HuggingFace_transformer/docs/source/en/model_doc/granitemoehybrid.md

GraniteMoeHybrid

Overview

The GraniteMoeHybrid model builds on top of GraniteMoeSharedModel and Bamba. Its decoding layers consist of state space layers or MoE attention layers with shared experts. By default, the attention layers do not use positional encoding.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "ibm-granite/granite-4.0-tiny-preview"
tokenizer = AutoTokenizer.from_pretrained(model_path)

# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
model.eval()

# change input text as desired
prompt = "Write a code to find the maximum value in a list of numbers."

# tokenize the text
input_tokens = tokenizer(prompt, return_tensors="pt")
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
    print(i)

This HF implementation is contributed by Sukriti Sharma and Alexander Brooks.

Notes

• GraniteMoeHybridForCausalLM supports padding-free training which concatenates distinct training examples while still processing inputs as separate batches. It can significantly accelerate inference by ~2x (depending on model and data distribution) and reduce memory-usage if there are examples of varying lengths by avoiding unnecessary compute and memory overhead from padding tokens.

  Padding-free training requires the flash-attn, mamba-ssm, and causal-conv1d packages and the following arguments must be passed to the model in addition to input_ids and labels.

  • position_ids: torch.LongTensor: the position index of each token in each sequence.
  • seq_idx: torch.IntTensor: the index of each sequence in the batch.
  • Each of the [FlashAttentionKwargs]
    • cu_seq_lens_q: torch.LongTensor: the cumulative sequence lengths of all queries.
    • cu_seq_lens_k: torch.LongTensor: the cumulative sequence lengths of all keys.
    • max_length_q: int: the longest query length in the batch.
    • max_length_k: int: the longest key length in the batch.

  The attention_mask inputs should not be provided. The [DataCollatorWithFlattening] programmatically generates the set of additional arguments above using return_seq_idx=True and return_flash_attn_kwargs=True. See the Improving Hugging Face Training Efficiency Through Packing with Flash Attention blog post for additional information.

  from transformers import DataCollatorWithFlattening
  
  # Example of using padding-free training
  data_collator = DataCollatorWithFlattening(
      tokenizer=tokenizer,
      return_seq_idx=True,
      return_flash_attn_kwargs=True
  )
  

GraniteMoeHybridConfig

autodoc GraniteMoeHybridConfig

GraniteMoeHybridModel

autodoc GraniteMoeHybridModel - forward

GraniteMoeHybridForCausalLM

autodoc GraniteMoeHybridForCausalLM - forward