b78588d163
* CUTLASS 3.7 * clean up changelog --------- Co-authored-by: yuzhai <yuzhai@nvidia.com> Co-authored-by: Haicheng Wu <haichengw@nvidia.com>
65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
# Distributed GEMM
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This example implements Tensor Parallel GEMMs for the Hopper architecture with the experimental
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[Distributed GEMM](../../include/cutlass/experimental/distributed) API in CUTLASS.
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This example requires Hopper GPUs with an any-to-any NVLink network.
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Please refer to [REQUIREMENTS.md](REQUIREMENTS.md) for more information.
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By default, the example assumes 8 GPUs (TP=8) and runs an All Gather + GEMM operation, which rotates
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operand A. To run with a different number of GPUs or schedule, please refer to
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[65_distributed_gemm.cu](65_distributed_gemm.cu).
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## Getting started
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Command line arguments are mostly similar to other examples:
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```
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--m=<int> Sets the M extent of the GEMM
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--n=<int> Sets the N extent of the GEMM
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--k=<int> Sets the K extent of the GEMM
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--l=<int> Sets the L extent (batch) of the GEMM (default: 1)
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--alpha=<f32> Epilogue scalar alpha (default: 1.0)
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--beta=<f32> Epilogue scalar beta (default: 0.0)
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--iterations=<int> Number of profiling iterations to perform (default: 100)
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--warmup-iterations=<int> Number of warmup iterations prior to profiling (default: 10)
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--eps=<f32> Threshold for error compared to reference GEMM (default: 0.0)
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```
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Sample run command:
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```bash
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./65_distributed_gemm --m=16384 --n=106496 --k=16384 --warmup-iterations=10 --iterations=100
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```
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This executes a GEMM with shape `<16384, 106496, 16384>`, and reports average runtime
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over 100 iterations, with 10 warmup iterations.
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A reference check with respect to a single-device GEMM is also performed by default.
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## Trying out other schedules
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Schedules that are currently supported are:
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* All Gather + GEMM:
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* `AllGather1D_TilingCD_RotatingA`
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* `AllGather1D_TilingCD_RotatingB`
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* GEMM + Reduce Scatter:
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* `ReduceScatter1D_TilingA_RotatingC`
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* `ReduceScatter1D_TilingB_RotatingC`
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To try out different schedules, simply change this line in the example, and set your desired
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schedule:
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```cpp
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using DistSchedule = cutlass::distributed::schedules::AllGather1D_TilingCD_RotatingA<TP>;
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```
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If you're interesting it trying out other TP values (run on a different number of GPUs), the
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procedure is the same, simply modify the following line in the example:
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```cpp
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using TP = _8;
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```
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