Tensor Parallelism All-Reduce Communication Cost
Infrastructure, Parallelism & Hardware Efficiency DS practice problem on Onlearn.
Difficulty: medium.
Topics: Tensor Parallelism All-Reduce Communication Cost, Ring All-Reduce Algorithm, NVLink Bandwidth Utilization, Communication-to-Computation Ratio, Tensor Partitioning Schemes, Collective Latency Modeling, Distributed Systems, Parallel Computing, Computer Architecture, Network Topology, Performance Engineering, Collective Communication, Interconnect Interfacing, Memory Hierarchy Management, Parallel Strategy Optimization, Hardware Resource Scheduling.
When training or serving large neural networks with tensor parallelism , individual layers (such as linear projections in transformers) are split across multiple GPUs. After each GPU computes its partial result, an all reduce collective communication operation is required to synchronize and combine the partial outputs across all participating GPUs. Understanding the communication cost of these all reduce operations is critical for performance analysis, since communication overhead can become the bottleneck in multi GPU systems. Implement a function tensor parallel allreduce cost that computes the communication cost analysis for tensor parallel training/inference of a transformer model. Inputs: hidden size: The hidden dimension of the model (int) sequence length: The sequence length (int) batch size: Micro batch size per GPU (int) num gpus: Number of GPUs in the tensor parallel group (int) num layers: Number of transformer layers (int) bytes per element: Bytes per parameter/activation element, e.g. 2 for FP16 (int, default 2) bandwidth gb per sec: Inter GPU bandwidth in GB/s (float, default 300.0) allreduces per layer: Number of all reduce operations per transformer layer in a single forward pass (int, default 2) Output: A dictionary with: 'message size bytes': Size in bytes of the activation tensor being all reduced (int) 'comm volume per allreduce bytes': Communication volume per GPU for a single all reduce using the ring algorithm (float, rounded to 4 decimals) 'total comm volume bytes': Total communication volume across all layers (float, rounded to 4 decimals) 'total comm time ms': Total communication time in milliseconds (float, rounded to 4 decimals) The activation tensor that gets all reduced has shape (batch size, sequence length, hidden size). The ring all reduce algorithm is a standard approach where each GPU sends and receives data in a ring topology, and you should compute the per GPU communication volume accordingly. Note: When there is only 1 GPU, no communication is needed.