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| // MIT License | ||
| // Copyright (c) 2022 - 傅莘莘 | ||
| // Source URL: https://github.com/zjhellofss/KuiperInfer | ||
| // Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| // of this software and associated documentation files (the "Software"), to deal | ||
| // in the Software without restriction, including without limitation the rights | ||
| // to use, copy, modify, merge, publish, distribute, sublicense, | ||
| // and/or sell copies of the Software, and to permit persons to whom the | ||
| // Software is furnished to do so, subject to the following conditions: | ||
| // | ||
| // The above copyright notice and this permission notice shall be included in | ||
| // all copies or substantial portions of the Software. | ||
| // | ||
| // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| // SOFTWARE. | ||
|
|
||
| // | ||
| // Created by fss on 24-2-9. | ||
| // | ||
|
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| #include "rms_norm.hpp" | ||
| namespace kuiper_infer { | ||
| RMSNormLayer::RMSNormLayer() : ParamLayer("rms_norm") { this->weights_.resize(1); } | ||
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| void RMSNormLayer::set_weights(const std::vector<float>& weights) { | ||
| LOG(FATAL) << "This function is not implement in the RMSNorm layer！"; | ||
| } | ||
|
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| void RMSNormLayer::set_weights(const std::vector<std::shared_ptr<Tensor<float>>>& weights) { | ||
| CHECK(weights.size() == weights_.size()); | ||
| for (uint32_t i = 0; i < weights.size(); ++i) { | ||
| if (this->weights_.at(i) != nullptr) { | ||
| CHECK(this->weights_.at(i)->rows() == weights.at(i)->rows()); | ||
| CHECK(this->weights_.at(i)->cols() == weights.at(i)->cols()); | ||
| CHECK(this->weights_.at(i)->channels() == weights.at(i)->channels()); | ||
| } | ||
| } | ||
| this->weights_ = weights; | ||
| } | ||
|
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| StatusCode RMSNormLayer::Forward(const std::vector<std::shared_ptr<Tensor<float>>>& inputs, | ||
| std::vector<std::shared_ptr<Tensor<float>>>& outputs) { | ||
| if (inputs.empty()) { | ||
| LOG(ERROR) << "The input tensor array in the rmsnorm layer is empty"; | ||
| return StatusCode::kInferInputsEmpty; | ||
| } | ||
|
|
||
| if (outputs.empty()) { | ||
| LOG(ERROR) << "The output tensor array in the rmsnorm layer is empty"; | ||
| return StatusCode::kInferOutputsEmpty; | ||
| } | ||
|
|
||
| if (inputs.size() != outputs.size()) { | ||
| LOG(ERROR) << "The input and output tensor array size of the rmsnorm " | ||
| "layer do not match"; | ||
| return StatusCode::kInferInOutShapeMismatch; | ||
| } | ||
|
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| if (weights_.empty() || weights_.front()->empty()) { | ||
| LOG(ERROR) << "The weight for the rmsnorm layer is missing."; | ||
| return StatusCode::kInferParameterError; | ||
| } | ||
|
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| std::shared_ptr<Tensor<float>> weight = this->weight(0); | ||
| arma::fvec weight_vec(weight->raw_ptr(), weight->size(), false, true); | ||
| const uint32_t batch_size = inputs.size(); | ||
| #pragma omp parallel for if (batch_size > 1) num_threads(batch_size) | ||
| for (uint32_t i = 0; i < batch_size; ++i) { | ||
| const auto& input = inputs.at(i); | ||
| CHECK(input != nullptr && !input->empty()) | ||
| << "The input tensor array in the rmsnorm layer has an " | ||
| "empty tensor " | ||
| << i << " th"; | ||
|
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| std::shared_ptr<Tensor<float>> output = outputs.at(i); | ||
| if (output == nullptr || output->empty()) { | ||
| output = std::make_shared<Tensor<float>>(input->shapes()); | ||
| outputs.at(i) = output; | ||
| } | ||
| CHECK(output->shapes() == input->shapes()) | ||
| << "The input and output tensor shapes of the rmsnorm " | ||
| "layer do not match " | ||
| << i << " th"; | ||
|
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| const size_t size = input->size(); | ||
| arma::fvec input_vec(input->raw_ptr(), size, false, true); | ||
| float mean_value = arma::mean(input_vec % input_vec); | ||
| float norm_value = 1.f / std::sqrt(mean_value + eps_); | ||
| arma::fvec output_vec(output->raw_ptr(), size, false, true); | ||
| output_vec = weight_vec % (norm_value * input_vec); | ||
| } | ||
| return StatusCode::kSuccess; | ||
| } | ||
| } // namespace kuiper_infer |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,45 @@ | ||
| // MIT License | ||
| // Copyright (c) 2022 - 傅莘莘 | ||
| // Source URL: https://github.com/zjhellofss/KuiperInfer | ||
| // Permission is hereby granted, free of charge, to any person obtaining a copy | ||
| // of this software and associated documentation files (the "Software"), to deal | ||
| // in the Software without restriction, including without limitation the rights | ||
| // to use, copy, modify, merge, publish, distribute, sublicense, | ||
| // and/or sell copies of the Software, and to permit persons to whom the | ||
| // Software is furnished to do so, subject to the following conditions: | ||
| // | ||
| // The above copyright notice and this permission notice shall be included in | ||
| // all copies or substantial portions of the Software. | ||
| // | ||
| // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
| // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
| // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
| // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
| // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
| // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
| // SOFTWARE. | ||
|
|
||
| // | ||
| // Created by fss on 24-2-9. | ||
| // | ||
|
|
||
| #ifndef KUIPER_INFER_SOURCE_LAYER_DETAILS_RMS_NORM_HPP | ||
| #define KUIPER_INFER_SOURCE_LAYER_DETAILS_RMS_NORM_HPP | ||
| #include "layer/abstract/param_layer.hpp" | ||
| #include "runtime/runtime_op.hpp" | ||
| namespace kuiper_infer { | ||
| class RMSNormLayer : public ParamLayer { | ||
| public: | ||
| explicit RMSNormLayer(); | ||
|
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| StatusCode Forward(const std::vector<std::shared_ptr<Tensor<float>>>& inputs, | ||
| std::vector<std::shared_ptr<Tensor<float>>>& outputs) override; | ||
|
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| void set_weights(const std::vector<std::shared_ptr<Tensor<float>>>& weights) override; | ||
|
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| void set_weights(const std::vector<float>& weights) override; | ||
| private: | ||
| float eps_ = 1e-5f; | ||
| }; | ||
| } // namespace kuiper_infer | ||
| #endif // KUIPER_INFER_SOURCE_LAYER_DETAILS_RMS_NORM_HPP |
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