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https://github.com/NVIDIA/TensorRT-LLM.git
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1ac0566a93
* fix for cp > kvHeadNum Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com> * fix for None kv_head_num Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com> --------- Signed-off-by: Dylan Chen <191843203+DylanChen-NV@users.noreply.github.com>
261 lines
13 KiB
C++
261 lines
13 KiB
C++
/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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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 "checkMacrosPlugin.h"
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#include "tensorrt_llm/common/cublasMMWrapper.h"
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#include "tensorrt_llm/common/logger.h"
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#include "tensorrt_llm/common/quantization.h"
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#include "tensorrt_llm/common/stringUtils.h"
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#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fmhaRunner.h"
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#include "tensorrt_llm/kernels/contextFusedMultiHeadAttention/fused_multihead_attention_common.h"
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#include "tensorrt_llm/kernels/gptKernels.h"
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#include "tensorrt_llm/plugins/common/plugin.h"
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#include "tensorrt_llm/plugins/gptAttentionCommon/gptAttentionCommon.h"
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#include <cassert>
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#include <cstddef>
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#include <cstdint>
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#include <set>
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#include <string>
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#include <vector>
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namespace tensorrt_llm::plugins
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{
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// batch_size = num_ctx_requests + num_gen_requests * beam_width
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// num_ctx_requests = number of context requests (single sequence per request).
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// num_gen_requests = number of generation requests (beam_width sequences per request).
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// Context sequences have to appear first, generation sequences after
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// inputs (see GPTAttentionPlugin::isEntryUsed for when each tensor is actually used)
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// 0. input_tensor [batch_size, seq_len, local_hidden_size + 2 * local_num_kv_heads * head_size] or
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// [num_tokens, local_hidden_size + 2 * local_num_kv_heads * head_size] when
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// enable_remove_input_padding
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// 1. sequence_length [batch_size] (optional)
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// 2. host_past_key_value_lengths [batch_size] (int32) (optional)
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// 3. host_max_attention_window_sizes [num_layers] (int32)
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// 4. host_sink_token_length [1] (int32)
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// 5. context_lengths [batch_size]
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// 6. cache_indir [num_gen_requests, beam_width, memory_max_len] (required in beamsearch) (optional)
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// 7. host_request_types [batch_size] int32. 0: context; 1: generation: 2: none. When not in inflight-batching
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// mode,
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// all elements must be identical.
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// 8. past_key_value_pool [batch_size, 2, local_num_kv_heads, max_seq_len, head_size] or
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// block_offsets [batch_size, 2, max_blocks_per_seq] if paged kv cache (optional)
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// 8.1 host_block_offsets [batch_size, 2, max_blocks_per_seq] if paged kv cache (optional)
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// 8.2 host_pool_pointers [2] if paged kv cache (optional)
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// 9. kv_cache_quantization_scale [1] (optional)
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// 10. kv_cache_dequantization_scale [1] (optional)
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// 11. attention_output_quantization_scale [1] (on device, optional)
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// 12. attention_mask [num_tokens, kv_seqlen] (on device, bool, optional)
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// 13. attention_packed_mask [num_tokens, kv_seqlen / 32] (on device, uint32_t, optional)
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// - pack masks by encoding multiple mask positions into a single 32-bit unsigned integer.
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// - see kernels/contextMultiHeadAttention/fmhaPackedMask.cpp for more details.
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// 14. rotary_inv_freq [head_size / 2] or [head_size] (longrope type) (float) (on device, optional)
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// 15. rotary_cos_sin [max_num_embedding_positions, 2] (float) (on device, optional)
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// 16. alibi_slopes [num_heads] (optional for ALiBi position embedding)
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// 17. relative_attention_bias [num_heads] (optional for ALiBi position embedding)
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// 18. host_context_lengths [batch_size] int32. (optional, required when remove_input_padding is true)
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// 19. qkv_bias (optional) [local_hidden_size * 3]
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// 20. spec_decoding_generation_lengths (optional, required when medusa is enabled) (int32_t) [batch_size]
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// 21. spec_decoding_packed_mask (optional, required when medusa is enabled) (int32_t) [num_tokens, packed_mask_dim]
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// packed_mask_dim = divUp(max_num_spec_decoding_tokens + 1, 32)
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// 22. spec_decoding_position_offsets (optional, required when medusa is enabled) (int32_t) [batch_size,
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// max_num_spec_decoding_tokens + 1]
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// 23. spec_decoding_use (optional, bool) [1]: If it is set as true, enable speculative decoding
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// 24. long_rope_rotary_inv_freq [head / 2] (float) (on device, optional)
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// 25. long_rope_rotary_cos_sin [max_num_embedding_positions, 2] (float) (on device, optional)
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// 26. host_runtime_perf_knobs (int64)
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// 27. host_context_progress (void*)
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// 28. position_id_tensor(MLA) [total_tokens], used for rope embedding in MLA
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// 29. q_a_proj_tensor(MLA) [hidden_dim, c_q_dim + c_k_dim + ropd_dim], used to proj compacted QKV
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// 30. q_a_layernorm_tensor(MLA) [c_q_dim], rmsnorm weight for compacted q
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// 31. q_b_proj_tensor(MLA) [c_q_dim, head_num * head_size], weight for companted q to q in context
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// 32. kv_a_proj_with_mqa_tensor(MLA) [c_q_dim, head_num * (c_k_dim + rope_dim)], weight for companted q to kdim in
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// generation
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// 33. kv_a_layernorm_tensor(MLA) [c_k_dim], rmsnorm weight for compacted kv
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// 34. kv_b_proj_tensor(MLA) [c_k_dim, head_num * 2 * (head_size - rope_dim)], weight for compacted kv to kv in
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// context
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// 35. skip_attn (optional, bool) [1]: If it is set as true, skip the atteniton plugin and return
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// directly.
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//
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// outputs
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// output_tensor [batch_size, seq_len, local_hidden_size]
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// present_key_value_pool (optional if not paged kv cache) [batch_size, 2, local_num_kv_heads, max_seq_len,
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// head_size]
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class GPTAttentionPlugin : public GPTAttentionPluginCommon
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{
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public:
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GPTAttentionPlugin(int layer_idx, int num_heads, int vision_start, int vision_length, int num_kv_heads,
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int num_kv_heads_origin, int head_size, int unidirectional, float q_scaling, float attn_logit_softcapping_scale,
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tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type,
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int rotary_embedding_dim, // for RoPE. 0 for non-RoPE
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float rotary_embedding_base, tensorrt_llm::kernels::RotaryScalingType rotary_embedding_scale_type,
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float rotary_embedding_scale, float rotary_embedding_short_m_scale, float rotary_embedding_long_m_scale,
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int rotary_embedding_max_positions, int rotary_embedding_original_max_positions, int tp_size,
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int tp_rank, // for ALiBi
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bool unfuse_qkv_gemm, // for AutoPP
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bool use_logn_scaling, // for LognScaling
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tensorrt_llm::kernels::ContextFMHAType context_fmha_type, int kv_cache_quant_mode, bool remove_input_padding,
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tensorrt_llm::kernels::AttentionMaskType mask_type,
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tensorrt_llm::kernels::BlockSparseParams block_sparse_params, bool paged_kv_cache, int tokens_per_block,
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nvinfer1::DataType type, int32_t max_context_length, bool qkv_bias_enabled, bool cross_attention = false,
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int max_distance = 0, bool pos_shift_enabled = false, bool dense_context_fmha = false,
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bool use_paged_context_fmha = true, bool use_fp8_context_fmha = true, bool has_full_attention_mask = false,
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bool use_cache = true, bool is_spec_decoding_enabled = false,
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bool spec_decoding_is_generation_length_variable = false, int spec_decoding_max_generation_length = 1,
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bool is_mla_enabled = false, int q_lora_rank = 0, int kv_lora_rank = 0, int qk_nope_head_dim = 0,
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int qk_rope_head_dim = 0, int v_head_dim = 0, bool fuse_fp4_quant = false, bool skip_attn = false,
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int cp_size = 1, int cp_rank = 0, std::set<int32_t> cp_group = {});
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GPTAttentionPlugin(void const* data, size_t length);
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~GPTAttentionPlugin() override = default;
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// IPluginV2DynamicExt Methods
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nvinfer1::DimsExprs getOutputDimensions(int outputIndex, nvinfer1::DimsExprs const* inputs, int nbInputs,
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nvinfer1::IExprBuilder& exprBuilder) noexcept override;
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bool supportsFormatCombination(
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int pos, nvinfer1::PluginTensorDesc const* inOut, int nbInputs, int nbOutputs) noexcept override;
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size_t getWorkspaceSize(nvinfer1::PluginTensorDesc const* inputs, int nbInputs,
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nvinfer1::PluginTensorDesc const* outputs, int nbOutputs) const noexcept override;
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int enqueue(nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
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void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream) noexcept override;
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template <typename T, typename AttentionOutT, typename KVCacheBuffer>
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int enqueueImpl(nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
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void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
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template <typename T, typename AttentionOutT = T>
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int enqueueDispatchKVCacheType(nvinfer1::PluginTensorDesc const* inputDesc,
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nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
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cudaStream_t stream);
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template <typename T, typename KVCacheBuffer>
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void configurePluginImpl(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
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nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept;
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template <typename T>
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void configurePluginDispatchKVCacheType(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
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nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept;
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void configurePlugin(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
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nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept override;
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// IPluginV2Ext Methods
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nvinfer1::DataType getOutputDataType(
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int index, nvinfer1::DataType const* inputTypes, int nbInputs) const noexcept override;
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// IPluginV2 Methods
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char const* getPluginType() const noexcept override;
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char const* getPluginVersion() const noexcept override;
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int getNbOutputs() const noexcept override;
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//! This is called on every trt ExecutionContext creation by TRT
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//! Note TRT does not call the initialize on cloned plugin, so clone internally should do initialization.
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GPTAttentionPlugin* clone() const noexcept override;
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size_t getSerializationSize() const noexcept override;
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void serialize(void* buffer) const noexcept override;
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private:
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template <typename T, typename AttentionOutT, typename KVCacheBuffer>
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int enqueueSome(int32_t seqIdxBeg, int32_t localNbSeq, int32_t tokenIdxBeg, int32_t localNbTokens,
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nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
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void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream);
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using IndexType = std::int32_t;
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std::vector<size_t> mEntryIdx;
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enum class IdxEntry : size_t
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{
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QKV_TENSOR,
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K_TENSOR,
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V_TENSOR,
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ATTENTION_MASK,
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ATTENTION_PACKED_MASK,
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SEQUENCE_LENGTH,
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HOST_PAST_KEY_VALUE_LENGTHS,
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HOST_MAX_ATTENTION_WINDOW,
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HOST_SINK_TOKEN_LENGTH,
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CONTEXT_LENGTHS,
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CACHE_INDIR,
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REQUEST_TYPES,
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KV_CACHE_BLOCK_OFFSETS,
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HOST_KV_CACHE_BLOCK_OFFSETS,
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HOST_KV_CACHE_POOL_POINTERS,
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HOST_KV_CACHE_POOL_MAPPING,
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PAST_KEY_VALUE,
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KV_CACHE_QUANTIZATION_SCALE,
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KV_CACHE_DEQUANTIZATION_SCALE,
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ATTENTION_OUTPUT_QUANTIZATION_SCALE,
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ATTENTION_OUTPUT_SF_SCALE,
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ROTARY_INV_FREQ,
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ROTARY_COS_SIN,
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ALIBI_SLOPES,
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RELATIVE_ATTENTION_BIAS,
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CROSS_KV,
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CROSS_KV_LENGTH,
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ENCODER_INPUT_LENGTH,
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HOST_CONTEXT_LENGTH,
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QKV_BIAS_TENSOR,
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SPEC_DECODING_GENERATION_LENGTHS,
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SPEC_DECODING_PACKED_MASK,
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SPEC_DECODING_POSITION_OFFSETS,
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SPEC_DECODING_USE,
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LONG_ROPE_ROTARY_INV_FREQ,
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LONG_ROPE_ROTARY_COS_SIN,
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MROPE_ROTARY_COS_SIN,
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MROPE_POSITION_DELTAS,
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HOST_RUNTIME_PERF_KNOBS,
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HOST_CONTEXT_PROGRESS,
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MLA_Q_B_PROJ_TENSOR,
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MLA_KV_B_PROJ_TENSOR,
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MLA_K_B_PROJ_TRANS_TENSOR,
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SKIP_ATTN,
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LOGN_SCALING,
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ENUM_SIZE, // Used to count the number of IdxEntry, must put in last
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};
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std::string toString(IdxEntry const& entry) const;
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bool isEntryUsed(IdxEntry const& entry) const;
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void initEntryIdx();
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IndexType getIdx(IdxEntry const& entry) const;
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// Get generation input sequence length (might be larger than 1 in the speculative decoding mode).
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int getGenerationInputSequenceLength(
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nvinfer1::PluginTensorDesc const* inputDesc, int32_t localNbSeq, int32_t localNbTokens) const;
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};
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class GPTAttentionPluginCreator : public GPTAttentionPluginCreatorCommon
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{
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public:
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GPTAttentionPluginCreator();
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char const* getPluginName() const noexcept override;
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char const* getPluginVersion() const noexcept override;
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nvinfer1::PluginFieldCollection const* getFieldNames() noexcept override;
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nvinfer1::IPluginV2* createPlugin(char const* name, nvinfer1::PluginFieldCollection const* fc) noexcept override;
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nvinfer1::IPluginV2* deserializePlugin(
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char const* name, void const* serialData, size_t serialLength) noexcept override;
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};
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} // namespace tensorrt_llm::plugins
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