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https://github.com/NVIDIA/TensorRT-LLM.git
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bca9a33b02
* Update TensorRT-LLM --------- Co-authored-by: Timur Abishev <abishev.timur@gmail.com> Co-authored-by: MahmoudAshraf97 <hassouna97.ma@gmail.com> Co-authored-by: Saeyoon Oh <saeyoon.oh@furiosa.ai> Co-authored-by: hattizai <hattizai@gmail.com>
98 lines
3.8 KiB
C++
98 lines
3.8 KiB
C++
/*
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* Copyright (c) 2020-2023, NVIDIA CORPORATION. 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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*/
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#pragma once
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/kernels/gptKernels.h"
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#include "tensorrt_llm/kernels/multiHeadAttentionCommon.h"
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namespace tensorrt_llm
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{
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namespace kernels
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{
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using XQADataType = Data_type;
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struct XQAParams
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{
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XQADataType data_type = DATA_TYPE_FP16;
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XQADataType kv_cache_data_type = DATA_TYPE_FP16;
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void* output = nullptr;
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void const* qkv = nullptr;
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int32_t const* cache_indir = nullptr;
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float const* kv_scale_orig_quant = nullptr;
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float const* kv_scale_quant_orig = nullptr;
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int32_t const* host_past_key_value_lengths = nullptr;
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int32_t const* host_context_lengths = nullptr;
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int32_t* semaphores = nullptr;
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void* workspaces = nullptr;
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uint32_t batch_size = 0;
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int32_t beam_width = 0;
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int32_t max_attention_window_size = 0;
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int32_t cyclic_attention_window_size = 0;
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int32_t sink_token_length = 0;
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int timestep = 0;
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void const* qkv_bias;
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int32_t const* sequence_lengths; //
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int32_t const* context_lengths; // maybe not used now
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void const* alibi_slopes; // maybe not used now
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float const* rotary_embedding_inv_freq_cache; // precomputed rotary inv freq
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int32_t const* spec_decoding_packed_mask;
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int const* spec_decoding_position_offsets; // for position embedding.
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int const* spec_decoding_generation_lengths; // variable input lengths.
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bool spec_decoding_is_generation_length_variable; // whether the generation lengths actually vary
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int32_t spec_decoding_max_generation_length; // max possible input length
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// almost copy from GPTAttentionPluginCommon.
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// maybe use one struct for parameters in GPTAttentionPluginCommon and share the same here.
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int32_t generation_input_length;
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int32_t layer_idx = 0;
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int32_t num_q_heads = 0;
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int32_t num_kv_heads = 0;
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int32_t head_size = 0;
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int unidirectional;
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float q_scaling = 0;
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int32_t rotary_embedding_dim = 0;
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float rotary_embedding_base = 0.0f;
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tensorrt_llm::kernels::RotaryScalingType rotary_embedding_scale_type;
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float rotary_embedding_scale;
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int rotary_embedding_max_positions;
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int rotary_vision_start;
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int rotary_vision_length;
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tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type;
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bool position_shift_enabled = false;
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bool remove_padding = false;
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tensorrt_llm::kernels::AttentionMaskType mask_type;
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// Paged KV cache parameters.
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bool paged_kv_cache;
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int tokens_per_block;
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int max_blocks_per_sequence;
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tensorrt_llm::common::QuantMode kv_cache_quant_mode;
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int tp_size = 1;
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int tp_rank = 0;
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bool qkv_bias_enabled;
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bool cross_attention;
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int max_distance = 0;
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bool multi_block_mode;
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bool multi_query_tokens = false;
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int32_t total_num_input_tokens; // total number of input tokens. may differ from batch_size due to medusa.
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float const* fp8_out_scale = nullptr; // fp8 output scale in case we need post-processing to convert output to fp8.
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// nullptr means no conversion.
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};
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} // namespace kernels
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} // namespace tensorrt_llm
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