DeBERTa's DisentangledSelfAttention hardcodes float dtype, which causes bfloat16 overflow error

[bauwenst]

System Info

transformers: 4.47.0
Python: 3.10.5
PyTorch: 2.5.1+cu124
GPU: NVIDIA GTX 980 Ti

Who can help?

@ArthurZucker

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, ...)
• My own task or dataset (give details below)

Reproduction

I'm training a DebertaForMaskedLM model with a broader experimental framework, but you can reproduce the bug with simple inference as follows: instantiate such a model with datatype bfloat16, and send a batch through it.

import torch
from transformers import DebertaConfig, DebertaForMaskedLM

model = DebertaForMaskedLM._from_config(DebertaConfig(), torch_dtype=torch.bfloat16)
model(**{"input_ids": torch.tensor([[101,102,103,104]]),
         "attention_mask": torch.tensor([[1,1,1,1]])})

One of two errors is now thrown in modeling_deberta.py, both in DisentangledSelfAttention.forward() (and they can both be traced back to the same issue):

1. RuntimeError: expected m1 and m2 to have the same dtype, but got: float != struct c10::BFloat16
2. RuntimeError: value cannot be converted to type at::BFloat16 without overflow

Here's where they come from: two fields in DeBERTa's DisentangledSelfAttention are constructed by explicitly declaring their dtype as torch.float:

transformers/src/transformers/models/deberta/modeling_deberta.py

Lines 187 to 188 in 9613933

	self.q_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))
	self.v_bias = nn.Parameter(torch.zeros((self.all_head_size), dtype=torch.float))

Then, in forward(), we create the two tensors query_layer and key_layer that start out with the dtype of the hidden states, which have the dtype of the model, namely bfloat16:

transformers/src/transformers/models/deberta/modeling_deberta.py

Lines 258 to 259 in 9613933

	qp = self.in_proj(hidden_states) # .split(self.all_head_size, dim=-1)
	query_layer, key_layer, value_layer = self.transpose_for_scores(qp).chunk(3, dim=-1)

But then, one of these tensors, query_layer, is modified by adding self.q_bias into it. The resulting tensor inherits the torch.float data type:

transformers/src/transformers/models/deberta/modeling_deberta.py

Line 268 in 9613933

query_layer = query_layer + self.transpose_for_scores(self.q_bias[None, None, :])

The first RuntimeError can occur on the following line, when query_layer (now torch.float) and key_layer (still torch.bfloat16) are multiplied. I've had this line crash on one machine and work on another, so perhaps this kind of mixed precision sometimes works.

transformers/src/transformers/models/deberta/modeling_deberta.py

Line 276 in 9613933

attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

The second RuntimeError occurs even when mixed precision is supported. It happens on the following line:

transformers/src/transformers/models/deberta/modeling_deberta.py

Line 290 in 9613933

attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(query_layer.dtype).min)

attention_scores is of type bfloat16. You then ask to fill it with the minimal value for the data type of query_layer, not the data type of attention_scores. Because query_layer.dtype is torch.float, that minimal value (-3.40282e+38) is more negative than the most negative torch.bfloat16 (-3.38953e+38). Hence, the overflow.

Expected behavior

The dtype of self.q_bias and self.v_bias should be set like the rest of the modules/tensors in the model, rather than being hardcoded. That would keep everything bfloat16.