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https://github.com/NVIDIA/TensorRT-LLM.git
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d37b507f41
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
120 lines
4.0 KiB
C++
120 lines
4.0 KiB
C++
/*
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* Copyright (c) 2019-2023, NVIDIA CORPORATION. All rights reserved.
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* Copyright (c) 2021, NAVER Corp. Authored by CLOVA.
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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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*/
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#pragma once
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#include <curand_kernel.h>
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#include "tensorrt_llm/common/tensor.h"
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#include "tensorrt_llm/kernels/penaltyTypes.h"
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#include "tensorrt_llm/layers/baseLayer.h"
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#include "tensorrt_llm/layers/decodingParams.h"
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namespace tc = tensorrt_llm::common;
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namespace tensorrt_llm
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{
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namespace layers
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{
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template <typename T>
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class BaseSamplingLayer : public BaseLayer
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{
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public:
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BaseSamplingLayer(size_t vocab_size, size_t vocab_size_padded, cudaStream_t stream,
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std::shared_ptr<tensorrt_llm::common::IAllocator> allocator, bool is_free_buffer_after_forward,
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cudaDeviceProp* cuda_device_prop);
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BaseSamplingLayer(BaseSamplingLayer const& sampling_layer);
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~BaseSamplingLayer() override;
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class SetupParams : public DecodingSetupParams
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{
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public:
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std::optional<std::vector<std::uint32_t>> runtime_top_k; // [1] or [batch_size] on cpu
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std::optional<std::vector<float>> runtime_top_p; // [1] or [batch_size] on cpu
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std::optional<std::vector<uint64_t>> randomSeed; // [1] or [batch_size] on cpu
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std::optional<std::vector<float>> top_p_decay; // [batch_size], must between [0, 1]
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std::optional<std::vector<float>> top_p_min; // [batch_size], must between [0, 1]
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std::optional<std::vector<std::int32_t>> top_p_reset_ids; // [batch_size]
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std::optional<bool> normalize_log_probs;
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};
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class ForwardParams : public DecodingParams
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{
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public:
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ForwardParams(int step, int ite, tc::Tensor logits, tc::Tensor end_ids, int max_seq_len)
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: DecodingParams{step, ite, std::move(logits), std::move(end_ids)}
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, max_seq_len{max_seq_len}
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{
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}
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// mandatory parameters
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int max_seq_len;
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// optional parameters
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std::optional<tc::Tensor> embedding_bias; // [vocab_size_padded]
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std::optional<tc::Tensor> input_lengths; // [local_batch_size * beam_width]
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};
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void forward(DecodingOutputParams& outputs, ForwardParams const& params);
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virtual void setup(size_t batch_size, SetupParams const& setupParams) = 0;
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protected:
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size_t vocab_size_;
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size_t vocab_size_padded_;
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size_t sampling_workspace_size_;
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void* sampling_workspace_ = nullptr;
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curandState_t* curandstate_buf_ = nullptr;
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uint64_t* random_seeds_buf_ = nullptr;
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float* temperature_buf_ = nullptr;
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float* repetition_penalty_buf_ = nullptr;
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float* presence_penalty_buf_ = nullptr;
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float* frequency_penalty_buf_ = nullptr;
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int32_t* min_lengths_buf_ = nullptr;
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bool* skip_decode_buf_ = nullptr;
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T* runtime_logits_buf_ = nullptr;
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std::vector<float> mTemperature;
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std::vector<float> mRepetitionPenalty;
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std::vector<float> mPresencePenalty;
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std::vector<float> mFrequencyPenalty;
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std::vector<int32_t> mMinLengths;
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bool* skip_decode_ = nullptr;
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bool skip_any_ = false;
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bool use_repetition_penalty_ = false;
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bool use_presence_penalty_ = false;
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bool use_frequency_penalty_ = false;
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virtual void runSampling(DecodingOutputParams& outputs, DecodingParams const& params) = 0;
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virtual void freeBuffer();
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void setupBase(size_t batch_size, SetupParams const& setupParams);
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private:
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void allocateBuffer(size_t batch_size);
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bool isValidBatchSize(size_t batch_size);
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};
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} // namespace layers
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} // namespace tensorrt_llm
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