wehub-resource-sync
90 lines
3.8 KiB
C++
90 lines
3.8 KiB
C++
// Copyright (c) 2025 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/backends/xpu/xpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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namespace xpu = baidu::xpu::api;
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namespace phi {
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template <typename T, typename Context>
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void MoECombineBackwardKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& combine_weights,
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const DenseTensor& scatter_index,
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const DenseTensor& grad_y,
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DenseTensor* grad_x,
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DenseTensor* grad_combine_weights_helper) {
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int64_t seq_len = combine_weights.dims()[0];
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int64_t k = combine_weights.dims()[1];
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int64_t hidden_size = x.dims()[1];
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto dy_data = reinterpret_cast<const XPUType*>(grad_y.data<T>());
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auto x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto weight_data =
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reinterpret_cast<const XPUType*>(combine_weights.data<T>());
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auto index_data = scatter_index.data<int>();
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auto dx_data = reinterpret_cast<XPUType*>(grad_x->data<T>());
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auto dw_data =
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reinterpret_cast<XPUType*>(grad_combine_weights_helper->data<T>());
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int ret =
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xpu::constant<XPUType>(dev_ctx.x_context(), dx_data, x.numel(), 0.0f);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "constant");
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ret = xpu::moe_combine_grad<XPUType, int>(dev_ctx.x_context(),
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dy_data,
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x_data,
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weight_data,
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index_data,
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dx_data,
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dw_data,
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seq_len,
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k,
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hidden_size);
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PADDLE_ENFORCE_XDNN_SUCCESS(ret, "moe_combine_grad");
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}
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template <typename T, typename Context>
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void MoeCombineGradKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& combine_weights,
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const DenseTensor& scatter_index,
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const DenseTensor& grad_y,
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DenseTensor* grad_x,
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DenseTensor* grad_combine_weights_helper) {
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PD_CHECK(x.dims().size() == 2, "The shape of X must be 2.");
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PD_CHECK(scatter_index.dtype() == DataType::INT32,
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"MoE combine only supports int32 for scatter_index");
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dev_ctx.template Alloc<T>(grad_x);
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dev_ctx.template Alloc<T>(grad_combine_weights_helper);
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MoECombineBackwardKernel<T, Context>(dev_ctx,
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x,
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combine_weights,
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scatter_index,
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grad_y,
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grad_x,
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grad_combine_weights_helper);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(moe_combine_grad,
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XPU,
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ALL_LAYOUT,
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phi::MoeCombineGradKernel,
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float,
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phi::bfloat16,
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phi::float16) {}
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