Support for Hugging Face Transformer Models

The SageMaker model parallelism library's tensor parallelism offers out-of-the-box support for the following Hugging Face Transformer models:

• GPT-2, BERT, and RoBERTa (Available in the SageMaker model parallelism library v1.7.0 and later)

• GPT-J (Available in the SageMaker model parallelism library v1.8.0 and later)

• GPT-Neo (Available in the SageMaker model parallelism library v1.10.0 and later)

Note

For any other Transformers models, you need to use the smdistributed.modelparallel.torch.tp_register_with_module() API to apply tensor parallelism.

Note

To use tensor parallelism for training Hugging Face Transformer models, make sure you use Hugging Face Deep Learning Containers for PyTorch that has the SageMaker model parallelism library v1.7.0 and later. For more information, see the SageMaker model parallelism library release notes.

Supported Models Out of the Box

For the Hugging Face transformer models supported by the library out of the box, you don't need to manually implement hooks to translate Transformer APIs to smdistributed transformer layers. You can activate tensor parallelism by using the context manager smdistributed.modelparallel.torch.tensor_parallelism() and wrapping the model by smdistributed.modelparallel.torch.DistributedModel(). You don't need to manually register hooks for tensor parallelism using the smp.tp_register API.

The state_dict translation functions between Hugging Face Transformers and smdistributed.modelparallel can be accessed as follows.

• smdistributed.modelparallel.torch.nn.huggingface.gpt2.translate_state_dict_to_hf_gpt2(state_dict, max_seq_len=None)

• smdistributed.modelparallel.torch.nn.huggingface.gpt2.translate_hf_state_dict_to_smdistributed_gpt2(state_dict)

• smdistributed.modelparallel.torch.nn.huggingface.bert.translate_state_dict_to_hf_bert(state_dict, max_seq_len=None)

• smdistributed.modelparallel.torch.nn.huggingface.bert.translate_hf_state_dict_to_smdistributed_bert(state_dict)

• smdistributed.modelparallel.torch.nn.huggingface.roberta.translate_state_dict_to_hf_roberta(state_dict, max_seq_len=None)

• smdistributed.modelparallel.torch.nn.huggingface.roberta.translate_hf_state_dict_to_smdistributed_roberta(state_dict)

• smdistributed.modelparallel.torch.nn.huggingface.gptj.translate_state_dict_to_hf_gptj(state_dict, max_seq_len=None) (Available in the SageMaker model parallelism library v1.8.0 and later)

• smdistributed.modelparallel.torch.nn.huggingface.gptj.translate_hf_gptj_state_dict_to_smdistributed_gptj (Available in the SageMaker model parallelism library v1.8.0 and later)

• smdistributed.modelparallel.torch.nn.huggingface.gptneo.translate_state_dict_to_hf_gptneo(state_dict, max_seq_len=None) (Available in the SageMaker model parallelism library v1.10.0 and later)

• smdistributed.modelparallel.torch.nn.huggingface.gptneo.translate_hf_state_dict_to_smdistributed_gptneo(state_dict) (Available in the SageMaker model parallelism library v1.10.0 and later)

Example usage of the GPT-2 translation function

Start with wrapping the model as shown in the following code.

from transformers import AutoModelForCausalLM

with smp.tensor_parallelism():
    model = AutoModelForCausalLM.from_config(hf_gpt2_config)

model = smp.DistributedModel(model)

Given a state_dict from the DistributedModel object, you can load the weights into the original Hugging Face GPT-2 model using the translate_state_dict_to_hf_gpt2 function as shown in the following code.

from smdistributed.modelparallel.torch.nn.huggingface.gpt2 \
                                      import translate_state_dict_to_hf_gpt2
max_seq_len = 1024

# [... code block for training ...]

if smp.rdp_rank() == 0:
    state_dict = dist_model.state_dict()
    hf_state_dict = translate_state_dict_to_hf_gpt2(state_dict, max_seq_len)

    # can now call model.load_state_dict(hf_state_dict) to the original HF model

Example usage of the RoBERTa translation function

Similarly, given a supported HuggingFace model state_dict, you can use the translate_hf_state_dict_to_smdistributed function to convert it to a format readable by smp.DistributedModel. This can be useful in transfer learning use cases, where a pre-trained model is loaded into a smp.DistributedModel for model-parallel fine-tuning:

from smdistributed.modelparallel.torch.nn.huggingface.roberta \
                                      import translate_state_dict_to_smdistributed

model = AutoModelForMaskedLM.from_config(roberta_config)
model = smp.DistributedModel(model)

pretrained_model = AutoModelForMaskedLM.from_pretrained("roberta-large")
translated_state_dict =
        translate_state_dict_to_smdistributed(pretrained_model.state_dict())

# load the translated pretrained weights into the smp.DistributedModel
model.load_state_dict(translated_state_dict)

# start fine-tuning...