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[legacy] clean up legacy code (#4743)
* [legacy] remove outdated codes of pipeline (#4692) * [legacy] remove cli of benchmark and update optim (#4690) * [legacy] remove cli of benchmark and update optim * [doc] fix cli doc test * [legacy] fix engine clip grad norm * [legacy] remove outdated colo tensor (#4694) * [legacy] remove outdated colo tensor * [test] fix test import * [legacy] move outdated zero to legacy (#4696) * [legacy] clean up utils (#4700) * [legacy] clean up utils * [example] update examples * [legacy] clean up amp * [legacy] fix amp module * [legacy] clean up gpc (#4742) * [legacy] clean up context * [legacy] clean core, constants and global vars * [legacy] refactor initialize * [example] fix examples ci * [example] fix examples ci * [legacy] fix tests * [example] fix gpt example * [example] fix examples ci * [devops] fix ci installation * [example] fix examples ci
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@@ -30,24 +30,4 @@ This command will inform you information regarding the version compatibility and
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To launch distributed jobs on single or multiple nodes, the command `colossalai run` can be used for process launching.
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You may refer to [Launch Colossal-AI](./launch_colossalai.md) for more details.
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## Tensor Parallel Micro-Benchmarking
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As Colossal-AI provides an array of tensor parallelism methods, it is not intuitive to choose one for your hardware and
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model. Therefore, we provide a simple benchmarking to evaluate the performance of various tensor parallelisms on your system.
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This benchmarking is run on a simple MLP model where the input data is of the shape `(batch_size, seq_length, hidden_size)`.
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Based on the number of GPUs, the CLI will look for all possible tensor parallel configurations and display the benchmarking results.
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You can customize the benchmarking configurations by checking out `colossalai benchmark --help`.
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```shell
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# run on 4 GPUs
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colossalai benchmark --gpus 4
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# run on 8 GPUs
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colossalai benchmark --gpus 8
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```
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:::caution
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Only single-node benchmarking is supported currently.
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:::
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<!-- doc-test-command: echo -->
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