[Train] Add example of pre-training Llama model on Intel Gaudi #45459
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harborn
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aslonnie
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May 21, 2024
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Signed-off-by: Wu, Gangsheng <gangsheng.wu@intel.com>
Signed-off-by: Wu, Gangsheng <gangsheng.wu@intel.com>
Signed-off-by: Wu, Gangsheng <gangsheng.wu@intel.com>
woshiyyya
reviewed
May 31, 2024
| @@ -0,0 +1,568 @@ | |||
| { | |||
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High-level question: seems that it's using deepspeed zero-3 for pre-training. why we also include megatron here?
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this example import megatron just for data processing
woshiyyya
reviewed
May 31, 2024
| " )\n", | ||
| "\n", | ||
| " # Data loader only on rank 0 of each model parallel group.\n", | ||
| " if args.use_dataset_only or mpu.get_tensor_model_parallel_rank() == 0:\n", |
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Where did we configure the tensor and pipeline parallel group size?
woshiyyya
reviewed
May 31, 2024
| "(RayTrainWorker pid=339380) {'loss': nan, 'grad_norm': nan, 'learning_rate': 4.9e-05, 'epoch': 0.0, 'memory_allocated (GB)': 40.42, 'max_memory_allocated (GB)': 93.68, 'total_memory_available (GB)': 94.62}\n", | ||
| "(RayTrainWorker pid=339380) {'loss': nan, 'grad_norm': nan, 'learning_rate': 4.875e-05, 'epoch': 0.0, 'memory_allocated (GB)': 40.4, 'max_memory_allocated (GB)': 93.68, 'total_memory_available (GB)': 94.62}\n", | ||
| "(RayTrainWorker pid=339380) {'loss': nan, 'grad_norm': nan, 'learning_rate': 4.85e-05, 'epoch': 0.0, 'memory_allocated (GB)': 40.4, 'max_memory_allocated (GB)': 93.68, 'total_memory_available (GB)': 94.62}\n", | ||
| "(RayTrainWorker pid=339380) {'loss': nan, 'grad_norm': nan, 'learning_rate': 4.825e-05, 'epoch': 0.0, 'memory_allocated (GB)': 40.45, 'max_memory_allocated (GB)': 93.68, 'total_memory_available (GB)': 94.62}\n", |
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seems that the loss and grad norm are nan, can you try to fix the bug?
woshiyyya
reviewed
May 31, 2024
| "\n", | ||
| " # Set backend to hccl in TorchConfig\n", | ||
| " torch_config = TorchConfig(backend=\"hccl\")\n", | ||
| " runtime_env = {\n", |
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Do we need this if it's empty
woshiyyya
reviewed
May 31, 2024
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Process dataset to dataloader" |
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In general, can we introduce the model sharding layouts, so that the users can better understand why the dataloader is defined this?
Including pp/tp/dp group size, global batch size, per device batch size, etc.
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Why are these changes needed?
To leverage the potential of Intel Gaudi accelerator, we extend Ray Train's capabilities by adding support for Intel Gaudi (HPU) hardware. This PR include an example for pre-training Llama-7b on multi HPUs.
Related issue number
Checks
git commit -s) in this PR.scripts/format.shto lint the changes in this PR.method in Tune, I've added it in
doc/source/tune/api/under thecorresponding
.rstfile.