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Large Model usage with minGPT

This tutorial provides example training scripts to demonstrate different DeepSpeed optimization technologies on HPU. This tutorial will focus on the memory optimization technologies, including Zero Redundancy Optimizer(ZeRO) and Activation Checkpointing.

Example Overview

The PyTorch minGPT example is based on the source code forked from GitHub repository minGPT.

Setup

Please follow the instructions provided in the Gaudi Installation Guide to set up the environment including the $PYTHON environment variable. The guide will walk you through the process of setting up your system to run the model on Gaudi.

Clone Habana Model-References

In the docker container, clone this repository and switch to the branch that matches your SynapseAI version. You can run the hl-smi utility to determine the SynapseAI version.

git clone https://github.com/HabanaAI/Gaudi-tutorials /path/to/Gaudi-tutorials
cd Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed/

Install Habana DeepSpeed

Please follow the instructions provided in the Gaudi DeepSpeed User Guide to install the DeepSpeed on Gaudi.

pip install git+https://github.com/HabanaAI/[email protected]

Memory Consumptions Under Different DeepSpeed Technologies

Preparations

  1. Make sure there are available Gaudi devices (HPUs). In this tutorial we use 8 Gaudi devices.
  2. To demonstrate the memory, add –dump-memory in the command line.
  3. To limit the training steps (e.g. 4 steps), add –steps 4 in the command line.

Run minGPT with different DeepSpeed technologies

  1. Create a big model instead of the default gpt-nano model. This makes the memory variation more obvious during different phases.

Change the model type from gpt-nano to gpt2

--- a/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed/demo_ds.py
+++ b/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed/demo_ds.py
@@ -146,7 +146,7 @@ for a, b in zip(x,y):
 from mingpt.model import GPT

 model_config = GPT.get_default_config()
-model_config.model_type = 'gpt-nano'
+model_config.model_type = 'gpt2'
 model_config.vocab_size = train_dataset.get_vocab_size()
 model_config.block_size = train_dataset.get_block_size()
  1. Run minGPT with DeepSpeed ZeRO0
cd /path/to/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config.json --use_hpu --steps 4 --dump-memory

The memory consumption on different training phases and the max memory consumption will look like below (in MB):

Step Before forward (M) After forward (M) Before backward (M) After backward (M) Before step (M) After step (M) Max memory (M)
0 328 328 328 1726(max 1735) 1726 1402 1735
1 1726(max 2700) 1726 1726 2051(max 2384) 2051 2051 2700
2 2051 2051 2051 2051(max 2384) 2051 1726 2384
3 1726 1726 1726 2051(max 2384) 2051 1726 2384
  1. Run minGPT with DeepSpeed ZeRO1
cd /path/to/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config_zero1.json --use_hpu --steps 4 --dump-memory

The memory consumption on different training phases and the max memory consumption will look like below (in MB):

Step Before forward (M) After forward (M) Before backward (M) After backward (M) Before step (M) After step (M) Max memory (M)
0 166 166 166 830(max 1056) 830 835 1056
1 672 672 672 695(max 997) 695 672(max 857) 997
2 672 672 672 695(max 997) 695 672(max 857) 997
3 672 672 672 695(max 997) 695 672(max 857) 997
  1. Run minGPT with DeepSpeed ZeRO1 and Activation Checkpoiting
cd /path/to/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config_zero1_ac.json --use_hpu --steps 4 --dump-memory --activation-checkpoint

The memory consumption on different training phases and the max memory consumption will look like below (in MB):

Step Before forward (M) After forward (M) Before backward (M) After backward (M) Before step (M) After step (M) Max memory (M)
0 166 166 166 581(max 758) 581 423(max 586) 758
1 423 423 423 446(max 755) 446 423(max 608) 755
2 423 423 423 446(max 758) 446 423(max 608) 758
3 423 423 423 446(max 758) 446 423(max 608) 758
  1. Run minGPT with DeepSpeed ZeRO2
cd /path/to/Gaudi-tutorials/PyTorch/Large_Model_DeepSpeed
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config_zero2.json --use_hpu --steps 4 --dump-memory

The memory consumption on different training phases and the max memory consumption will look like below (in MB):

Step Before forward (M) After forward (M) Before backward (M) After backward (M) Before step (M) After step (M) Max memory (M)
0 166 166 166 660(max 993) 660 682 993
1 520 520 520 663(max 935) 663 523(max 708) 935
2 523 523 523 568(max 935) 568 523(max 708) 935
3 523 523 523 568(max 935) 568 523(max 708) 935

Conclusions:

  1. Zero0 (basically the default DDP) takes biggest memory
  2. Zero1 & 2 takes less memory than Zero0
  3. With Activation Checkpointing, memory decreases even more.

Use ZeRO to solve the Out-Of-Memory issue

Due to the limited memory on HPU device, it may fail to run a big model on HPU with default configuration (e.g. ZeRO0)

  1. Create a very big model with minGPT

Change the model type from gpt-nano to gpt2-xl

--- a/PyTorch/examples/DeepSpeed/minGPT/demo_ds.py
+++ b/PyTorch/examples/DeepSpeed/minGPT/demo_ds.py
@@ -146,7 +146,7 @@ for a, b in zip(x,y):
 from mingpt.model import GPT

 model_config = GPT.get_default_config()
-model_config.model_type = 'gpt-nano'
+model_config.model_type = 'gpt2-xl'
 model_config.vocab_size = train_dataset.get_vocab_size()
 model_config.block_size = train_dataset.get_block_size()
  1. Run minGPT with DeepSpeed ZeRO0
cd /path/to/Model-References/PyTorch/examples/DeepSpeed/minGPT
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config.json --use_hpu --steps 4 --dump-memory

There will be a OOM error from HPU SW stack like below:

...
RuntimeError: FATAL ERROR :: MODULE:BRIDGE Exception in Launch thread...
FATAL ERROR :: MODULE:DEVMEM Allocation failed for size::40960000 (39.0625)MB
  1. Run minGPT with DeepSpeed ZeRO1
cd /path/to/Model-References/PyTorch/examples/DeepSpeed/minGPT
deepspeed demo_ds.py --deepspeed --deepspeed_config ds_config_zero1.json --use_hpu --steps 4 --dump-memory

Via applying ZeRO technology, e.g. ZeRO1, the model can run successfully on HPU.

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