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[https://nvbugs/5501557][fix] Fix nemotron build error #7644

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[https://nvbugs/5501557][fix] Fix nemotron build error #7644

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4 changes: 2 additions & 2 deletions cpp/tensorrt_llm/batch_manager/trtGptModelInflightBatching.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -272,8 +272,8 @@ TrtGptModelInflightBatching::TrtGptModelInflightBatching(std::shared_ptr<nvinfer
auto [numKvHeadsPerLayerBegin, numKvHeadsPerLayerEnd] = modelConfig.getNumKvHeadsPerLayerLocalRange(
worldConfig.getPipelineParallelism(), worldConfig.getPipelineParallelRank(), isCrossAttention);
auto numKvHeadsPerLayer = std::vector<SizeType32>(numKvHeadsPerLayerBegin, numKvHeadsPerLayerEnd);
auto windowSizeLayers
= BaseKVCacheManager::groupLayersByWindowSize(maxAttentionWindowVec, modelConfig.getNbLayers());
auto const numLayers = static_cast<SizeType32>(numKvHeadsPerLayer.size());
auto const windowSizeLayers = KVCacheManager::groupLayersByWindowSize(maxAttentionWindowVec, numLayers);
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@brb-nv brb-nv Sep 9, 2025 •
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Here's my issue with groupLayersByWindowSize: It doesn't take PP into account and leads to incorrect memory computation.
https://github.com/NVIDIA/TensorRT-LLM/blob/main/cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp#L2177-L2192

Let's take an example of a model with 4 layers and maxAttentionWindowVec = {2, 3, 4, 5}

Case1: PP=1
ppRank 0; layers = {l0, l1, l2, l3}
Input to groupLayersByWindowSize: maxAttentionWindowVec={2, 3, 4, 5}, numLayers=4.
Output from groupLayersByWindowSize: {2:{0}, 3:{1}, 4:{2}, 5{3}}.

Case1: PP=2
ppRank 0; layers = {l0, l1}
Input to groupLayersByWindowSize: maxAttentionWindowVec={2, 3, 4, 5}, numLayers=2.
Output from groupLayersByWindowSize: {2:{0}, 3:{1}}.
ppRank 1; layers = {l2, l3}
Input to groupLayersByWindowSize: maxAttentionWindowVec={2, 3, 4, 5}, numLayers=2.
Output from groupLayersByWindowSize: {2:{0}, 3:{1}}. // THIS SHOULD'VE BEEN {4{0}, 5{1}} WHERE 0,1 ARE LOCAL LAYER IDS.

Problem is it's just taking numLayers as input. Instead it should take a layer range (where layerIds are global) to get the math right. [OR] maxAttentionWindowVec must be different for each PP rank.

Please let me know if I'm missing something.

std::map<SizeType32, SizeType32> cacheSizeBytesPerTokenPerWindow;
for (auto const& [windowSize, managedLayers] : windowSizeLayers)
{
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@brb-nv brb-nv Sep 9, 2025 •
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There's a similar issue with calculateCacheSizePerTokenForSingleWindowSize which further calls getNumKvForGivenLayers.

To my understanding, getNumKvForGivenLayers expects global layer ids. Currently, we pass in local layer ids when using PP.

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