| import math |
| from contextlib import nullcontext |
| from typing import TYPE_CHECKING |
|
|
| import torch |
| from transformers.integrations import is_deepspeed_zero3_enabled |
|
|
| from ...extras.logging import get_logger |
|
|
|
|
| if TYPE_CHECKING: |
| from transformers import PreTrainedModel, PreTrainedTokenizer |
|
|
|
|
| logger = get_logger(__name__) |
|
|
|
|
| def _noisy_mean_initialization(embed_weight: torch.Tensor, num_new_tokens: int) -> None: |
| embedding_dim = embed_weight.size(1) |
| avg_weight = embed_weight[:-num_new_tokens].mean(dim=0, keepdim=True) |
| noise_weight = torch.empty_like(embed_weight[-num_new_tokens:]) |
| noise_weight.normal_(mean=0, std=(1.0 / math.sqrt(embedding_dim))) |
| embed_weight[-num_new_tokens:] = avg_weight + noise_weight |
|
|
|
|
| def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedTokenizer") -> None: |
| r""" |
| Resize token embeddings. |
| """ |
| if is_deepspeed_zero3_enabled(): |
| import deepspeed |
|
|
| params = [model.get_input_embeddings().weight] |
| if model.get_output_embeddings() is not None and not model.config.tie_word_embeddings: |
| params.append(model.get_output_embeddings().weight) |
|
|
| context_maybe_zero3 = deepspeed.zero.GatheredParameters(params, modifier_rank=0) |
| else: |
| context_maybe_zero3 = nullcontext() |
|
|
| with context_maybe_zero3: |
| current_embedding_size = model.get_input_embeddings().weight.size(0) |
|
|
| if len(tokenizer) > current_embedding_size: |
| if getattr(model, "quantization_method", None): |
| raise ValueError("Cannot resize embedding layers of a quantized model.") |
|
|
| if not isinstance(model.get_output_embeddings(), torch.nn.Linear): |
| raise ValueError("Current model does not support resizing embedding layers.") |
|
|
| model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64) |
| with context_maybe_zero3: |
| new_embedding_size = model.get_input_embeddings().weight.size(0) |
| num_new_tokens = new_embedding_size - current_embedding_size |
| _noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens) |
| _noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens) |
|
|
| logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size)) |
|
|
