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be9cd719f7
* Update TensorRT-LLM --------- Co-authored-by: akhoroshev <arthoroshev@gmail.com> Co-authored-by: Fabian Joswig <fjosw@users.noreply.github.com> Co-authored-by: Tayef Shah <tayefshah@gmail.com> Co-authored-by: lfz941 <linfanzai941@gmail.com>
179 lines
12 KiB
Plaintext
179 lines
12 KiB
Plaintext
/*
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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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#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention.h"
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#include <assert.h>
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#include <float.h>
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#include <type_traits>
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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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namespace mmha
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{
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////////////////////////////////////////////////////////////////////////////////////////////////////
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// Forward declaration of the kernel launcher to avoid including decoderMaskedMultiheadAttentionLaunch.h
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template <typename T, typename KVCacheBuffer, typename T_PARAMS, int Dh, bool BLOCK_SPARSE_ATTN,
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bool IMPLICIT_REL_ATTN_BIAS, bool QK_TANH_SCALE>
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void mmha_launch_kernel(const T_PARAMS& params, KVCacheBuffer const& kv_cache_buffer,
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KVLinearBuffer const& shift_k_cache, cudaStream_t const& stream);
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} // namespace mmha
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namespace
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{
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#define MMHA_LAUNCH_KERNEL(Dh) \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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break;
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#define MMHA_LAUNCH_KERNE_EX1(Dh) \
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if (has_implicit_rel_attn_bias) \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, true, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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else \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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break;
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#define MMHA_LAUNCH_KERNE_EX2(Dh) \
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if (has_implicit_rel_attn_bias) \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, true, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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else if (has_qk_tanh_scale) \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, true>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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else if (has_block_sparse_attn) \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, true, false, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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else \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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break;
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#define MMHA_LAUNCH_KERNE_EX3(Dh) \
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if (has_qk_tanh_scale) \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, true>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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else \
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{ \
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mmha::mmha_launch_kernel<T, KVCacheBuffer, KERNEL_PARAMS_TYPE, Dh, false, false, false>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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break;
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template <typename T, typename KVCacheBuffer, typename KERNEL_PARAMS_TYPE>
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void multihead_attention_(const KERNEL_PARAMS_TYPE& params, KVCacheBuffer const& kv_cache_buffer,
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KVLinearBuffer const& shift_k_cache, cudaStream_t const& stream)
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{
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bool const has_implicit_rel_attn_bias = params.max_distance > 0 && params.relative_attention_bias != nullptr;
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bool const has_qk_tanh_scale = params.qk_tanh_scale > 0.f;
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int const head_size = params.hidden_size_per_head;
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TLLM_CHECK_WITH_INFO(!has_implicit_rel_attn_bias || head_size == 32 || head_size == 64 || head_size == 128,
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"MMHA kernels haven't instantiate implicit_relative_attention_bias paths for head size %d.", head_size);
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TLLM_CHECK_WITH_INFO(!has_qk_tanh_scale || head_size == 128 || head_size == 256,
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"MMHA kernels haven't instantiate qk_tanh_scale paths for head size %d.", head_size);
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TLLM_CHECK_WITH_INFO(!(has_qk_tanh_scale && has_implicit_rel_attn_bias),
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"MMHA kernels haven't instantiate implicit_relative_attention_bias + qk_tanh_scale paths for head size %d.",
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head_size);
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bool const has_block_sparse_attn = params.block_sparse_attention;
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TLLM_CHECK_WITH_INFO(!has_block_sparse_attn || head_size == 128,
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"MMHA kernels were not instantiated for block_sparse_attention for head size %d.", head_size);
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TLLM_CHECK_WITH_INFO(!(has_implicit_rel_attn_bias && has_block_sparse_attn),
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"MMHA kernels do not support combining implicit_relative_attention_bias and block_sparse_attention");
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switch (params.hidden_size_per_head)
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{
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case 32: MMHA_LAUNCH_KERNE_EX1(32);
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case 64: MMHA_LAUNCH_KERNE_EX1(64);
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case 128: MMHA_LAUNCH_KERNE_EX2(128);
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case 256: MMHA_LAUNCH_KERNE_EX3(256);
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#ifndef FAST_BUILD // skip mmha 48, 80, 96, 104, 112, 144, 160, 192 and 224 for fast build
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case 48: MMHA_LAUNCH_KERNEL(48);
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case 80: MMHA_LAUNCH_KERNEL(80);
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case 96: MMHA_LAUNCH_KERNEL(96);
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case 104: MMHA_LAUNCH_KERNEL(104);
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case 112: MMHA_LAUNCH_KERNEL(112);
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case 144: MMHA_LAUNCH_KERNEL(144);
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case 160: MMHA_LAUNCH_KERNEL(160);
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case 192: MMHA_LAUNCH_KERNEL(192);
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case 224: MMHA_LAUNCH_KERNEL(224);
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#endif // FAST_BUILD
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default: TLLM_CHECK_WITH_INFO(false, "unsupported head_size %d", params.hidden_size_per_head);
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}
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}
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#undef MMHA_LAUNCH_KERNEL
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} // namespace
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////////////////////////////////////////////////////////////////////////////////////////////////////
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static constexpr std::array<int, 13> MMHA_SUPPORTED_HEAD_SIZES{
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32, 48, 64, 80, 96, 104, 112, 128, 144, 160, 192, 224, 256};
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bool mmha_supported(int head_size)
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{
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return std::find(MMHA_SUPPORTED_HEAD_SIZES.begin(), MMHA_SUPPORTED_HEAD_SIZES.end(), head_size)
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!= MMHA_SUPPORTED_HEAD_SIZES.end();
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}
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#define INSTANTIATE_MMHA_NORMAL_AND_PAGED(T, CROSS_ATTENTION) \
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void masked_multihead_attention(const Multihead_attention_params<T, CROSS_ATTENTION>& params, \
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const KVBlockArray& kv_cache_buffer, const KVLinearBuffer& shift_k_cache, const cudaStream_t& stream) \
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{ \
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multihead_attention_<T, KVBlockArray, Multihead_attention_params<T, CROSS_ATTENTION>>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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} \
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void masked_multihead_attention(const Multihead_attention_params<T, CROSS_ATTENTION>& params, \
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const KVLinearBuffer& kv_cache_buffer, const KVLinearBuffer& shift_k_cache, const cudaStream_t& stream) \
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{ \
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multihead_attention_<T, KVLinearBuffer, Multihead_attention_params<T, CROSS_ATTENTION>>( \
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params, kv_cache_buffer, shift_k_cache, stream); \
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}
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(float, true)
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(float, false)
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(uint16_t, true)
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(uint16_t, false)
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#ifdef ENABLE_BF16
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(__nv_bfloat16, true)
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INSTANTIATE_MMHA_NORMAL_AND_PAGED(__nv_bfloat16, false)
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#endif
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#undef INSTANTIATE_MMHA_NORMAL_AND_PAGED
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////////////////////////////////////////////////////////////////////////////////////////////////////
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} // namespace kernels
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} // namespace tensorrt_llm
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