wehub-resource-sync
81 lines
3.0 KiB
C++
81 lines
3.0 KiB
C++
// Copyright (c) 2023 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 "glog/logging.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace phi {
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namespace fusion {
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template <typename T, typename Context>
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void FastWhereXPUKernel(const Context& dev_ctx,
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const DenseTensor& condition,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto* condition_data = condition.data<bool>();
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auto* x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto* y_data = reinterpret_cast<const XPUType*>(y.data<T>());
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auto* out_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
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auto condition_dims = vectorize<int64_t>(condition.dims());
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auto x_dims = vectorize<int64_t>(x.dims());
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auto y_dims = vectorize<int64_t>(y.dims());
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PADDLE_ENFORCE_EQ(
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x_dims,
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y_dims,
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errors::PreconditionNotMet(
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"The dimensions of inputs should be equal, but x_dims=[%s] and "
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"y_dims=[%s]",
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x.dims(),
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y.dims()));
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#ifndef PADDLE_WITH_XPU_PLUGIN
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LOG(INFO)
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<< "Add -DWITH_XPU_PLUGIN=ON to build xpu::plugin::fast_where(), or use "
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"xpu::where() instead, which leads low performance.";
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int r = xpu::where<XPUType>(dev_ctx.x_context(),
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condition_data,
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x_data,
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y_data,
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out_data,
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condition_dims,
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x_dims);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "where");
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#else
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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if (condition_dims != x_dims) {
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bool* temp_data = RAII_GUARD.alloc_l3_or_gm<bool>(x.numel());
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int r = xpu::broadcast<bool>(
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dev_ctx.x_context(), condition_data, temp_data, condition_dims, x_dims);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast");
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condition_data = temp_data;
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}
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int r = xpu::plugin::fast_where<XPUType>(
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dev_ctx.x_context(), condition_data, x_data, y_data, out_data, x.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "fast_where");
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#endif
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}
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} // namespace fusion
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} // namespace phi
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PD_REGISTER_KERNEL(fast_where_xpu,
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XPU,
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ALL_LAYOUT,
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phi::fusion::FastWhereXPUKernel,
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float,
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phi::float16,
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int) {}
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