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Fix hardcoded float dtypes in DeBERTa model, which caused multiple RuntimeErrors in bfloat16 #35336

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Fix hardcoded float dtypes in DeBERTa model, which caused multiple RuntimeErrors in bfloat16 #35336

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15 changes: 7 additions & 8 deletions src/transformers/models/deberta/modeling_deberta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,7 +62,7 @@ def __init__(self, size, eps=1e-12):

def forward(self, hidden_states):
input_type = hidden_states.dtype
hidden_states = hidden_states.float()
hidden_states = hidden_states.float() # TODO: Even when working in bfloat16?
mean = hidden_states.mean(-1, keepdim=True)
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
hidden_states = (hidden_states - mean) / torch.sqrt(variance + self.variance_epsilon)
Expand Down Expand Up @@ -134,7 +134,7 @@ def pos_dynamic_expand(pos_index, p2c_att, key_layer):
# Full credits to @Szustarol
@torch.jit.script
def scaled_size_sqrt(query_layer: torch.Tensor, scale_factor: int):
return torch.sqrt(torch.tensor(query_layer.size(-1), dtype=torch.float) * scale_factor)
return torch.sqrt(torch.tensor(query_layer.size(-1), dtype=query_layer.dtype) * scale_factor)


@torch.jit.script
Expand Down Expand Up @@ -184,8 +184,8 @@ def __init__(self, config):
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.in_proj = nn.Linear(config.hidden_size, self.all_head_size * 3, bias=False)
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))
self.q_bias = nn.Parameter(torch.zeros((self.all_head_size)))
self.v_bias = nn.Parameter(torch.zeros((self.all_head_size)))
self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []

self.relative_attention = getattr(config, "relative_attention", False)
Expand Down Expand Up @@ -271,8 +271,7 @@ def forward(
rel_att: int = 0
# Take the dot product between "query" and "key" to get the raw attention scores.
scale_factor = 1 + len(self.pos_att_type)
scale = scaled_size_sqrt(query_layer, scale_factor)
query_layer = query_layer / scale.to(dtype=query_layer.dtype)
query_layer = query_layer / scaled_size_sqrt(query_layer, scale_factor)
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))

if self.relative_attention and rel_embeddings is not None and relative_pos is not None:
Expand All @@ -287,7 +286,7 @@ def forward(
attention_scores = self.head_logits_proj(attention_scores.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)

attention_mask = attention_mask.bool()
attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(query_layer.dtype).min)
attention_scores = attention_scores.masked_fill(~(attention_mask), torch.finfo(attention_scores.dtype).min)
# bsz x height x length x dimension
attention_probs = nn.functional.softmax(attention_scores, dim=-1)

Expand Down Expand Up @@ -1133,7 +1132,7 @@ def forward(
)
labels = torch.gather(labels, 0, label_index.view(-1))
loss_fct = CrossEntropyLoss()
loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))
loss = loss_fct(labeled_logits.view(-1, self.num_labels).to(dtype=encoder_layer.dtype), labels.view(-1))
else:
loss = torch.tensor(0).to(logits)
else:
Expand Down