Here is a list of all variables with links to the classes they belong to:

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• offset : Mila::Dnn::Serialization::TensorBlobMetadata
• operation_ : Mila::Dnn::Dropout< TDeviceType, TInput, TOutput >, Mila::Dnn::FusedComponent< TDeviceType, TPrecision >, Mila::Dnn::Gelu< TDeviceType, TPrecision >, Mila::Dnn::GroupedQueryAttention< TDeviceType, TComputePrecision, TKvPolicy >, Mila::Dnn::LayerNorm< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TComputePrecision, TWeightQuant >, Mila::Dnn::Lpe< TDeviceType, TIndex, TPrecision >, Mila::Dnn::MultiHeadAttention< TDeviceType, TPrecision >, Mila::Dnn::Residual< TDeviceType, TPrecision >, Mila::Dnn::RmsNorm< TDeviceType, TPrecision >, Mila::Dnn::Rope< TDeviceType, TPrecision >, Mila::Dnn::Softmax< TDeviceType, TPrecision >, Mila::Dnn::SoftmaxCrossEntropy< TDeviceType, TLogits, TTargets, TPrecision >, Mila::Dnn::Swiglu< TDeviceType, TPrecision >, Mila::Dnn::TokenEmbedding< TDeviceType, TIndex, TPrecision >
• optimized_for_transfer : Mila::Dnn::Compute::MemoryResourceTraits< CudaPinnedMemoryResource >
• original_components_ : Mila::Dnn::FusedComponent< TDeviceType, TPrecision >
• out_features_ : Mila::Dnn::Compute::CpuLinearOp, Mila::Dnn::Compute::Cuda::Linear::CudaLinearOp< TComputePrecision, TWeightQuant >
• out_proj_ : Mila::Dnn::GptBlock< TDeviceType, TPrecision >, Mila::Dnn::LlamaBlock< TDeviceType, TPrecision, TWeightQuant, TKvPolicy >
• outer_shape : Mila::Dnn::MultiAxisPartition
• outer_shape_ : Mila::Dnn::RmsNorm< TDeviceType, TPrecision >
• outer_size : Mila::Dnn::AxisPartition, Mila::Dnn::MultiAxisPartition
• outer_size_ : Mila::Dnn::Compute::CpuLayerNormOp, Mila::Dnn::Compute::Cuda::RmsNorm::CudaRmsNormOp< TPrecision >
• output_ : Mila::Dnn::Gelu< TDeviceType, TPrecision >, Mila::Dnn::GroupedQueryAttention< TDeviceType, TComputePrecision, TKvPolicy >, Mila::Dnn::LayerNorm< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TComputePrecision, TWeightQuant >, Mila::Dnn::Lpe< TDeviceType, TIndex, TPrecision >, Mila::Dnn::Residual< TDeviceType, TPrecision >, Mila::Dnn::RmsNorm< TDeviceType, TPrecision >, Mila::Dnn::Swiglu< TDeviceType, TPrecision >, Mila::Dnn::TokenEmbedding< TDeviceType, TIndex, TPrecision >
• output_features_ : Mila::Dnn::LinearConfig
• output_shape_ : Mila::Dnn::GptTransformer< TDeviceType, TPrecision >, Mila::Dnn::LlamaTransformer< TDeviceType, TPrecision, TWeightQuantization, TKvCachePolicy >
• output_state_ : Mila::Dnn::Dropout< TDeviceType, TInput, TOutput >
• output_view_ : Mila::Dnn::Gelu< TDeviceType, TPrecision >, Mila::Dnn::GroupedQueryAttention< TDeviceType, TComputePrecision, TKvPolicy >, Mila::Dnn::LayerNorm< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TComputePrecision, TWeightQuant >, Mila::Dnn::MultiHeadAttention< TDeviceType, TPrecision >, Mila::Dnn::Residual< TDeviceType, TPrecision >, Mila::Dnn::RmsNorm< TDeviceType, TPrecision >, Mila::Dnn::Swiglu< TDeviceType, TPrecision >
• owned_context_ : Mila::Dnn::GptTransformer< TDeviceType, TPrecision >
• owned_decode_output_ : Mila::Dnn::MultiHeadAttention< TDeviceType, TPrecision >
• owned_exec_context_ : Mila::Dnn::Gelu< TDeviceType, TPrecision >, Mila::Dnn::GptBlock< TDeviceType, TPrecision >, Mila::Dnn::LayerNorm< TDeviceType, TPrecision >, Mila::Dnn::Linear< TDeviceType, TComputePrecision, TWeightQuant >, Mila::Dnn::LlamaBlock< TDeviceType, TPrecision, TWeightQuant, TKvPolicy >, Mila::Dnn::Lpe< TDeviceType, TIndex, TPrecision >, Mila::Dnn::MLP< TDeviceType, TPrecision >, Mila::Dnn::Residual< TDeviceType, TPrecision >, Mila::Dnn::RmsNorm< TDeviceType, TPrecision >, Mila::Dnn::Rope< TDeviceType, TPrecision >, Mila::Dnn::Softmax< TDeviceType, TPrecision >, Mila::Dnn::Swiglu< TDeviceType, TPrecision >, Mila::Dnn::TokenEmbedding< TDeviceType, TIndex, TPrecision >
• owned_input_grad_ : Mila::Dnn::MultiHeadAttention< TDeviceType, TPrecision >
• owned_K_grad_ : Mila::Dnn::Rope< TDeviceType, TPrecision >
• owned_output_ : Mila::Dnn::MultiHeadAttention< TDeviceType, TPrecision >
• owned_Q_grad_ : Mila::Dnn::Rope< TDeviceType, TPrecision >
• owns_cache_ : Mila::Dnn::Compute::Cuda::Rope::CudaRopeOp< TComputePrecision >