mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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139 lines
5.0 KiB
C++
139 lines
5.0 KiB
C++
/*
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* Copyright (c) 2019-2023, 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/config.h"
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#include "tensorrt_llm/common/cudaUtils.h"
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#include "tensorrt_llm/kernels/kvCacheUtils.h"
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#include "tensorrt_llm/kernels/unfusedAttentionKernels.h"
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#include <assert.h>
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#include <cstdint>
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#include <cuda_fp16.h>
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#include <cuda_runtime.h>
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TRTLLM_NAMESPACE_BEGIN
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namespace kernels
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{
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enum class KvCacheDataType;
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struct MlaMetaParams
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{
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int32_t q_lora_rank = 0;
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int32_t kv_lora_rank = 0;
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int32_t qk_nope_head_dim = 0;
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int32_t qk_rope_head_dim = 0;
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int32_t v_head_dim = 0;
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int32_t predicted_tokens_per_seq = 1;
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int32_t num_layers = 0;
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auto data() const
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{
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return std::make_tuple(q_lora_rank, kv_lora_rank, qk_nope_head_dim, qk_rope_head_dim, v_head_dim,
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predicted_tokens_per_seq, num_layers);
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}
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};
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template <typename T>
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struct MlaParams
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{
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T const* latent_cache; // cKV + k_pe
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// Tensor Q for both context and generation MLA, contiguous. Pre-process kernel will apply RoPE and modify it
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// in-place. For context MLA, shape: [total_q_len, h * (d_nope + d_rope)], stride: [h * (d_nope + d_rope), 1]
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T* q_buf;
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// Separate tensor K for context MLA, contiguous. Pre-process kernel will apply RoPE and modify it in-place.
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// shape: [total_kv_len, h * (d_nope + d_rope)], stride: [h * (d_nope + d_rope), 1]
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T* k_buf = nullptr;
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// Separate tensor V for context MLA, NOT contiguous,
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// shape: [total_kv_len, h * d_v], stride: [h * (d_nope + d_v), 1]
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T const* v_buf = nullptr;
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// Tensor quantized Q for both context and generation MLA.
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// For context MLA, shape: [total_q_len, h * (d_nope + d_rope)], stride: [h * (d_nope + d_rope), 1]
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void* quant_q_buf = nullptr;
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// Tensor quantized K for context MLA, contiguous
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// shape: [total_kv_len, h * (d_nope + d_rope)], stride: [h * (d_nope + d_rope), 1]
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void* quant_k_buf = nullptr;
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// Tensor quantized V for context MLA, contiguous
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// shape: [total_kv_len, h * d_v], stride: [h * d_v, 1]
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void* quant_v_buf = nullptr;
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T* context_buf;
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T* q_pe; // [b, h, d_r], strided
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float2 const* cos_sin_cache; // [s, rope]
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int32_t batch_size;
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int32_t acc_q_len;
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int32_t head_num; // h
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void* workspace;
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int32_t const* cache_seq_lens;
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int* seqQOffset;
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uint32_t* fmha_tile_counter;
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int32_t max_input_seq_len;
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int* cu_q_seqlens;
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int* cu_kv_seqlens;
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int32_t q_pe_ld;
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int32_t q_pe_stride;
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MlaMetaParams meta;
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int const* block_ids_per_seq;
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KvCacheDataType cache_type;
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// Scales for mla quantization
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float* bmm1_scale;
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float* bmm2_scale;
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float const* quant_scale_o;
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float const* quant_scale_q;
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float const* quant_scale_kv;
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float const* dequant_scale_q;
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float const* dequant_scale_kv;
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float host_bmm1_scale;
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// Is it absorption mode?
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bool absorption_mode = false;
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// For FP8 context qkv quantization
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float const* quant_scale_qkv = nullptr;
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// for Helix parallelism: the rotary position offsets [b]
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int32_t const* helix_position_offsets{nullptr};
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// for Helix parallelism: whether the current rank is inactive, shape [b]
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// (the current query tokens are not appended to this rank's KV cache)
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bool const* helix_is_inactive_rank{nullptr};
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};
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template <typename T, typename KVCacheBuffer>
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void invokeMLARopeContext(MlaParams<T>& params, KVCacheBuffer kv_cache_buffer, cudaStream_t stream);
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template <typename T>
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void invokeMLAContextFp8Quantize(MlaParams<T>& params, int total_kv_len, cudaStream_t stream);
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template <typename T, typename KVCacheBuffer>
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void invokeMLARopeGeneration(MlaParams<T>& params, KVCacheBuffer kv_cache_buffer, cudaStream_t stream);
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template <typename T, typename TCache>
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void invokeMLALoadPagedKV(T* compressed_kv_ptr, T* k_pe_ptr, KVBlockArray& kv_cache, int const num_contexts,
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int64_t const* cu_ctx_cached_kv_lens, int const max_input_seq_len, int const lora_size, int const rope_size,
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float const* kv_scale_quant_orig_ptr, cudaStream_t stream);
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template <typename T, typename TCache>
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void invokeMLARopeAppendPagedKVAssignQ(KVBlockArray& kv_cache, T* q_ptr, T* latent_cache_ptr, int const num_requests,
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int64_t const* cu_ctx_cached_kv_lens, int64_t const* cu_seq_lens, int const max_input_uncached_seq_len,
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float2 const* cos_sin_cache, size_t head_num, int nope_size, int rope_size, int lora_size,
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float const* kv_scale_orig_quant_ptr, cudaStream_t stream);
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} // namespace kernels
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TRTLLM_NAMESPACE_END
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