LlamaDecoder.cc

/*
 * Copyright (c) OpenMMLab. All rights reserved.
 * Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
 * Copyright (c) 2022, SK Telecom Authored by A. Dialog
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

 // Modified from https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/models/multi_gpu_gpt/ParallelGptDecoder.cc

#include "src/fastertransformer/models/llama/LlamaDecoder.h"
#include "src/fastertransformer/models/llama/llama_decoder_kernels.h"
#include "src/fastertransformer/models/llama/llama_kernels.h"
#include "src/fastertransformer/models/llama/llama_utils.h"

namespace fastertransformer {

template<typename T>
LlamaDecoder<T>::LlamaDecoder(size_t           head_num,
                              size_t           size_per_head,
                              size_t           inter_size,
                              size_t           num_layer,
                              size_t           rotary_embedding_dim,
                              float            rmsnorm_eps,
                              NcclParam        tensor_para,
                              cudaStream_t     stream,
                              cublasMMWrapper* cublas_wrapper,
                              IAllocator*      allocator,
                              bool             is_free_buffer_after_forward):
    BaseLayer(stream, cublas_wrapper, allocator, is_free_buffer_after_forward),
    head_num_(head_num),
    size_per_head_(size_per_head),
    inter_size_(inter_size),
    num_layer_(num_layer),
    rotary_embedding_dim_(rotary_embedding_dim),
    hidden_units_(head_num * size_per_head),
    rmsnorm_eps_(rmsnorm_eps),
    tensor_para_(tensor_para),
    data_type_(getTensorType<T>())
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    initialize();
}

template<typename T>
LlamaDecoder<T>::~LlamaDecoder()
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    delete self_attention_layer_;
    delete silu_ffn_layer_;
}

template<typename T>
void LlamaDecoder<T>::initialize()
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);

    self_attention_layer_ = new LlamaDecoderSelfAttentionLayer<T>(head_num_,
                                                                  size_per_head_,
                                                                  rotary_embedding_dim_,
                                                                  false,  // neox_rotary_style
                                                                  tensor_para_,
                                                                  stream_,
                                                                  cublas_wrapper_,
                                                                  allocator_,
                                                                  is_free_buffer_after_forward_);

    silu_ffn_layer_ = new LlamaFfnLayer<T>(head_num_,
                                           size_per_head_,
                                           inter_size_,
                                           tensor_para_,
                                           stream_,
                                           cublas_wrapper_,
                                           allocator_,
                                           is_free_buffer_after_forward_);
}

template<typename T>
void LlamaDecoder<T>::allocateBuffer()
{
    FT_CHECK(false);
}

template<typename T>
void LlamaDecoder<T>::allocateBuffer(size_t batch_size)
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    is_allocate_buffer_ = true;
}

template<typename T>
void LlamaDecoder<T>::freeBuffer()
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    if (is_allocate_buffer_) {
        is_allocate_buffer_ = false;
    }
}

template<typename T>
void LlamaDecoder<T>::forwardSelfAttn(const LlamaDecoder::Session&                   sess,
                                      T*                                             attn_io,
                                      const std::unordered_map<std::string, Tensor>* input_tensors,
                                      size_t                                         layer)
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    TensorMap self_attention_input_tensors(*input_tensors);
    self_attention_input_tensors.insert("input_query",
                                        {MEMORY_GPU, data_type_, {sess.batch_size, hidden_units_}, attn_io});
    const int layer_id = layer;
    self_attention_input_tensors.insert("layer_id", {MEMORY_CPU, TYPE_INT32, {1}, &layer_id});
    auto& k_cache = *sess.k_cache;
    auto& v_cache = *sess.v_cache;

    TensorMap self_attention_output_tensors{
        {"attention_output", {MEMORY_GPU, data_type_, {sess.batch_size, hidden_units_}, attn_io}},
        {"key_cache", k_cache},
        {"value_cache", v_cache},
    };

    self_attention_layer_->forward(&self_attention_output_tensors,  //
                                   &self_attention_input_tensors,
                                   &sess.weights->at(layer)->self_attn_weights);
}

template<typename T>
void LlamaDecoder<T>::forwardFfn(const LlamaDecoder::Session& sess, T* ffn_io, size_t layer)
{
    TensorMap ffn_inputs{{"ffn_input", {MEMORY_GPU, data_type_, {sess.batch_size, hidden_units_}, ffn_io}}};
    TensorMap ffn_outputs{{"ffn_output", {MEMORY_GPU, data_type_, {sess.batch_size, hidden_units_}, ffn_io}}};
    silu_ffn_layer_->forward(&ffn_outputs, &ffn_inputs, &sess.weights->at(layer)->ffn_weights);
}

template<typename T>
void LlamaDecoder<T>::forward(std::vector<Tensor>*                            output_tensors,
                              const std::vector<Tensor>*                      input_tensors,
                              const std::vector<LlamaDecoderLayerWeight<T>*>* decoder_layer_weights)
{
    FT_CHECK(false);
}

template<typename T>
void LlamaDecoder<T>::forward(std::unordered_map<std::string, Tensor>*        output_tensors,
                              const std::unordered_map<std::string, Tensor>*  input_tensors,
                              const std::vector<LlamaDecoderLayerWeight<T>*>* decoder_layer_weights)
{
    FT_LOG_DEBUG(__PRETTY_FUNCTION__);
    /**
     * input_tensors:
     *   \param decoder_input [batch_size, hidden_dims]
     *   \param sequence_lengths [batch_size] int
     *   \param output_norm_weight [hidden_dims]
     *   \param step [1] on cpu
     *   \param ite [1] on cpu
     *   \param finished [batch_size] bool
     *   \param total_padding_tokens [batch_size], int
     *   \param max_seq_len [1] on cpu
     *   \param masked_tokens [batch_size, memory_len] bool (optional), NOT USED YET
     *
     * output_tensors:
     *   \param decoder_output [batch_size, hidden_dimension]
     *   \param key_cache [batch_size] uint64_t
     *   \param value_cache [batch_size] uint64_t
     */

    // for the shape of key cache, refer to decoder_masked_multihead_attention_template.hpp

    Session sess{};
    sess.batch_size = input_tensors->at("decoder_input").shape[0];
    sess.weights    = decoder_layer_weights;

    allocateBuffer(sess.batch_size);

    sess.ite     = input_tensors->at("ite").getVal<const int>();
    sess.k_cache = &output_tensors->at("key_cache");
    sess.v_cache = &output_tensors->at("value_cache");

    sess.max_memory_len = input_tensors->at("max_seq_len").getVal<int>();

    T* decoder_input  = input_tensors->at("decoder_input").getPtr<T>();
    T* decoder_output = output_tensors->at("decoder_output").getPtr<T>();

    ////////////////////////////////////////////
    /// RMSNorm
    invokeRootMeanSquareNorm(decoder_output,
                             decoder_input,
                             decoder_layer_weights->at(0)->self_attn_norm_weights,
                             rmsnorm_eps_,
                             sess.batch_size,
                             hidden_units_,
                             stream_);
    sync_check_cuda_error();

    for (size_t layer = 0; layer < num_layer_; ++layer) {
        // output: self_attn_output_, k_cache, v_cache = self_attn(decoder_normed_input_)
        forwardSelfAttn(sess, decoder_output, input_tensors, layer);

        invokeFusedAddResidualRMSNorm(decoder_input,
                                      decoder_output,
                                      decoder_layer_weights->at(layer)->ffn_norm_weights,
                                      rmsnorm_eps_,
                                      sess.batch_size,
                                      hidden_units_,
                                      stream_);
        sync_check_cuda_error();

        // decoder_layer_output_ = ffn(decoder_normed_input_)
        forwardFfn(sess, decoder_output, layer);

        auto scale_weight = layer < num_layer_ - 1 ? decoder_layer_weights->at(layer + 1)->self_attn_norm_weights :
                                                     input_tensors->at("output_norm_weight").getPtr<T>();
        invokeFusedAddResidualRMSNorm(decoder_input,  //
                                      decoder_output,
                                      scale_weight,
                                      rmsnorm_eps_,
                                      sess.batch_size,
                                      hidden_units_,
                                      stream_);
        sync_check_cuda_error();
    }

    if (is_free_buffer_after_forward_) {
        freeBuffer();
    }
}

template class LlamaDecoder<half>;
template class LlamaDecoder<float>;

}  // namespace fastertransformer