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example/ck_tile/16_batched_gemm/README.md
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# Batched GEMM with CK Tile
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This example demonstrates batched matrix multiplication (Batched GEMM) using the CK Tile programming model, enabling efficient parallel computation of multiple independent GEMMs in a single kernel launch.
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---
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## Algorithm and Math
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Given:
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- $A$: $[\text{batch}, M, K]$
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- $B$: $[\text{batch}, K, N]$
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- $C$: $[\text{batch}, M, N]$
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For each batch $b$:
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$$
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C^{(b)} = A^{(b)} \times B^{(b)}
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$$
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- **Tilewise Batched GEMM**: Each thread block processes a tile of $C$ for a specific batch, loading corresponding tiles from $A$ and $B$, performing blockwise matrix multiply-accumulate, and writing results.
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---
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## Tile Programming Model
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- **Tiles**: Each thread block processes a tile of $C$ for a given batch.
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- **Pipeline**: Modular, supports different memory/computation pipelines.
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---
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## Features
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- **Flexible Layouts**: Supports row/column-major and custom strides for $A$, $B$, $C$.
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- **Batching**: Efficiently computes multiple GEMMs in parallel.
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- **Precision**: Supports fp16, bf16, fp8, bf8.
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- **Validation**: CPU/GPU validation and error tolerance options.
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---
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## Build & Run
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```bash
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mkdir build && cd build
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# you can replace <arch> with the appropriate architecture (for example gfx90a or gfx942) or leave it blank
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../script/cmake-ck-dev.sh ../ <arch>
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make tile_example_batched_gemm -j
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```
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This will result in an executable `build/bin/tile_example_batched_gemm`
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### Arguments
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```bash
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args:
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-m m dimension (default:512)
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-n n dimension (default:1024)
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-k k dimension (default:2048)
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-stride_a Tensor A stride (default:0)
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-stride_b Tensor B stride (default:0)
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-stride_c Tensor C stride (default:0)
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-a_layout A tensor data layout - Row by default (default:R)
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-b_layout B tensor data layout - Row by default (default:C)
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-c_layout C tensor data layout - Row by default (default:R)
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-batch_stride_a Batch A stride (default:1048576)
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-batch_stride_b Batch B stride (default:2097152)
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-batch_stride_c Batch C stride (default:524288)
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-batch_count Batch count (default:8)
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-v 0. No validation, 1. Validation on CPU, 2. Validation on GPU (default:2)
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-prec data type. fp16/bf16/fp8/bf8 (default:fp16)
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-warmup number of iterations before benchmark the kernel (default:50)
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-repeat number of iterations to benchmark the kernel (default:100)
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-timer gpu:gpu timer, cpu:cpu timer (default:gpu)
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-split_k splitK value (default:1)
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-json 0: No Json, 1: Dump Results in Json format (default:0)
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-jsonfile json file name to dump results (default:cktile_batched_gemm.json)
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```
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---
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## Source Structure
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- **Kernel**: [`batched_gemm.hpp`](batched_gemm.hpp) (tile-programming kernel template)
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- **Executable**: [`batched_gemm.cpp`](batched_gemm.cpp)
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- **Build**: `CMakeLists.txt`, `run_batched_gemm_example.inc`
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---
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## Related CK Tile Examples
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- [03_gemm](../03_gemm/README.md): Single GEMM with tiles
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- [15_fused_moe](../15_fused_moe/README.md): Fused MoE block (uses group/batched GEMM)
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- [13_moe_sorting](../13_moe_sorting/README.md): MoE sorting for expert dispatch
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For distribution, [`include/ck_tile/tile_program/tile_distribution/`](../../../include/ck_tile/tile_program/tile_distribution/).
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---
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[Back to CK Tile Examples](../README.md)
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