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TensorRT-LLMs/cpp/tensorrt_llm/plugins/gptAttentionPlugin/gptAttentionPlugin.cpp
zhhuang-nv 7e135d2ea7
[None][feat] Use Separate QKV Input Layout for Context MLA (#6538) 
Signed-off-by: Zhen Huang <145532724+zhhuang-nv@users.noreply.github.com>
2025-08-19 22:04:48 +08:00

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/*
* SPDX-FileCopyrightText: Copyright (c) 1993-2022 NVIDIA CORPORATION &
* AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0
*
* 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.
*/
#include "gptAttentionPlugin.h"
#include "tensorrt_llm/batch_manager/contextProgress.h"
#include "tensorrt_llm/common/logger.h"
#include "tensorrt_llm/kernels/decoderMaskedMultiheadAttention.h"
#include "tensorrt_llm/kernels/gptKernels.h"
#include "tensorrt_llm/kernels/unfusedAttentionKernels.h"
#include "tensorrt_llm/plugins/common/checkMacrosPlugin.h"
#include "tensorrt_llm/plugins/common/plugin.h"
#include "tensorrt_llm/plugins/gptAttentionCommon/gptAttentionCommon.h"
#include "tensorrt_llm/plugins/gptAttentionCommon/gptAttentionCommonImpl.h"
#include "tensorrt_llm/runtime/common.h"
#include "tensorrt_llm/runtime/iBuffer.h"
#include "tensorrt_llm/runtime/utils/debugUtils.h"
#include <NvInferRuntimeBase.h>
#include <algorithm>
#include <cstdint>
#include <functional>
#include <numeric>
using namespace nvinfer1;
using namespace tensorrt_llm::kernels;
using namespace tensorrt_llm::common;
using tensorrt_llm::plugins::GPTAttentionPluginCreator;
using tensorrt_llm::plugins::GPTAttentionPlugin;
static char const* GPT_ATTENTION_PLUGIN_VERSION{"1"};
static char const* GPT_ATTENTION_PLUGIN_NAME{"GPTAttention"};
GPTAttentionPlugin::GPTAttentionPlugin(int layer_idx, int num_heads, int vision_start, int vision_length,
int num_kv_heads, int num_kv_heads_origin, int head_size, int unidirectional, float q_scaling,
float attn_logit_softcapping_scale, tensorrt_llm::kernels::PositionEmbeddingType position_embedding_type,
int rotary_embedding_dim, // for RoPE. 0 for non-RoPE
float rotary_embedding_base, tensorrt_llm::kernels::RotaryScalingType rotary_embedding_scale_type,
float rotary_embedding_scale, float rotary_embedding_short_m_scale,
float rotary_embedding_long_m_scale, // magnitude scaling factors for Phi-3 long RoPE
int rotary_embedding_max_positions, int rotary_embedding_original_max_positions, int tp_size,
int tp_rank, // for ALiBi
bool unfuse_qkv_gemm, // for AutoPP
bool use_logn_scaling, // for LognScaling
tensorrt_llm::kernels::ContextFMHAType context_fmha_type, int kv_cache_quant_mode, bool remove_input_padding,
tensorrt_llm::kernels::AttentionMaskType mask_type, tensorrt_llm::kernels::BlockSparseParams block_sparse_params,
bool paged_kv_cache, int tokens_per_block, nvinfer1::DataType type, int32_t max_context_length,
bool qkv_bias_enabled, bool cross_attention, int max_distance, bool pos_shift_enabled, bool dense_context_fmha,
bool use_paged_context_fmha, bool use_fp8_context_fmha, bool has_full_attention_mask, bool use_cache,
bool is_spec_decoding_enabled, bool spec_decoding_is_generation_length_variable,
int spec_decoding_max_generation_length, bool is_mla_enabled, int q_lora_rank, int kv_lora_rank,
int qk_nope_head_dim, int qk_rope_head_dim, int v_head_dim, bool fuse_fp4_quant, bool skip_attn, int cp_size,
int cp_rank, std::set<int32_t> cp_group)
: GPTAttentionPluginCommon(layer_idx, num_heads, vision_start, vision_length, num_kv_heads, num_kv_heads_origin,
head_size, unidirectional, q_scaling, attn_logit_softcapping_scale, position_embedding_type,
rotary_embedding_dim, rotary_embedding_base, rotary_embedding_scale_type, rotary_embedding_scale,
rotary_embedding_short_m_scale, rotary_embedding_long_m_scale, rotary_embedding_max_positions,
rotary_embedding_original_max_positions, tp_size, tp_rank, unfuse_qkv_gemm, use_logn_scaling, context_fmha_type,
kv_cache_quant_mode, remove_input_padding, mask_type, block_sparse_params, paged_kv_cache, tokens_per_block,
type, max_context_length, qkv_bias_enabled, cross_attention, max_distance, pos_shift_enabled,
dense_context_fmha, use_paged_context_fmha, use_fp8_context_fmha, has_full_attention_mask, use_cache,
is_spec_decoding_enabled, spec_decoding_is_generation_length_variable, spec_decoding_max_generation_length,
is_mla_enabled, q_lora_rank, kv_lora_rank, qk_nope_head_dim, qk_rope_head_dim, v_head_dim, fuse_fp4_quant,
skip_attn, cp_size, cp_rank, cp_group)
{
TLLM_CHECK_WITH_INFO(
!is_mla_enabled, "GPTAttentionPlugin no longer supports MLA. Please use the PyTorch workflow instead.");
initEntryIdx();
}
GPTAttentionPlugin::GPTAttentionPlugin(void const* data, size_t length)
: GPTAttentionPluginCommon(data, length)
{
initEntryIdx();
}
std::string GPTAttentionPlugin::toString(IdxEntry const& entry) const
{
#define TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(name) \
case IdxEntry::name: return #name
switch (entry)
{
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(QKV_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(K_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(V_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ATTENTION_MASK);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ATTENTION_PACKED_MASK);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SEQUENCE_LENGTH);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_PAST_KEY_VALUE_LENGTHS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_MAX_ATTENTION_WINDOW);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_SINK_TOKEN_LENGTH);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(CONTEXT_LENGTHS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(CACHE_INDIR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(REQUEST_TYPES);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(KV_CACHE_BLOCK_OFFSETS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_KV_CACHE_BLOCK_OFFSETS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_KV_CACHE_POOL_POINTERS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_KV_CACHE_POOL_MAPPING);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(PAST_KEY_VALUE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(KV_CACHE_QUANTIZATION_SCALE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(KV_CACHE_DEQUANTIZATION_SCALE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ATTENTION_OUTPUT_QUANTIZATION_SCALE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ATTENTION_OUTPUT_SF_SCALE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ROTARY_INV_FREQ);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ROTARY_COS_SIN);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ALIBI_SLOPES);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(RELATIVE_ATTENTION_BIAS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(CROSS_KV);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(CROSS_KV_LENGTH);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ENCODER_INPUT_LENGTH);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_CONTEXT_LENGTH);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(QKV_BIAS_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SPEC_DECODING_GENERATION_LENGTHS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SPEC_DECODING_PACKED_MASK);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SPEC_DECODING_POSITION_OFFSETS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SPEC_DECODING_USE);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(LONG_ROPE_ROTARY_INV_FREQ);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(LONG_ROPE_ROTARY_COS_SIN);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(MROPE_ROTARY_COS_SIN);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(MROPE_POSITION_DELTAS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_RUNTIME_PERF_KNOBS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(HOST_CONTEXT_PROGRESS);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(MLA_Q_B_PROJ_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(MLA_KV_B_PROJ_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(MLA_K_B_PROJ_TRANS_TENSOR);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(SKIP_ATTN);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(LOGN_SCALING);
TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING(ENUM_SIZE);
}
#undef TLLM_GPT_ATTN_IDX_ENTRY_TO_STRING
TLLM_LOG_TRACE(common::fmtstr("Missing string description for IdxEntry enum %lu.\n", static_cast<size_t>(entry)));
return "";
}
bool GPTAttentionPlugin::isEntryUsed(IdxEntry const& entry) const
{
switch (entry)
{
case IdxEntry::QKV_TENSOR: return true;
case IdxEntry::K_TENSOR: return mUnfuseQkvGemm;
case IdxEntry::V_TENSOR: return mUnfuseQkvGemm;
case IdxEntry::ATTENTION_MASK: return useFullCustomMask();
case IdxEntry::ATTENTION_PACKED_MASK: return useCustomMask();
case IdxEntry::SEQUENCE_LENGTH: return useKVCache();
case IdxEntry::HOST_PAST_KEY_VALUE_LENGTHS: return useKVCache();
case IdxEntry::HOST_MAX_ATTENTION_WINDOW: return true;
case IdxEntry::HOST_SINK_TOKEN_LENGTH: return true;
case IdxEntry::CONTEXT_LENGTHS: return true;
case IdxEntry::CACHE_INDIR: return useKVCache();
case IdxEntry::REQUEST_TYPES: return true;
case IdxEntry::KV_CACHE_BLOCK_OFFSETS: return useKVCache() && mPagedKVCache;
case IdxEntry::HOST_KV_CACHE_BLOCK_OFFSETS: return useKVCache() && mPagedKVCache;
case IdxEntry::HOST_KV_CACHE_POOL_POINTERS: return useKVCache() && mPagedKVCache;
case IdxEntry::HOST_KV_CACHE_POOL_MAPPING: return useKVCache() && mPagedKVCache;
case IdxEntry::PAST_KEY_VALUE: return useKVCache() && !mPagedKVCache;
case IdxEntry::KV_CACHE_QUANTIZATION_SCALE: return useKVCache() && mKVCacheQuantMode.hasKvCacheQuant();
case IdxEntry::KV_CACHE_DEQUANTIZATION_SCALE: return useKVCache() && mKVCacheQuantMode.hasKvCacheQuant();
case IdxEntry::ATTENTION_OUTPUT_QUANTIZATION_SCALE: return mFP8ContextFMHA;
case IdxEntry::ATTENTION_OUTPUT_SF_SCALE: return mFuseFp4Quant;
case IdxEntry::ROTARY_INV_FREQ: return isRoPE();
case IdxEntry::ROTARY_COS_SIN: return isRoPE();
case IdxEntry::ALIBI_SLOPES: return isALiBi();
case IdxEntry::RELATIVE_ATTENTION_BIAS: return isRelativePosition();
case IdxEntry::CROSS_KV: return isCrossAttention();
case IdxEntry::CROSS_KV_LENGTH: return isCrossAttention();
case IdxEntry::LOGN_SCALING: return isLognScaling();
case IdxEntry::ENCODER_INPUT_LENGTH: return isCrossAttention();
case IdxEntry::HOST_CONTEXT_LENGTH: return mRemovePadding;
case IdxEntry::QKV_BIAS_TENSOR: return mQKVBiasEnabled;
case IdxEntry::SPEC_DECODING_GENERATION_LENGTHS: return mIsSpecDecodingEnabled;
case IdxEntry::SPEC_DECODING_PACKED_MASK: return mIsSpecDecodingEnabled;
case IdxEntry::SPEC_DECODING_POSITION_OFFSETS: return mIsSpecDecodingEnabled;
case IdxEntry::SPEC_DECODING_USE: return mIsSpecDecodingEnabled;
case IdxEntry::LONG_ROPE_ROTARY_INV_FREQ: return isLongRoPE();
case IdxEntry::LONG_ROPE_ROTARY_COS_SIN: return isLongRoPE();
case IdxEntry::MROPE_ROTARY_COS_SIN: return isMRoPE();
case IdxEntry::MROPE_POSITION_DELTAS: return isMRoPE();
case IdxEntry::HOST_RUNTIME_PERF_KNOBS: return true;
case IdxEntry::HOST_CONTEXT_PROGRESS: return true;
case IdxEntry::MLA_Q_B_PROJ_TENSOR: return mIsMLAEnabled;
case IdxEntry::MLA_KV_B_PROJ_TENSOR: return mIsMLAEnabled;
case IdxEntry::MLA_K_B_PROJ_TRANS_TENSOR: return mIsMLAEnabled;
case IdxEntry::SKIP_ATTN: return mSkipAttn;
default: return false;
}
}
void GPTAttentionPlugin::initEntryIdx()
{
mEntryIdx.resize(static_cast<size_t>(IdxEntry::ENUM_SIZE));
size_t entryIdx = 0;
for (size_t i = 0; i < static_cast<size_t>(IdxEntry::ENUM_SIZE); i++)
{
mEntryIdx[i] = entryIdx;
entryIdx += isEntryUsed(static_cast<IdxEntry>(i));
}
}
GPTAttentionPlugin::IndexType GPTAttentionPlugin::getIdx(IdxEntry const& entry) const
{
TLLM_CHECK_WITH_INFO(
isEntryUsed(entry), common::fmtstr("getIdx() should not be used with entry %s.\n", toString(entry).data()));
return mEntryIdx[static_cast<size_t>(entry)];
}
// IPluginV2DynamicExt Methods
GPTAttentionPlugin* GPTAttentionPlugin::clone() const noexcept
{
return dynamic_cast<GPTAttentionPlugin*>(this->cloneImpl<GPTAttentionPlugin>());
}
static int getPackedTensorHiddenDimIndex(bool removePadding)
{
return removePadding ? 1 : 2;
}
// NOTE: generation input length might be larger than one in the spec decoding mode.
int GPTAttentionPlugin::getGenerationInputSequenceLength(
nvinfer1::PluginTensorDesc const* inputDesc, int32_t localNbSeq, int32_t localNbTokens) const
{
if (mRemovePadding)
{
// Speculative decoding mode might need variable generation input sequence length.
if (mIsSpecDecodingEnabled && mUseSpecDecoding)
{
TLLM_CHECK_WITH_INFO(mCpSize <= 1, "Context Parallel does not support speculative decoding mode for now");
// SPEC_DECODING_POSITION_OFFSETS: [batch_size, max_generation_input_length].
return inputDesc[getIdx(IdxEntry::SPEC_DECODING_POSITION_OFFSETS)].dims.d[1];
}
else
{
if (mCpSize > 1)
{
// Given that localNbTokens == (beamSize * localNbSeq + mCpSize - 1) / mCpSize, but when mCpSize - 1 >
// localNbSeq, there are multiple choices for beamSize. Assume beamSize == 1 here.
TLLM_CHECK_WITH_INFO(localNbTokens == (localNbSeq + mCpSize - 1) / mCpSize,
"Context Parallel does not support beamSize > 1 for non-speculative decoding mode, "
"localNbTokens=%d, localNbSeq=%d",
localNbTokens, localNbSeq);
return 1;
}
// [num_tokens, local_hidden_size] where num_tokens = batch_size * generation_input_length
TLLM_CHECK_WITH_INFO(localNbTokens % localNbSeq == 0,
"seq_len should be same for all generation requests, localNbTokens=%d, localNbSeq=%d", localNbTokens,
localNbSeq);
return localNbTokens / localNbSeq;
}
}
else
{
// We don't have IFB without mRemovePadding, so just take it out from inputDesc
// [batch_size, seq_len, local_hidden_size]
return inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[1];
}
}
// outputs
// output_tensor [batch_size, seq_len, local_hidden_size] or [num_tokens, local_hidden_size]
// present_key_value_pool (optional if mPagedKVCache is false) [batch_size, 2, local_num_kv_heads, max_seq_len,
// head_size]
nvinfer1::DimsExprs GPTAttentionPlugin::getOutputDimensions(
int outputIndex, nvinfer1::DimsExprs const* inputs, int nbInputs, nvinfer1::IExprBuilder& exprBuilder) noexcept
{
if (mFuseFp4Quant)
{
TLLM_CHECK(outputIndex == 0 || outputIndex == 1 || (!mPagedKVCache && useKVCache() && outputIndex == 2));
// Compute the output dimension for FP4 quantized tensor. Consistent with QuantizeToFP4Plugin.
if (outputIndex == 0)
{
auto ret = inputs[getIdx(IdxEntry::QKV_TENSOR)];
return ret;
}
// Compute the output dimension for output scaling factor tensor. Consistent with QuantizeToFP4Plugin.
if (outputIndex == 1)
{
auto ret = inputs[getIdx(IdxEntry::QKV_TENSOR)];
// Sequence dimension or token dimension.
// Pad to multiple of 128.
auto dimM = exprBuilder.operation(DimensionOperation::kCEIL_DIV,
*ret.d[getPackedTensorHiddenDimIndex(mRemovePadding) - 1], *exprBuilder.constant(128));
ret.d[getPackedTensorHiddenDimIndex(mRemovePadding) - 1]
= exprBuilder.operation(DimensionOperation::kPROD, *dimM, *exprBuilder.constant(128));
// Hidden size dimension.
// Div (rounding up) by 16 since 16 elements share one SF and SF padded to k%4==0.
ret.d[getPackedTensorHiddenDimIndex(mRemovePadding)] = exprBuilder.operation(DimensionOperation::kCEIL_DIV,
*ret.d[getPackedTensorHiddenDimIndex(mRemovePadding)], *exprBuilder.constant(16));
return ret;
}
}
else
{
TLLM_CHECK(outputIndex == 0 || (!mPagedKVCache && useKVCache() && outputIndex == 1));
if (outputIndex == 0)
{
auto ret = inputs[getIdx(IdxEntry::QKV_TENSOR)];
// In MLA, the output dim is v_head_dim
auto const head_size = mHeadSize;
ret.d[getPackedTensorHiddenDimIndex(mRemovePadding)] = exprBuilder.operation(
DimensionOperation::kPROD, *exprBuilder.constant(head_size), *exprBuilder.constant(mNumHeads));
return ret;
}
}
return inputs[getIdx(IdxEntry::PAST_KEY_VALUE)];
}
bool GPTAttentionPlugin::supportsFormatCombination(
int pos, nvinfer1::PluginTensorDesc const* inOut, int nbInputs, int nbOutputs) noexcept
{
bool result = false;
int posCaseLine = -1;
if (pos == getIdx(IdxEntry::CONTEXT_LENGTHS) || pos == getIdx(IdxEntry::REQUEST_TYPES)
|| pos == getIdx(IdxEntry::HOST_MAX_ATTENTION_WINDOW) || pos == getIdx(IdxEntry::HOST_SINK_TOKEN_LENGTH)
|| (isEntryUsed(IdxEntry::SPEC_DECODING_PACKED_MASK) && pos == getIdx(IdxEntry::SPEC_DECODING_PACKED_MASK))
|| (isEntryUsed(IdxEntry::SPEC_DECODING_POSITION_OFFSETS)
&& pos == getIdx(IdxEntry::SPEC_DECODING_POSITION_OFFSETS))
|| (isEntryUsed(IdxEntry::SPEC_DECODING_GENERATION_LENGTHS)
&& pos == getIdx(IdxEntry::SPEC_DECODING_GENERATION_LENGTHS))
|| (isEntryUsed(IdxEntry::SPEC_DECODING_USE) && pos == getIdx(IdxEntry::SPEC_DECODING_USE)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32;
}
else if (isMRoPE() && (pos == getIdx(IdxEntry::MROPE_ROTARY_COS_SIN)))
{
return inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
else if (isMRoPE() && (pos == getIdx(IdxEntry::MROPE_POSITION_DELTAS)))
{
return inOut[pos].type == nvinfer1::DataType::kINT32;
}
else if (pos == getIdx(IdxEntry::HOST_RUNTIME_PERF_KNOBS) || pos == getIdx(IdxEntry::HOST_CONTEXT_PROGRESS))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT64;
}
else if (useKVCache()
&& (pos == getIdx(IdxEntry::SEQUENCE_LENGTH) || pos == getIdx(IdxEntry::HOST_PAST_KEY_VALUE_LENGTHS)
|| pos == getIdx(IdxEntry::CACHE_INDIR)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32;
}
else if (isRoPE() && (pos == getIdx(IdxEntry::ROTARY_INV_FREQ) || pos == getIdx(IdxEntry::ROTARY_COS_SIN)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
else if (isLongRoPE()
&& (pos == getIdx(IdxEntry::LONG_ROPE_ROTARY_INV_FREQ) || pos == getIdx(IdxEntry::LONG_ROPE_ROTARY_COS_SIN)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
else if (useKVCache() && mKVCacheQuantMode.hasKvCacheQuant()
&& (pos == getIdx(IdxEntry::KV_CACHE_DEQUANTIZATION_SCALE)
|| pos == getIdx(IdxEntry::KV_CACHE_QUANTIZATION_SCALE)))
{
// kv_scale for mType->int8/fp8 and int8/fp8->mType conversion
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kFLOAT && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (mFP8ContextFMHA && pos == getIdx(IdxEntry::ATTENTION_OUTPUT_QUANTIZATION_SCALE))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kFLOAT && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (mFuseFp4Quant && pos == getIdx(IdxEntry::ATTENTION_OUTPUT_SF_SCALE))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kFLOAT && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useFullCustomMask() && pos == getIdx(IdxEntry::ATTENTION_MASK))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kBOOL && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useCustomMask() && pos == getIdx(IdxEntry::ATTENTION_PACKED_MASK))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32 && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useKVCache() && mPagedKVCache
&& (pos == getIdx(IdxEntry::KV_CACHE_BLOCK_OFFSETS) || pos == getIdx(IdxEntry::HOST_KV_CACHE_BLOCK_OFFSETS)))
{
// kv cache block offsets
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32 && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useKVCache() && mPagedKVCache && (pos == getIdx(IdxEntry::HOST_KV_CACHE_POOL_POINTERS)))
{
// kv cache pool pointers
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT64 && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useKVCache() && mPagedKVCache && (pos == getIdx(IdxEntry::HOST_KV_CACHE_POOL_MAPPING)))
{
// kv cache pool mapping
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32 && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (useKVCache() && mKVCacheQuantMode.hasInt8KvCache()
&& (!mPagedKVCache && (pos == getIdx(IdxEntry::PAST_KEY_VALUE) || pos == nbInputs + 1)))
{
// If use Int8 K/V cache we require I/O KV values to int8
posCaseLine = __LINE__;
result = (inOut[pos].type == nvinfer1::DataType::kINT8) && (inOut[pos].format == TensorFormat::kLINEAR);
}
else if (useKVCache() && mKVCacheQuantMode.hasFp8KvCache()
&& (!mPagedKVCache && (pos == getIdx(IdxEntry::PAST_KEY_VALUE) || pos == nbInputs + 1)))
{
// If use FP8 K/V cache we require I/O KV values to FP8
posCaseLine = __LINE__;
result = (inOut[pos].type == nvinfer1::DataType::kFP8) && (inOut[pos].format == TensorFormat::kLINEAR);
}
else if (mRemovePadding && (pos == getIdx(IdxEntry::HOST_CONTEXT_LENGTH)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32 && inOut[pos].format == TensorFormat::kLINEAR;
}
else if (mCrossAttention
&& (pos == getIdx(IdxEntry::CROSS_KV_LENGTH) || pos == getIdx(IdxEntry::ENCODER_INPUT_LENGTH)))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kINT32;
}
else if (isLognScaling() && pos == getIdx(IdxEntry::LOGN_SCALING))
{
return inOut[pos].type == nvinfer1::DataType::kFLOAT;
}
else if (pos == nbInputs && mFuseFp4Quant)
{
// Set dtype for output FP4 quantized tensor.
posCaseLine = __LINE__;
result = (inOut[pos].type == nvinfer1::DataType::kFP4) && (inOut[pos].format == TensorFormat::kLINEAR);
}
else if (pos == nbInputs + 1 && mFuseFp4Quant)
{
// Set dtype for output scaling factor tensor. Use kINT32 as storage type (same as QuantizeToFP4Plugin).
posCaseLine = __LINE__;
result = (inOut[pos].type == nvinfer1::DataType::kFP8) && (inOut[pos].format == TensorFormat::kLINEAR);
}
else if (pos == nbInputs && mFP8ContextFMHA)
{
// Output tensor now supports fp8 data type.
posCaseLine = __LINE__;
result = (inOut[pos].type == nvinfer1::DataType::kFP8) && (inOut[pos].format == TensorFormat::kLINEAR);
}
else if (mSkipAttn && pos == getIdx(IdxEntry::SKIP_ATTN))
{
posCaseLine = __LINE__;
result = inOut[pos].type == nvinfer1::DataType::kBOOL && inOut[pos].format == TensorFormat::kLINEAR;
}
else
{
posCaseLine = __LINE__;
result = (inOut[pos].type == mType) && (inOut[pos].format == TensorFormat::kLINEAR);
}
TLLM_LOG_DEBUG(
"%s: pos: %d, result: %d, posCaseLine: %d", __PRETTY_FUNCTION__, pos, static_cast<int>(result), posCaseLine);
return result;
}
template <typename T, typename KVCacheBuffer>
void GPTAttentionPlugin::configurePluginImpl(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept
{
TLLM_CHECK(mHeadSize > 0);
int beamWidth = -1;
if (!isCrossAttention() && useKVCache())
{
// desc_val == -1 means beam_width is not static, we should look at min/max/opt.
//
// In prepareEnqueueGeneration, we'll prepare for all cases where beam_width doesn't exceed max.
// TODO: pass min AND max to prepareEnqueueGeneration instead of max only.
int desc_val = in[getIdx(IdxEntry::CACHE_INDIR)].desc.dims.d[1];
int max_val = in[getIdx(IdxEntry::CACHE_INDIR)].max.d[1];
beamWidth = desc_val == -1 ? max_val : desc_val;
}
else
{
beamWidth = 1;
}
TLLM_CHECK(beamWidth != -1);
// Commonly, cyclic_attention_window_size, and max_attention_window_size will be the same
// unless each layer has different attention window sizes.
// the kv_cache capacity.
int max_encoder_context_len = isCrossAttention() ? in[getIdx(IdxEntry::CROSS_KV_LENGTH)].desc.dims.d[0] : 0;
int const max_attention_window_size = isCrossAttention()
? max_encoder_context_len
: (useKVCache() ? in[getIdx(IdxEntry::CACHE_INDIR)].desc.dims.d[2] : 0);
int const cyclic_attention_window_size = max_attention_window_size;
int const num_requests = 256;
int const sink_token_length = 0;
EnqueueGenerationParams<T> enqueueParams;
enqueueParams.max_attention_window_size = max_attention_window_size;
enqueueParams.cyclic_attention_window_size = cyclic_attention_window_size;
enqueueParams.max_cyclic_attention_window_size = cyclic_attention_window_size;
enqueueParams.sink_token_length = sink_token_length;
enqueueParams.beam_width = beamWidth;
enqueueParams.num_requests = num_requests;
prepareEnqueueGeneration<T, KVCacheBuffer>(enqueueParams);
// Always reserve SemaphoreArray (for multi-block mode) as MMHA may enable multi-block mode when shared memory is
// not enough.
auto const& ctxLenTensor = in[getIdx(IdxEntry::CONTEXT_LENGTHS)];
TLLM_CHECK_DEBUG(ctxLenTensor.max.nbDims == 1);
int32_t const max_batch_beam = in[getIdx(IdxEntry::CONTEXT_LENGTHS)].max.d[0];
reserveSemaphoreArray(mNumHeads * max_batch_beam);
}
template <typename T>
void GPTAttentionPlugin::configurePluginDispatchKVCacheType(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept
{
if (mPagedKVCache)
{
configurePluginImpl<T, KVBlockArray>(in, nbInputs, out, nbOutputs);
}
else
{
configurePluginImpl<T, KVLinearBuffer>(in, nbInputs, out, nbOutputs);
}
}
void GPTAttentionPlugin::configurePlugin(nvinfer1::DynamicPluginTensorDesc const* in, int nbInputs,
nvinfer1::DynamicPluginTensorDesc const* out, int nbOutputs) noexcept
{
if (mType == nvinfer1::DataType::kHALF)
{
configurePluginDispatchKVCacheType<half>(in, nbInputs, out, nbOutputs);
}
else if (mType == nvinfer1::DataType::kFLOAT)
{
configurePluginDispatchKVCacheType<float>(in, nbInputs, out, nbOutputs);
}
#ifdef ENABLE_BF16
else if (mType == nvinfer1::DataType::kBF16)
{
configurePluginDispatchKVCacheType<__nv_bfloat16>(in, nbInputs, out, nbOutputs);
}
#endif
}
size_t GPTAttentionPlugin::getWorkspaceSize(nvinfer1::PluginTensorDesc const* inputs, int nbInputs,
nvinfer1::PluginTensorDesc const* outputs, int nbOutputs) const noexcept
{
int const max_context_length = mMaxContextLength;
int const cross_kv_length = isCrossAttention() ? inputs[getIdx(IdxEntry::CROSS_KV_LENGTH)].dims.d[0] : 0;
int const max_num_seq = inputs[getIdx(IdxEntry::CONTEXT_LENGTHS)].dims.d[0];
auto const type = inputs[getIdx(IdxEntry::QKV_TENSOR)].type;
int const max_kv_cache_length
= isCrossAttention() ? cross_kv_length : (useKVCache() ? inputs[getIdx(IdxEntry::CACHE_INDIR)].dims.d[2] : 0);
int const max_num_tokens
= mRemovePadding ? inputs[getIdx(IdxEntry::QKV_TENSOR)].dims.d[0] : max_num_seq * max_context_length;
size_t const context_workspace_size
= getWorkspaceSizeForContext(type, max_num_seq, max_context_length, cross_kv_length, max_num_tokens);
size_t const generation_workspace_size
= getWorkspaceSizeForGeneration(type, max_num_seq, max_kv_cache_length, max_num_tokens);
size_t attention_input_workspace_size = 0;
if (mUnfuseQkvGemm)
{
int const local_hidden_units_q
= inputs[getIdx(IdxEntry::QKV_TENSOR)].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)];
int const local_hidden_units_kv
= inputs[getIdx(IdxEntry::K_TENSOR)].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)];
size_t const size = tensorrt_llm::runtime::BufferDataType(type).getSize();
size_t const attention_input_size = size * max_num_tokens * (local_hidden_units_q + 2 * local_hidden_units_kv);
size_t workspaces[1];
workspaces[0] = attention_input_size;
attention_input_workspace_size = tensorrt_llm::common::calculateTotalWorkspaceSize(workspaces, 1);
}
return std::max(context_workspace_size, generation_workspace_size) + attention_input_workspace_size;
}
static size_t getStride(nvinfer1::Dims const& dims, int n)
{
TLLM_CHECK(n >= 0 && n < dims.nbDims);
return std::accumulate(dims.d + n + 1, dims.d + dims.nbDims, 1, std::multiplies<size_t>{});
}
template <typename T, typename AttentionOutT, typename KVCacheBuffer>
int GPTAttentionPlugin::enqueueImpl(nvinfer1::PluginTensorDesc const* inputDesc,
nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream)
{
TLLM_LOG_TRACE("Attention plugin start at layer %d", mLayerIdx);
using runtime::RequestType;
int32_t const nbSeq = inputDesc[getIdx(IdxEntry::CONTEXT_LENGTHS)].dims.d[0];
RequestType const* reqTypes = static_cast<RequestType const*>(inputs[getIdx(IdxEntry::REQUEST_TYPES)]);
int32_t nbContextRequests = 0;
int32_t contextTokenIdxEnd = 0;
int32_t contextTokenIdxEndForCp = 0;
// count context requests
for (int32_t seqIdx = 0; seqIdx < nbSeq; seqIdx++)
{
if (reqTypes[seqIdx] != RequestType::kCONTEXT)
{
break;
}
++nbContextRequests;
contextTokenIdxEnd += mRemovePadding
? static_cast<int32_t const*>(inputs[getIdx(IdxEntry::HOST_CONTEXT_LENGTH)])[seqIdx]
: inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[1];
contextTokenIdxEndForCp += mRemovePadding
? (static_cast<int32_t const*>(inputs[getIdx(IdxEntry::HOST_CONTEXT_LENGTH)])[seqIdx] + mCpSize - 1)
/ mCpSize
: (inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[1] + mCpSize - 1) / mCpSize;
}
for (int32_t seqIdx = nbContextRequests; seqIdx < nbSeq; seqIdx++)
{
TLLM_CHECK(reqTypes[seqIdx] == RequestType::kGENERATION);
}
// mixed requests require mRemovePadding and mPagedKVCache
if (nbContextRequests != 0 && nbContextRequests != nbSeq)
{
TLLM_CHECK(mRemovePadding && mPagedKVCache);
}
if (nbContextRequests > 0)
{
auto seqIdxBeg = 0;
auto tokenIdxBeg = 0;
auto localNbTokens = contextTokenIdxEnd;
enqueueSome<T, AttentionOutT, KVCacheBuffer>(seqIdxBeg, nbContextRequests, tokenIdxBeg, localNbTokens,
inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
if (auto nbGenerationSeq = nbSeq - nbContextRequests; nbGenerationSeq > 0)
{
auto seqIdxBeg = nbContextRequests;
auto tokenIdxBeg = mCpSize > 1 ? contextTokenIdxEndForCp : contextTokenIdxEnd;
// if mRemovePadding is true, we may have IFB, and need to remove context tokens.
// if mRemovePadding is false, it is only generation requests, so just multiply batch_beam and seq_len (May not
// 1 for Parallel Decoding)
auto localNbTokens = mRemovePadding
? inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[0] - tokenIdxBeg
: inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[0] * inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[1];
enqueueSome<T, AttentionOutT, KVCacheBuffer>(seqIdxBeg, nbGenerationSeq, tokenIdxBeg, localNbTokens, inputDesc,
outputDesc, inputs, outputs, workspace, stream);
}
sync_check_cuda_error(stream);
TLLM_LOG_TRACE("Attention plugin stop at layer %d", mLayerIdx);
return 0;
}
template <typename T, typename AttentionOutT, typename KVCacheBuffer>
int GPTAttentionPlugin::enqueueSome(int32_t seqIdxBeg, int32_t localNbSeq, int32_t tokenIdxBeg, int32_t localNbTokens,
nvinfer1::PluginTensorDesc const* inputDesc, nvinfer1::PluginTensorDesc const* outputDesc,
void const* const* inputs, void* const* outputs, void* workspace, cudaStream_t stream)
{
// relative_attention_bias [head_num, max_seq_len, max_seq_len] (optional in relative position)
// or [head_num, num_buckets] (optional in implicit relative attention)
// cross_kv [batch_size, seq_len, 2 * local_hidden_size] or [num_tokens, 2 * local_hidden_size]
// when enable remove_input_padding (optional in cross attention mode)
// cross_kv_length [int] max encoder input context length (optional in cross attention mode)
// encoder_input_lengths [batch_size] raw sequence lengths (optional in cross attention mode)
using runtime::RequestType;
auto const* const reqTypeInBatchPtr
= static_cast<RequestType const*>(inputs[getIdx(IdxEntry::REQUEST_TYPES)]) + seqIdxBeg;
bool const is_context = (reqTypeInBatchPtr[0] == RequestType::kCONTEXT);
T const* attention_input = static_cast<T const*>(inputs[getIdx(IdxEntry::QKV_TENSOR)])
+ inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)]
* size_t(tokenIdxBeg);
bool changeSpecDecodingMode = false;
if (mIsSpecDecodingEnabled)
{
bool useSpecDecoding
= static_cast<bool>(reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::SPEC_DECODING_USE)])[0]);
changeSpecDecodingMode = mUseSpecDecoding != useSpecDecoding;
mUseSpecDecoding = useSpecDecoding;
}
[[maybe_unused]] MlaParams<T> mla_params;
T const* qkv_bias = nullptr;
if (mQKVBiasEnabled)
{
qkv_bias = reinterpret_cast<T const*>(inputs[getIdx(IdxEntry::QKV_BIAS_TENSOR)]);
}
// Note we still need context length during generation for MMHA optimization.
int32_t const max_context_q_len = [&]()
{
if (!mRemovePadding)
{
return static_cast<int>(inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[1]);
}
auto const host_context_lengths
= static_cast<int32_t const*>(inputs[getIdx(IdxEntry::HOST_CONTEXT_LENGTH)]) + seqIdxBeg;
return *std::max_element(host_context_lengths, host_context_lengths + localNbSeq);
}();
// Rotary inv_freq, cos_sin cache to avoid re-computing.
float const* rotary_inv_freq = nullptr;
float2 const* rotary_cos_sin = nullptr;
bool const useLongRoPECache = isLongRoPE() && max_context_q_len > mRotaryEmbeddingOriginalMaxPositions;
if (isRoPE())
{
auto inputName = useLongRoPECache ? IdxEntry::LONG_ROPE_ROTARY_INV_FREQ : IdxEntry::ROTARY_INV_FREQ;
rotary_inv_freq = reinterpret_cast<float const*>(inputs[getIdx(inputName)]);
}
if (isRoPE())
{
auto inputName = useLongRoPECache ? IdxEntry::LONG_ROPE_ROTARY_COS_SIN : IdxEntry::ROTARY_COS_SIN;
rotary_cos_sin = reinterpret_cast<float2 const*>(inputs[getIdx(inputName)]);
}
auto const mrope_rotary_cos_sin
= isMRoPE() ? reinterpret_cast<float2 const*>(inputs[getIdx(IdxEntry::MROPE_ROTARY_COS_SIN)]) : nullptr;
auto const mrope_position_deltas
= isMRoPE() ? reinterpret_cast<int32_t const*>(inputs[getIdx(IdxEntry::MROPE_POSITION_DELTAS)]) : nullptr;
if (mUnfuseQkvGemm)
{
int const max_seqlen = inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[mRemovePadding ? 0 : 1];
int const batch_size = mRemovePadding ? 1 : inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[0];
T const* attention_input_q = static_cast<T const*>(inputs[getIdx(IdxEntry::QKV_TENSOR)]);
T const* attention_input_k = static_cast<T const*>(inputs[getIdx(IdxEntry::K_TENSOR)]);
T const* attention_input_v = static_cast<T const*>(inputs[getIdx(IdxEntry::V_TENSOR)]);
size_t const hidden_units_q
= inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)];
size_t const hidden_units_kv
= inputDesc[getIdx(IdxEntry::K_TENSOR)].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)];
size_t const hidden_units = hidden_units_q + 2 * hidden_units_kv;
size_t const size_qkv = sizeof(T) * hidden_units;
size_t const size_q = sizeof(T) * hidden_units_q;
size_t const size_kv = sizeof(T) * hidden_units_kv;
size_t const total_size = size_qkv * batch_size * max_seqlen;
int8_t* workspace_byte_ptr = reinterpret_cast<int8_t*>(workspace);
size_t offset = 0;
T* attention_input_qkv = reinterpret_cast<T*>(nextWorkspacePtr(workspace_byte_ptr, offset, total_size));
workspace = reinterpret_cast<void*>(workspace_byte_ptr + offset);
cudaMemcpy2DAsync(attention_input_qkv, size_qkv, attention_input_q, size_q, size_q, batch_size * max_seqlen,
cudaMemcpyDeviceToDevice, stream);
cudaMemcpy2DAsync(attention_input_qkv + hidden_units_q, size_qkv, attention_input_k, size_kv, size_kv,
batch_size * max_seqlen, cudaMemcpyDeviceToDevice, stream);
cudaMemcpy2DAsync(attention_input_qkv + hidden_units_q + hidden_units_kv, size_qkv, attention_input_v, size_kv,
size_kv, batch_size * max_seqlen, cudaMemcpyDeviceToDevice, stream);
attention_input = attention_input_qkv + hidden_units * tokenIdxBeg;
}
int const* context_q_lengths = reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::CONTEXT_LENGTHS)]) + seqIdxBeg;
int const* sequence_kv_length = useKVCache()
? static_cast<int const*>(inputs[getIdx(IdxEntry::SEQUENCE_LENGTH)]) + seqIdxBeg
: context_q_lengths;
int max_encoder_context_len = isCrossAttention() ? inputDesc[getIdx(IdxEntry::CROSS_KV_LENGTH)].dims.d[0] : 0;
// for enc-dec model, since decoder_input_ids could be longer than 1,
// such model has an encoder context (for cross attn) and an decoder context (for self attn)
// clarify 3 lens:
// -- max_context_q_len: len of decoder input. No "max" concept, it's what it is given.
// Also called (decoder_)input_seq_length, normally 1 for encoder-decoder start token
// -- max_seq_len: max allowed len of decoder output, i.e. final results
// -- max_encoder_context_len: len of encoder input (in cross attn). Also called encoder_input_seq_length
int const beamWidth
= isCrossAttention() ? 1 : (useKVCache() ? inputDesc[getIdx(IdxEntry::CACHE_INDIR)].dims.d[1] : 1);
// Commonly, cyclic_attention_window_size, and max_attention_window_size will be the same
// unless each layer has different attention window sizes.
// the kv_cache capacity.
int const max_attention_window_size = isCrossAttention()
? max_encoder_context_len
: (useKVCache() ? inputDesc[getIdx(IdxEntry::CACHE_INDIR)].dims.d[2] : 0);
// The cyclic_attention_window_size will determine the cyclic kv cache position of new tokens.
// Note that this cyclic_attention_window_size might be smaller than the actual kv cache capactity.
int const* cyclic_attention_window_sizes
= reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::HOST_MAX_ATTENTION_WINDOW)]);
int const cyclic_attention_window_size
= isCrossAttention() ? max_encoder_context_len : cyclic_attention_window_sizes[mLayerIdx];
int const sink_token_length = reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::HOST_SINK_TOKEN_LENGTH)])[0];
int const num_attn_layer = inputDesc[getIdx(IdxEntry::HOST_MAX_ATTENTION_WINDOW)].dims.d[0];
int const max_cyclic_attention_window_size = isCrossAttention()
? max_encoder_context_len
: *std::max_element(cyclic_attention_window_sizes, cyclic_attention_window_sizes + num_attn_layer);
bool const can_use_one_more_block = beamWidth > 1;
float const* kv_scale_orig_quant = nullptr;
float const* kv_scale_quant_orig = nullptr;
if (useKVCache() && mKVCacheQuantMode.hasKvCacheQuant())
{
assert(inputDesc[getIdx(IdxEntry::KV_CACHE_QUANTIZATION_SCALE)].type == nvinfer1::DataType::kFLOAT);
assert(inputDesc[getIdx(IdxEntry::KV_CACHE_DEQUANTIZATION_SCALE)].type == nvinfer1::DataType::kFLOAT);
kv_scale_orig_quant = reinterpret_cast<float const*>(inputs[getIdx(IdxEntry::KV_CACHE_QUANTIZATION_SCALE)]);
kv_scale_quant_orig = reinterpret_cast<float const*>(inputs[getIdx(IdxEntry::KV_CACHE_DEQUANTIZATION_SCALE)]);
}
float const* attention_output_orig_quant = nullptr;
if (mFP8ContextFMHA)
{
assert(inputDesc[getIdx(IdxEntry::ATTENTION_OUTPUT_QUANTIZATION_SCALE)].type == nvinfer1::DataType::kFLOAT);
attention_output_orig_quant
= reinterpret_cast<float const*>(inputs[getIdx(IdxEntry::ATTENTION_OUTPUT_QUANTIZATION_SCALE)]);
}
float const* attention_output_sf_scale = nullptr;
if (mFuseFp4Quant)
{
assert(inputDesc[getIdx(IdxEntry::ATTENTION_OUTPUT_SF_SCALE)].type == nvinfer1::DataType::kFLOAT);
attention_output_sf_scale = reinterpret_cast<float const*>(inputs[getIdx(IdxEntry::ATTENTION_OUTPUT_SF_SCALE)]);
}
uint32_t const* attention_packed_mask = nullptr;
if (useCustomMask())
{
assert(inputDesc[getIdx(IdxEntry::ATTENTION_PACKED_MASK)].type == nvinfer1::DataType::kINT32);
attention_packed_mask = reinterpret_cast<uint32_t const*>(inputs[getIdx(IdxEntry::ATTENTION_PACKED_MASK)]);
}
bool const* attention_mask = nullptr;
int attention_mask_stride = 0;
if (useFullCustomMask())
{
attention_mask_stride = static_cast<int>(inputDesc[getIdx(IdxEntry::ATTENTION_MASK)].dims.d[1]);
attention_mask = reinterpret_cast<bool const*>(inputs[getIdx(IdxEntry::ATTENTION_MASK)])
+ attention_mask_stride * static_cast<size_t>(tokenIdxBeg);
}
int max_blocks_per_sequence = 0;
kernels::KVBlockArray::DataType* block_offsets = nullptr;
kernels::KVBlockArray::DataType* host_block_offsets = nullptr;
void* host_primary_pool_pointer = nullptr;
void* host_secondary_pool_pointer = nullptr;
if (useKVCache() && mPagedKVCache)
{
auto const& kvCacheBlockOffsetsShape = inputDesc[getIdx(IdxEntry::KV_CACHE_BLOCK_OFFSETS)].dims;
max_blocks_per_sequence = kvCacheBlockOffsetsShape.d[kvCacheBlockOffsetsShape.nbDims - 1];
std::int32_t const* host_pool_mapping
= static_cast<std::int32_t const*>(inputs[getIdx(IdxEntry::HOST_KV_CACHE_POOL_MAPPING)]);
int32_t const layerToPool = host_pool_mapping[mLayerIdx * 2];
int32_t const layerIdxInCachePool = host_pool_mapping[mLayerIdx * 2 + 1];
TLLM_LOG_TRACE("Layer%d: LayerCachePoolLocator{.indexOfPool=%d, .layerIdxInCachePool=%d}", mLayerIdx,
layerToPool, layerIdxInCachePool);
auto const seqStride = getStride(kvCacheBlockOffsetsShape, 1);
auto const poolStride = getStride(kvCacheBlockOffsetsShape, 0);
auto const seqOffset = seqIdxBeg * seqStride;
auto const poolOffset = layerToPool * poolStride;
block_offsets
= reinterpret_cast<kernels::KVBlockArray::DataType*>(inputs[getIdx(IdxEntry::KV_CACHE_BLOCK_OFFSETS)])
+ poolOffset + seqOffset;
host_block_offsets
= reinterpret_cast<kernels::KVBlockArray::DataType*>(inputs[getIdx(IdxEntry::HOST_KV_CACHE_BLOCK_OFFSETS)])
+ poolOffset + seqOffset;
auto const* const typed_host_pool_pointers
= static_cast<char* const*>(inputs[getIdx(IdxEntry::HOST_KV_CACHE_POOL_POINTERS)]);
auto const cacheElemSize = (mKVCacheQuantMode.hasKvCacheQuant() ? 1 : sizeof(T));
auto const kv_cache_head_num = (mNumKVHeads + mCpSize - 1) / mCpSize;
auto const blockSize = mTokensPerBlock * kv_cache_head_num * mHeadSize;
auto const bytesPerBlock = blockSize * cacheElemSize;
auto const layerOffset = layerIdxInCachePool * 2 * bytesPerBlock;
host_primary_pool_pointer = reinterpret_cast<void*>(typed_host_pool_pointers[layerToPool * 2] + layerOffset);
host_secondary_pool_pointer
= reinterpret_cast<void*>(typed_host_pool_pointers[layerToPool * 2 + 1] + layerOffset);
}
// The index of kv cache tensor in outputs. If fuse FP4 quant, an additional scaling factor output is added before
// the kv cache tensor.
int const kvCacheIdxInOutputs = mFuseFp4Quant ? 2 : 1;
// The number of elements per storage type. For FP4 output, storage type is uint8_t.
int const numEltsPerStorageType = mFuseFp4Quant ? 2 : 1;
AttentionOutT* context_buf_ = static_cast<AttentionOutT*>(outputs[0])
+ outputDesc[0].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)] * tokenIdxBeg / numEltsPerStorageType;
__nv_fp8_e4m3* context_buf_sf_ = nullptr;
if (mFuseFp4Quant)
{
// The output address for FP4 scaling factor.
context_buf_sf_ = static_cast<__nv_fp8_e4m3*>(outputs[1]);
}
void* key_value_cache = nullptr;
if (useKVCache() && !mPagedKVCache)
{
auto const cacheElemSize = (mKVCacheQuantMode.hasKvCacheQuant() ? 1 : sizeof(T));
key_value_cache = static_cast<std::byte*>(outputs[kvCacheIdxInOutputs])
+ cacheElemSize * getStride(outputDesc[kvCacheIdxInOutputs].dims, 0) * seqIdxBeg;
void const* past_key_value_cache = inputs[getIdx(IdxEntry::PAST_KEY_VALUE)];
if (past_key_value_cache != outputs[kvCacheIdxInOutputs])
{
auto shape = outputDesc[kvCacheIdxInOutputs].dims;
auto const size
= cacheElemSize * std::accumulate(shape.d, shape.d + shape.nbDims, 1, std::multiplies<size_t>{});
cudaMemcpyAsync(outputs[kvCacheIdxInOutputs], past_key_value_cache, size, cudaMemcpyDeviceToDevice, stream);
}
}
T const* alibi_slopes = isALiBi() ? static_cast<T const*>(inputs[getIdx(IdxEntry::ALIBI_SLOPES)]) : nullptr;
int const* spec_decoding_packed_mask = nullptr;
int const* spec_decoding_position_offsets = nullptr;
int const* spec_decoding_generation_lengths = nullptr;
int num_decoding_draft_tokens = 0;
if (mIsSpecDecodingEnabled && mUseSpecDecoding)
{
// Second dimension of spec_decoding_position_offsets is num_decoding_draft_tokens + 1.
// [batch_size, num_decoding_draft_tokens + 1]
num_decoding_draft_tokens = inputDesc[getIdx(IdxEntry::SPEC_DECODING_POSITION_OFFSETS)].dims.d[1] - 1;
if (num_decoding_draft_tokens > 0)
{
// spec_decoding_* tensors are not filled for context requests. Hence, always strting from 0th index
int32_t constexpr genSeqIdx = 0;
spec_decoding_packed_mask = static_cast<int const*>(inputs[getIdx(IdxEntry::SPEC_DECODING_PACKED_MASK)])
+ genSeqIdx * getStride(inputDesc[getIdx(IdxEntry::SPEC_DECODING_PACKED_MASK)].dims, 0);
// Packed as [num_tokens, packed_mask_size]
// Use seqIdxBeg * (num_decoding_draft_tokens + 1) here as only generation tokens have the packed_mask
// buffer.
// TODO: support variable sequence length based on generationTokenIdxBeg.
spec_decoding_packed_mask = static_cast<int const*>(inputs[getIdx(IdxEntry::SPEC_DECODING_PACKED_MASK)])
+ genSeqIdx * (num_decoding_draft_tokens + 1)
* getStride(inputDesc[getIdx(IdxEntry::SPEC_DECODING_PACKED_MASK)].dims, 0);
spec_decoding_position_offsets
= static_cast<int const*>(inputs[getIdx(IdxEntry::SPEC_DECODING_POSITION_OFFSETS)])
+ genSeqIdx * getStride(inputDesc[getIdx(IdxEntry::SPEC_DECODING_POSITION_OFFSETS)].dims, 0);
spec_decoding_generation_lengths
= static_cast<int const*>(inputs[getIdx(IdxEntry::SPEC_DECODING_GENERATION_LENGTHS)]) + genSeqIdx;
}
}
int32_t const* host_past_kv_len_list = useKVCache()
? static_cast<int const*>(inputs[getIdx(IdxEntry::HOST_PAST_KEY_VALUE_LENGTHS)]) + seqIdxBeg
: nullptr;
int32_t const max_context_kv_len = useKVCache()
? *std::max_element(host_past_kv_len_list, host_past_kv_len_list + localNbSeq)
: max_context_q_len;
int const* host_context_lengths
= mRemovePadding ? reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::HOST_CONTEXT_LENGTH)]) : nullptr;
int64_t const* runtime_perf_knobs = static_cast<int64_t const*>(inputs[getIdx(IdxEntry::HOST_RUNTIME_PERF_KNOBS)]);
EnqueueParams<T> common_enqueue_params;
common_enqueue_params.attention_input = attention_input;
common_enqueue_params.qkv_bias = qkv_bias;
common_enqueue_params.attention_mask = attention_mask;
common_enqueue_params.rotary_inv_freq = rotary_inv_freq;
common_enqueue_params.rotary_cos_sin = rotary_cos_sin;
common_enqueue_params.max_attention_window_size = max_attention_window_size;
common_enqueue_params.cyclic_attention_window_size = cyclic_attention_window_size;
common_enqueue_params.max_cyclic_attention_window_size = max_cyclic_attention_window_size;
common_enqueue_params.can_use_one_more_block = can_use_one_more_block;
common_enqueue_params.sink_token_length = sink_token_length;
common_enqueue_params.kv_scale_orig_quant = kv_scale_orig_quant;
common_enqueue_params.kv_scale_quant_orig = kv_scale_quant_orig;
common_enqueue_params.attention_output_orig_quant = attention_output_orig_quant;
common_enqueue_params.attention_output_sf_scale = attention_output_sf_scale;
common_enqueue_params.alibi_slopes = alibi_slopes;
common_enqueue_params.context_buf = context_buf_;
common_enqueue_params.context_buf_sf = context_buf_sf_;
common_enqueue_params.key_value_cache = key_value_cache;
common_enqueue_params.block_offsets = block_offsets;
common_enqueue_params.host_primary_pool_pointer = host_primary_pool_pointer;
common_enqueue_params.host_secondary_pool_pointer = host_secondary_pool_pointer;
common_enqueue_params.num_tokens = localNbTokens;
common_enqueue_params.max_blocks_per_sequence = max_blocks_per_sequence;
common_enqueue_params.sequence_lengths = sequence_kv_length;
common_enqueue_params.context_lengths = context_q_lengths;
common_enqueue_params.host_context_lengths = host_context_lengths;
common_enqueue_params.workspace = workspace;
common_enqueue_params.runtime_perf_knobs = runtime_perf_knobs;
if (isRelativePosition())
{
common_enqueue_params.relative_attention_bias
= static_cast<T const*>(inputs[getIdx(IdxEntry::RELATIVE_ATTENTION_BIAS)]);
common_enqueue_params.relative_attention_bias_stride
= inputDesc[getIdx(IdxEntry::RELATIVE_ATTENTION_BIAS)].dims.d[1]; // max_seq_len or num_buckets
}
if (isLognScaling())
{
common_enqueue_params.logn_scaling_ptr = static_cast<float const*>(inputs[getIdx(IdxEntry::LOGN_SCALING)]);
}
if (isCrossAttention())
{
common_enqueue_params.encoder_input_lengths
= reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::ENCODER_INPUT_LENGTH)]) + seqIdxBeg;
}
if (is_context) // context stage
{
int const batch_size = localNbSeq;
int const request_batch_size = batch_size;
// num of total tokens (without paddings when remove paddings).
int num_encoder_tokens = 0;
if (isCrossAttention())
{
if (!mRemovePadding)
{
num_encoder_tokens = request_batch_size * max_encoder_context_len;
}
else
{
num_encoder_tokens = inputDesc[getIdx(IdxEntry::CROSS_KV)].dims.d[0];
}
}
common_enqueue_params.input_seq_length = max_context_q_len;
common_enqueue_params.max_past_kv_length = max_context_kv_len;
EnqueueContextParams<T> enqueue_params{common_enqueue_params};
enqueue_params.attention_packed_mask = attention_packed_mask;
enqueue_params.host_block_offsets = host_block_offsets;
enqueue_params.batch_size = batch_size;
enqueue_params.mrope_rotary_cos_sin = mrope_rotary_cos_sin;
enqueue_params.total_kv_len = enqueue_params.num_tokens;
if (isCrossAttention())
{
enqueue_params.cross_kv = static_cast<T const*>(inputs[getIdx(IdxEntry::CROSS_KV)]);
enqueue_params.cross_kv_length = max_encoder_context_len;
enqueue_params.num_encoder_tokens = num_encoder_tokens;
}
enqueueContext<T, KVCacheBuffer>(enqueue_params, stream);
{
std::string const afterContexStr = "ctx attention at layer " + std::to_string(mLayerIdx);
TLLM_LOG_TRACE("GPTAttentionPlugin - %s", afterContexStr.c_str());
auto progress = static_cast<batch_manager::ContextProgress* const*>(
inputs[getIdx(IdxEntry::HOST_CONTEXT_PROGRESS)])[0];
if (progress != nullptr)
{
progress->recordEvent(mLayerIdx, stream);
}
if (!mFuseFp4Quant)
{
TLLM_CHECK_DEBUG_WITH_INFO(
tensorrt_llm::runtime::utils::tensorHasInvalid(localNbTokens,
outputDesc[0].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)],
mFP8ContextFMHA ? nvinfer1::DataType::kFP8 : mType, context_buf_, stream, afterContexStr)
== false,
"Found invalid number (NaN or Inf) in " + afterContexStr);
}
}
}
else // generation stage; max_context_q_len == input_seq_len == 1
{
TLLM_CHECK_WITH_INFO(useKVCache(), "KV-cache-less is only supported for context");
int batch_beam = localNbSeq;
TLLM_CHECK(batch_beam % beamWidth == 0);
int32_t const num_requests = batch_beam / beamWidth;
int const* cache_indir
= beamWidth == 1 ? nullptr : reinterpret_cast<int const*>(inputs[getIdx(IdxEntry::CACHE_INDIR)]);
// Medusa: the max input sequence length if variable sequence length is needed.
int const input_seq_length = getGenerationInputSequenceLength(inputDesc, localNbSeq, localNbTokens);
int const max_past_kv_length = isCrossAttention() ? max_encoder_context_len : max_context_kv_len;
auto qkvDims = inputDesc[getIdx(IdxEntry::QKV_TENSOR)].dims;
TLLM_CHECK_WITH_INFO(input_seq_length == 1 || (mIsSpecDecodingEnabled && mUseSpecDecoding),
"Only speculative decoding mode supports input length > 1 in the generation phase, input_seq_length=%d, "
"mIsSpecDecodingEnabled=%s, nDims=%d, (" FMT_DIM ", " FMT_DIM ", " FMT_DIM ")",
input_seq_length, mIsSpecDecodingEnabled ? "true" : "false", qkvDims.nbDims, qkvDims.d[0], qkvDims.d[1],
qkvDims.d[2]);
TLLM_CHECK_WITH_INFO(
input_seq_length == num_decoding_draft_tokens + 1, "The generation input length is not expected.");
common_enqueue_params.input_seq_length = input_seq_length;
common_enqueue_params.max_past_kv_length = max_past_kv_length;
EnqueueGenerationParams<T> enqueue_params{common_enqueue_params};
enqueue_params.beam_width = beamWidth;
enqueue_params.attention_mask_stride = attention_mask_stride;
enqueue_params.num_requests = num_requests;
enqueue_params.cache_indir = cache_indir;
enqueue_params.semaphores = multiBlockSemaphores();
enqueue_params.host_past_key_value_lengths = host_past_kv_len_list;
enqueue_params.mrope_position_deltas = mrope_position_deltas;
if (mIsSpecDecodingEnabled && mUseSpecDecoding)
{
enqueue_params.spec_decoding_packed_mask = spec_decoding_packed_mask;
enqueue_params.spec_decoding_position_offsets = spec_decoding_position_offsets;
enqueue_params.spec_decoding_generation_lengths = spec_decoding_generation_lengths;
enqueue_params.spec_decoding_is_generation_length_variable = mSpecDecodingIsGenerationLengthVariable;
enqueue_params.spec_decoding_max_generation_length = mSpecDecodingMaxGenerationLength;
}
if (mFuseFp4Quant)
{
enqueue_params.start_token_idx_sf = tokenIdxBeg;
}
if (changeSpecDecodingMode)
{
// mUseSpecDecoding is changed, need to re-prepare the DecoderXQARunner
prepareEnqueueGeneration<T, KVCacheBuffer>(enqueue_params);
}
enqueueGeneration<T, KVCacheBuffer>(enqueue_params, stream);
{
std::string const afterGenStr = "gen attention at layer " + std::to_string(mLayerIdx);
{
TLLM_CHECK_DEBUG_WITH_INFO(
tensorrt_llm::runtime::utils::tensorHasInvalid(localNbTokens,
outputDesc[0].dims.d[getPackedTensorHiddenDimIndex(mRemovePadding)],
mFP8ContextFMHA ? nvinfer1::DataType::kFP8 : mType, context_buf_, stream, afterGenStr)
== false,
"Found invalid number (NaN or Inf) in " + afterGenStr);
}
}
}
return 0;
}
template <typename T, typename AttentionOutT>
int GPTAttentionPlugin::enqueueDispatchKVCacheType(nvinfer1::PluginTensorDesc const* inputDesc,
nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream)
{
if (mPagedKVCache)
{
return enqueueImpl<T, AttentionOutT, KVBlockArray>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
else
{
return enqueueImpl<T, AttentionOutT, KVLinearBuffer>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
return 0;
}
int GPTAttentionPlugin::enqueue(nvinfer1::PluginTensorDesc const* inputDesc,
nvinfer1::PluginTensorDesc const* outputDesc, void const* const* inputs, void* const* outputs, void* workspace,
cudaStream_t stream) noexcept
{
if (isBuilding())
{
return 0;
}
if (mSkipAttn)
{
bool const* SKIP_ATTN = reinterpret_cast<bool const*>(inputs[getIdx(IdxEntry::SKIP_ATTN)]);
if (SKIP_ATTN[0])
{
return 0;
}
}
if (mType == nvinfer1::DataType::kHALF)
{
if (mFuseFp4Quant)
{
return enqueueDispatchKVCacheType<half, uint8_t>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#ifdef ENABLE_FP8
if (mFP8ContextFMHA)
{
return enqueueDispatchKVCacheType<half, __nv_fp8_e4m3>(
inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#endif
return enqueueDispatchKVCacheType<half>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
else if (mType == nvinfer1::DataType::kFLOAT)
{
return enqueueDispatchKVCacheType<float>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#ifdef ENABLE_BF16
else if (mType == nvinfer1::DataType::kBF16)
{
if (mFuseFp4Quant)
{
return enqueueDispatchKVCacheType<__nv_bfloat16, uint8_t>(
inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#ifdef ENABLE_FP8
if (mFP8ContextFMHA)
{
return enqueueDispatchKVCacheType<__nv_bfloat16, __nv_fp8_e4m3>(
inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#endif
return enqueueDispatchKVCacheType<__nv_bfloat16>(inputDesc, outputDesc, inputs, outputs, workspace, stream);
}
#endif
return 0;
}
// IPluginV2Ext Methods
nvinfer1::DataType GPTAttentionPlugin::getOutputDataType(
int index, nvinfer1::DataType const* inputTypes, int nbInputs) const noexcept
{
if (mFuseFp4Quant)
{
TLLM_CHECK(index == 0 || index == 1 || (!mPagedKVCache && useKVCache() && index == 2));
}
else
{
TLLM_CHECK(index == 0 || (!mPagedKVCache && useKVCache() && index == 1));
}
if (index == 0)
{
if (mFuseFp4Quant)
{
return nvinfer1::DataType::kFP4;
}
return mFP8ContextFMHA && mEnableContextFMHA ? nvinfer1::DataType::kFP8
: inputTypes[getIdx(IdxEntry::QKV_TENSOR)];
}
if (mFuseFp4Quant && index == 1)
{
return nvinfer1::DataType::kFP8;
}
return inputTypes[getIdx(IdxEntry::PAST_KEY_VALUE)];
}
// IPluginV2 Methods
char const* GPTAttentionPlugin::getPluginType() const noexcept
{
return GPT_ATTENTION_PLUGIN_NAME;
}
char const* GPTAttentionPlugin::getPluginVersion() const noexcept
{
return GPT_ATTENTION_PLUGIN_VERSION;
}
int GPTAttentionPlugin::getNbOutputs() const noexcept
{
int nbOutputs = mFuseFp4Quant ? 2 : 1;
if (!mPagedKVCache && useKVCache())
{
nbOutputs += 1;
}
return nbOutputs;
}
size_t GPTAttentionPlugin::getSerializationSize() const noexcept
{
return GPTAttentionPluginCommon::getCommonSerializationSize();
}
void GPTAttentionPlugin::serialize(void* buffer) const noexcept
{
GPTAttentionPluginCommon::serializeCommon(buffer);
}
///////////////
GPTAttentionPluginCreator::GPTAttentionPluginCreator()
: GPTAttentionPluginCreatorCommon()
{
}
char const* GPTAttentionPluginCreator::getPluginName() const noexcept
{
return GPT_ATTENTION_PLUGIN_NAME;
}
char const* GPTAttentionPluginCreator::getPluginVersion() const noexcept
{
return GPT_ATTENTION_PLUGIN_VERSION;
}
PluginFieldCollection const* GPTAttentionPluginCreator::getFieldNames() noexcept
{
return &mFC;
}
IPluginV2* GPTAttentionPluginCreator::createPlugin(char const* name, PluginFieldCollection const* fc) noexcept
{
PluginFieldParser p{fc->nbFields, fc->fields};
try
{
auto* obj = new GPTAttentionPlugin(p.getScalar<int32_t>("layer_idx").value(),
p.getScalar<int32_t>("num_heads").value(), p.getScalar<int32_t>("vision_start").value(),
p.getScalar<int32_t>("vision_length").value(), p.getScalar<int32_t>("num_kv_heads").value(),
p.getScalar<int32_t>("num_kv_heads_origin").value(), p.getScalar<int32_t>("head_size").value(),
p.getScalar<int32_t>("unidirectional").value(), p.getScalar<float>("q_scaling").value(),
p.getScalar<float>("attn_logit_softcapping_scale").value(),
static_cast<PositionEmbeddingType>(p.getScalar<int8_t>("position_embedding_type").value()),
p.getScalar<int32_t>("rotary_embedding_dim").value(), p.getScalar<float>("rotary_embedding_base").value(),
static_cast<RotaryScalingType>(p.getScalar<int8_t>("rotary_embedding_scale_type").value()),
p.getScalar<float>("rotary_embedding_scale").value(),
p.getScalar<float>("rotary_embedding_short_m_scale").value(),
p.getScalar<float>("rotary_embedding_long_m_scale").value(),
p.getScalar<int32_t>("rotary_embedding_max_positions").value(),
p.getScalar<int32_t>("rotary_embedding_original_max_positions").value(),
static_cast<int32_t>(p.getScalar<int32_t>("tp_size").value()),
static_cast<int32_t>(p.getScalar<int32_t>("tp_rank").value()),
static_cast<bool>(p.getScalar<int8_t>("unfuse_qkv_gemm").value()),
static_cast<bool>(p.getScalar<int8_t>("use_logn_scaling").value()),
static_cast<ContextFMHAType>(p.getScalar<int8_t>("context_fmha_type").value()),
p.getScalar<int32_t>("kv_cache_quant_mode").value(),
static_cast<bool>(p.getScalar<int8_t>("remove_input_padding").value()),
static_cast<AttentionMaskType>(p.getScalar<int32_t>("mask_type").value()),
BlockSparseParams{p.getScalar<int32_t>("block_sparse_block_size").value(),
static_cast<bool>(p.getScalar<int8_t>("block_sparse_homo_head_pattern").value()),
p.getScalar<int32_t>("block_sparse_num_local_blocks").value(),
p.getScalar<int32_t>("block_sparse_vertical_stride").value()},
static_cast<bool>(p.getScalar<int32_t>("paged_kv_cache").value()),
p.getScalar<int32_t>("tokens_per_block").value(),
static_cast<nvinfer1::DataType>(p.getScalar<int32_t>("type_id").value()),
p.getScalar<int32_t>("max_context_length").value(),
static_cast<bool>(p.getScalar<int8_t>("qkv_bias_enabled").value()),
static_cast<bool>(p.getScalar<int8_t>("do_cross_attention").value()),
static_cast<int32_t>(p.getScalar<int32_t>("max_distance").value()),
static_cast<bool>(p.getScalar<int8_t>("pos_shift_enabled").value()),
static_cast<bool>(p.getScalar<int8_t>("dense_context_fmha").value()),
static_cast<bool>(p.getScalar<int8_t>("use_paged_context_fmha").value()),
static_cast<bool>(p.getScalar<int8_t>("use_fp8_context_fmha").value()),
static_cast<bool>(p.getScalar<int8_t>("has_full_attention_mask").value()),
static_cast<bool>(p.getScalar<int32_t>("use_cache").value()),
static_cast<bool>(p.getScalar<int8_t>("is_spec_decoding_enabled").value()),
static_cast<bool>(p.getScalar<int8_t>("spec_decoding_is_generation_length_variable").value()),
p.getScalar<int32_t>("spec_decoding_max_generation_length").value(),
static_cast<int8_t>(p.getScalar<int8_t>("is_mla_enabled").value()),
static_cast<int32_t>(p.getScalar<int32_t>("q_lora_rank").value()),
static_cast<int32_t>(p.getScalar<int32_t>("kv_lora_rank").value()),
static_cast<int32_t>(p.getScalar<int32_t>("qk_nope_head_dim").value()),
static_cast<int32_t>(p.getScalar<int32_t>("qk_rope_head_dim").value()),
static_cast<int32_t>(p.getScalar<int32_t>("v_head_dim").value()),
static_cast<bool>(p.getScalar<int8_t>("fuse_fp4_quant").value()),
static_cast<bool>(p.getScalar<int8_t>("skip_attn").value()),
static_cast<int32_t>(p.getScalar<int32_t>("cp_size").value()),
static_cast<int32_t>(p.getScalar<int32_t>("cp_rank").value()),
static_cast<std::set<int32_t>>(p.getSet<int32_t>("cp_group").value()));
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
catch (std::exception const& e)
{
caughtError(e);
}
return nullptr;
}
IPluginV2* GPTAttentionPluginCreator::deserializePlugin(
char const* name, void const* serialData, size_t serialLength) noexcept
{
// This object will be deleted when the network is destroyed, which will
// call GPTAttentionPlugin::destroy()
try
{
auto* obj = new GPTAttentionPlugin(serialData, serialLength);
obj->setPluginNamespace(mNamespace.c_str());
return obj;
}
catch (std::exception const& e)
{
caughtError(e);
}
return nullptr;
}
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