wehub-resource-sync
117 lines
4.5 KiB
C++
117 lines
4.5 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/layer_norm_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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namespace phi {
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template <typename T, typename TW, typename Context>
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void LayerNormKernelImpl(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const optional<DenseTensor>& bias,
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float epsilon,
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int begin_norm_axis,
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DenseTensor* out,
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DenseTensor* mean,
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DenseTensor* variance) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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using XPUTypeTW = typename XPUTypeTrait<TW>::Type;
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const auto& x_dims = x.dims();
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auto matrix_dim = common::flatten_to_2d(x_dims, begin_norm_axis);
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int64_t left = matrix_dim[0];
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int64_t right = matrix_dim[1];
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const auto* x_data = x.data<T>();
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const auto* scale_data = scale.get_ptr() ? scale->data<TW>() : nullptr;
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const auto* bias_data = bias.get_ptr() ? bias->data<TW>() : nullptr;
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xpu::ctx_guard RAII_GUARD(dev_ctx.x_context());
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auto* out_data = dev_ctx.template Alloc<T>(out);
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auto* mean_data = dev_ctx.template Alloc<float>(mean);
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auto* variance_data = dev_ctx.template Alloc<float>(variance);
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if (x.numel() == 0) return;
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int r = xpu::layer_norm(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x_data),
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reinterpret_cast<XPUType*>(out_data),
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left,
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right,
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epsilon,
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reinterpret_cast<const XPUTypeTW*>(scale_data),
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reinterpret_cast<const XPUTypeTW*>(bias_data),
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mean_data,
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variance_data);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "layer_norm");
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}
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template <typename T, typename Context>
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void LayerNormKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& scale,
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const optional<DenseTensor>& bias,
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double epsilon,
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int begin_norm_axis,
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DenseTensor* out,
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DenseTensor* mean,
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DenseTensor* variance) {
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bool valid_scale = (scale.get_ptr() != nullptr);
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bool valid_bias = (bias.get_ptr() != nullptr);
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auto x_dtype = x.dtype();
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DataType scale_bias_dtype;
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if (valid_scale) {
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scale_bias_dtype = scale->dtype();
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if (valid_bias) {
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PADDLE_ENFORCE_EQ(scale->dtype(),
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bias->dtype(),
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common::errors::InvalidArgument(
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"This Scale and Bias of layer_norm op "
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"should have the same data type."));
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}
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} else {
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scale_bias_dtype = valid_bias ? bias->dtype() : x_dtype;
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}
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bool is_scale_bias_same_dtype_with_x = (x_dtype == scale_bias_dtype);
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if (!is_scale_bias_same_dtype_with_x) {
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PADDLE_ENFORCE_EQ(scale_bias_dtype,
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DataType::FLOAT32,
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common::errors::InvalidArgument(
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"Unsupported data type of Scale and Bias"));
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}
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if (is_scale_bias_same_dtype_with_x) {
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LayerNormKernelImpl<T, T, Context>(
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dev_ctx, x, scale, bias, epsilon, begin_norm_axis, out, mean, variance);
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} else {
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LayerNormKernelImpl<T, float, Context>(
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dev_ctx, x, scale, bias, epsilon, begin_norm_axis, out, mean, variance);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(layer_norm,
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XPU,
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ALL_LAYOUT,
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phi::LayerNormKernel,
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float,
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phi::float16,
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phi::bfloat16) {
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kernel->OutputAt(1).SetDataType(phi::DataType::UNDEFINED);
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kernel->OutputAt(2).SetDataType(phi::DataType::UNDEFINED);
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}
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