wehub-resource-sync
94 lines
3.4 KiB
C++
94 lines
3.4 KiB
C++
// Copyright (c) 2024 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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// 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/swiglu_grad_kernel.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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#include "paddle/phi/kernels/full_kernel.h"
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namespace phi {
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template <typename T, typename Context>
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void SwiGluGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& y,
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const DenseTensor& dz,
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DenseTensor* dx,
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DenseTensor* dy) {
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if (dx && dx->numel() == 0) {
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dev_ctx.template Alloc<T>(dx);
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if (dy) {
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Full<T, Context>(dev_ctx, dy->dims(), 0, dy);
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}
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return;
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}
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if (dy && dy->numel() == 0) {
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dev_ctx.template Alloc<T>(dy);
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if (dx) {
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Full<T, Context>(dev_ctx, dx->dims(), 0, dx);
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}
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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const auto* x_data = x.data<T>();
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const auto* dz_data = dz.data<T>();
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auto* dx_data = dev_ctx.template Alloc<T>(dx);
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const auto& dims = x.dims();
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int64_t axis = dims.size() - 1;
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auto dims_vec = vectorize<int64_t>(dims);
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const XPUType* y_ptr = nullptr;
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XPUType* dy_ptr = nullptr;
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if (y) {
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const auto& y_tensor = y.get();
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const auto& y_dims = y_tensor.dims();
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const auto* y_data = y_tensor.data<T>();
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auto* dy_data = dev_ctx.template Alloc<T>(dy);
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y_ptr = reinterpret_cast<const XPUType*>(y_data);
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dy_ptr = reinterpret_cast<XPUType*>(dy_data);
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PADDLE_ENFORCE_EQ(y_dims,
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dims,
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common::errors::InvalidArgument(
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"The shape of Input(Y):[%s] must be equal "
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"to the shape of Input(X):[%s].",
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y_dims,
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dims));
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}
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int ret = xpu::swiglu_grad(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_data),
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y_ptr,
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reinterpret_cast<const XPUType*>(dz_data),
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reinterpret_cast<XPUType*>(dx_data),
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dy_ptr,
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dims_vec,
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axis,
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true);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "swiglu_grad");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(swiglu_grad,
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XPU,
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ALL_LAYOUT,
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phi::SwiGluGradKernel,
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float,
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phi::float16,
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phi::bfloat16){};
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