wehub-resource-sync
88 lines
2.8 KiB
Plaintext
88 lines
2.8 KiB
Plaintext
// 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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/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. */
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/*This code is copied from NVIDIA apex:
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* https://github.com/NVIDIA/apex
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* with minor changes. */
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#include "ln.h" // NOLINT
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#include "paddle/phi/core/dense_tensor.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 LnFwdKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& scale,
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const DenseTensor& bias,
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float epsilon,
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DenseTensor* y,
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DenseTensor* mean,
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DenseTensor* invvar) {
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auto input_type = x.type();
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auto weight_type = scale.type();
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auto output_type = weight_type;
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auto compute_type = DataType::FLOAT32;
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PD_CHECK(bias.type() == weight_type);
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auto sizes = x.dims();
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PD_CHECK(sizes.size() >= 2);
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const int cols = sizes[sizes.size() - 1];
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const int rows = x.numel() / cols;
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auto hidden_size = scale.numel();
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PD_CHECK(scale.dims() == bias.dims());
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PD_CHECK(hidden_size == cols);
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PD_CHECK(epsilon >= 0.f);
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auto place = x.place();
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dev_ctx.template Alloc<T>(y);
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dev_ctx.template Alloc<float>(mean);
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dev_ctx.template Alloc<float>(invvar);
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LaunchNormFwd<T, Context>(dev_ctx,
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dev_ctx.stream(),
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place,
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/* x_ptr */ x.data(),
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/* scale_ptr */ scale.data(),
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/* bias_ptr */ bias.data(),
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/* y_ptr */ y->data(),
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/* mean_ptr */ mean->data(),
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/* invvar_ptr */ invvar->data(),
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weight_type,
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input_type,
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output_type,
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compute_type,
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hidden_size,
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rows,
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cols,
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epsilon);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(fast_ln,
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GPU,
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ALL_LAYOUT,
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phi::LnFwdKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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