUN"), "1") == 0) {
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VLOG(3) << "WARNING: In the simulator mode, the variable true_num_cpu "
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"stores an uninitialized value. To avoid allocating a memory of "
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"random size, we assign numel to true_num_cpu";
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true_num_cpu = numel;
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}
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out->Resize({true_num_cpu, rank});
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auto* out_data = dev_ctx.template Alloc<int64_t>(out);
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if (true_num_cpu == 0) {
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return;
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}
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auto condition_shape = vectorize<int64_t>(dims);
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ret = xpu::nonzero_compute<XPUType, int64_t, int64_t>(dev_ctx.x_context(),
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cond_data,
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out_data,
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condition_shape,
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true_num_cpu,
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workspace);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "nonzero_compute");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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nonzero, XPU, ALL_LAYOUT, phi::NonZeroKernel, int, bool, float, int64_t) {
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kernel->OutputAt(0).SetDataType(phi::DataType::INT64);
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}
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