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[None][chore] No-op changes to support context parallelism in disaggregated serving later #7063

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Aug 21, 2025
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[None][chore] No-op changes to support context parallelism in disaggregated serving later #7063

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32 changes: 20 additions & 12 deletions cpp/include/tensorrt_llm/executor/dataTransceiverState.h
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Original file line number Diff line number Diff line change
Expand Up @@ -52,29 +52,30 @@ class CacheState final
AttentionType attentionType = AttentionType::kDEFAULT, int kvFactor = 2)
: mModelConfig(std::move(modelConfig))
, mParallelConfig{worldConfig.getTensorParallelism(), worldConfig.getPipelineParallelism(),
worldConfig.enableAttentionDP(), worldConfig.getTensorParallelRank(), worldConfig.getTensorParallelism()}
worldConfig.getContextParallelism(), worldConfig.enableAttentionDP(), worldConfig.getTensorParallelRank(),
worldConfig.getTensorParallelism()}
, mDataType{dataType}
, mAttentionConfig(attentionType, kvFactor)
{
}

CacheState(std::vector<SizeType32> nbKvHeadPerLayer, SizeType32 sizePerHead, SizeType32 tokensPerBlock,
SizeType32 tensorParallelism, SizeType32 pipelineParallelism, nvinfer1::DataType dataType,
AttentionType attentionType = AttentionType::kDEFAULT, int kvFactor = 2, bool enableAttentionDP = false,
int DPrank = 0, int DPsize = 0)
SizeType32 tensorParallelism, SizeType32 pipelineParallelism, SizeType32 contextParallelism,
nvinfer1::DataType dataType, AttentionType attentionType = AttentionType::kDEFAULT, int kvFactor = 2,
bool enableAttentionDP = false, int DPrank = 0, int DPsize = 0)
: mModelConfig{std::move(nbKvHeadPerLayer), sizePerHead, tokensPerBlock}
, mParallelConfig{tensorParallelism, pipelineParallelism, enableAttentionDP, DPrank, DPsize}
, mParallelConfig{tensorParallelism, pipelineParallelism, contextParallelism, enableAttentionDP, DPrank, DPsize}
, mDataType{dataType}
, mAttentionConfig(attentionType, kvFactor)
{
}

CacheState(SizeType32 nbAttentionLayers, SizeType32 nbKvHeads, SizeType32 sizePerHead, SizeType32 tokensPerBlock,
SizeType32 tensorParallelism, SizeType32 pipelineParallelism, nvinfer1::DataType dataType,
AttentionType attentionType = AttentionType::kDEFAULT, int kvFactor = 2, bool enableAttentionDP = false,
int DPrank = 0, int DPsize = 0)
SizeType32 tensorParallelism, SizeType32 pipelineParallelism, SizeType32 contextParallelism,
nvinfer1::DataType dataType, AttentionType attentionType = AttentionType::kDEFAULT, int kvFactor = 2,
bool enableAttentionDP = false, int DPrank = 0, int DPsize = 0)
: mModelConfig{std::vector(nbAttentionLayers, nbKvHeads), sizePerHead, tokensPerBlock}
, mParallelConfig{tensorParallelism, pipelineParallelism, enableAttentionDP, DPrank, DPsize}
, mParallelConfig{tensorParallelism, pipelineParallelism, contextParallelism, enableAttentionDP, DPrank, DPsize}
, mDataType{dataType}
, mAttentionConfig(attentionType, kvFactor)
{
Expand All @@ -83,7 +84,7 @@ class CacheState final
[[nodiscard]] bool operator==(kv_cache::CacheState const& other) const noexcept
{
return mModelConfig == other.mModelConfig && mParallelConfig == other.mParallelConfig
&& mDataType == other.mDataType;
&& mAttentionConfig == other.mAttentionConfig && mDataType == other.mDataType;
}

struct ModelConfig
Expand All @@ -103,15 +104,16 @@ class CacheState final
{
SizeType32 mTensorParallelism;
SizeType32 mPipelineParallelism;
SizeType32 mContextParallelism;
bool mEnableAttentionDP;
SizeType32 mDPrank;
SizeType32 mDPsize;

[[nodiscard]] bool operator==(ParallelConfig const& other) const noexcept
{
return mTensorParallelism == other.mTensorParallelism && mPipelineParallelism == other.mPipelineParallelism
&& mEnableAttentionDP == other.mEnableAttentionDP && mDPrank == other.mDPrank
&& mDPsize == other.mDPsize;
&& mContextParallelism == other.mContextParallelism && mEnableAttentionDP == other.mEnableAttentionDP
&& mDPrank == other.mDPrank && mDPsize == other.mDPsize;
}
};

Expand All @@ -125,6 +127,11 @@ class CacheState final
{
}

[[nodiscard]] bool operator==(AttentionConfig const& other) const noexcept
{
return mAttentionType == other.mAttentionType && mKvFactor == other.mKvFactor;
}

// attentionType ;
AttentionType mAttentionType;
int mKvFactor;
Expand Down Expand Up @@ -162,6 +169,7 @@ class CacheState final
sstring << "mTokensPerBlock:" << mModelConfig.mTokensPerBlock << "\n";
sstring << "tp:" << mParallelConfig.mTensorParallelism << "\n";
sstring << "pp:" << mParallelConfig.mPipelineParallelism << "\n";
sstring << "cp:" << mParallelConfig.mContextParallelism << "\n";
sstring << "enableAttentionDP:" << mParallelConfig.mEnableAttentionDP << "\n";
sstring << "datatype:" << static_cast<int32_t>(mDataType) << "\n";
sstring << "attentionType:" << static_cast<int32_t>(mAttentionConfig.mAttentionType) << "\n";
Expand Down
8 changes: 8 additions & 0 deletions cpp/tensorrt_llm/batch_manager/cacheFormatter.cpp
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Expand Up @@ -822,6 +822,14 @@ void CacheFormatter::unformat(TransferSession& session)
TLLM_LOG_WARNING("CacheFormatter::inquireSupport: only support non-MLA");
return false;
}
if (selfConfig.getParallelConfig().mContextParallelism != 1
|| destConfig.getParallelConfig().mContextParallelism != 1)
{
TLLM_LOG_WARNING(
"CacheFormatter::inquireSupport: context parallelism is not currently supported (selfCP=%d, destCP=%d).",
selfConfig.getParallelConfig().mContextParallelism, destConfig.getParallelConfig().mContextParallelism);
return false;
}

std::unordered_set<int> setVecDest{
destConfig.getModelConfig().mNbKvHeadsPerLayer.begin(), destConfig.getModelConfig().mNbKvHeadsPerLayer.end()};
Expand Down
14 changes: 8 additions & 6 deletions cpp/tensorrt_llm/batch_manager/mlaCacheFormatter.cpp
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Expand Up @@ -558,18 +558,20 @@ void MLACacheFormatter::unformat(TransferSession& session)
TLLM_LOG_WARNING("MLACacheFormatter::inquireSupport: only support MLA");
return false;
}

if (selfConfig.getAttentionConfig().mKvFactor != destConfig.getAttentionConfig().mKvFactor)
{
TLLM_LOG_WARNING("MLACacheFormatter::inquireSupport: only support same kv factor");
return false;
}
if (selfConfig.getParallelConfig().mEnableAttentionDP
&& (selfConfig.getParallelConfig().mTensorParallelism % selfConfig.getParallelConfig().mDPsize != 0))
{
TLLM_LOG_WARNING("MLACacheFormatter::inquireSupport: TP size must be divisible by DP size");
return false;
}
if (selfConfig.getParallelConfig().mContextParallelism != 1
|| destConfig.getParallelConfig().mContextParallelism != 1)
{
TLLM_LOG_WARNING(
"MLACacheFormatter::inquireSupport: context parallelism is not currently supported (selfCP=%d, destCP=%d).",
selfConfig.getParallelConfig().mContextParallelism, destConfig.getParallelConfig().mContextParallelism);
return false;
}
if (destConfig.getParallelConfig().mEnableAttentionDP
&& (destConfig.getParallelConfig().mTensorParallelism % destConfig.getParallelConfig().mDPsize != 0))
{
Expand Down
7 changes: 5 additions & 2 deletions cpp/tensorrt_llm/executor/serialization.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -531,14 +531,15 @@ kv_cache::CacheState Serialization::deserializeCacheState(std::istream& is)
auto tokensPerBlock = su::deserialize<decltype(CacheState::ModelConfig::mTokensPerBlock)>(is);
auto tensorParallelism = su::deserialize<decltype(CacheState::ParallelConfig::mTensorParallelism)>(is);
auto pipelineParallelism = su::deserialize<decltype(CacheState::ParallelConfig::mPipelineParallelism)>(is);
auto contextParallelism = su::deserialize<decltype(CacheState::ParallelConfig::mContextParallelism)>(is);
auto enableAttentionDP = su::deserialize<decltype(CacheState::ParallelConfig::mEnableAttentionDP)>(is);
auto DPrank = su::deserialize<decltype(CacheState::ParallelConfig::mDPrank)>(is);
auto DPsize = su::deserialize<decltype(CacheState::ParallelConfig::mDPsize)>(is);
auto dataType = su::deserialize<decltype(CacheState::mDataType)>(is);
auto attentionType = su::deserialize<decltype(CacheState::AttentionConfig::mAttentionType)>(is);
auto kvFactor = su::deserialize<decltype(CacheState::AttentionConfig::mKvFactor)>(is);
return CacheState{nbKvHeadsPerLayer, sizePerHead, tokensPerBlock, tensorParallelism, pipelineParallelism, dataType,
attentionType, kvFactor, enableAttentionDP, DPrank, DPsize};
return CacheState{nbKvHeadsPerLayer, sizePerHead, tokensPerBlock, tensorParallelism, pipelineParallelism,
contextParallelism, dataType, attentionType, kvFactor, enableAttentionDP, DPrank, DPsize};
}

void Serialization::serialize(kv_cache::CacheState const& state, std::ostream& os)
Expand All @@ -548,6 +549,7 @@ void Serialization::serialize(kv_cache::CacheState const& state, std::ostream& o
su::serialize(state.mModelConfig.mTokensPerBlock, os);
su::serialize(state.mParallelConfig.mTensorParallelism, os);
su::serialize(state.mParallelConfig.mPipelineParallelism, os);
su::serialize(state.mParallelConfig.mContextParallelism, os);
su::serialize(state.mParallelConfig.mEnableAttentionDP, os);
su::serialize(state.mParallelConfig.mDPrank, os);
su::serialize(state.mParallelConfig.mDPsize, os);
Expand All @@ -564,6 +566,7 @@ size_t Serialization::serializedSize(kv_cache::CacheState const& state)
totalSize += su::serializedSize(state.mModelConfig.mTokensPerBlock);
totalSize += su::serializedSize(state.mParallelConfig.mTensorParallelism);
totalSize += su::serializedSize(state.mParallelConfig.mPipelineParallelism);
totalSize += su::serializedSize(state.mParallelConfig.mContextParallelism);
totalSize += su::serializedSize(state.mParallelConfig.mEnableAttentionDP);
totalSize += su::serializedSize(state.mParallelConfig.mDPrank);
totalSize += su::serializedSize(state.mParallelConfig.mDPsize);
Expand Down
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