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added type hints for blip_text pytorch model (#23071)

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* added type hints for blip_text pytorch model

* updated type hints for blip_text pytorch model
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Arun Brahma 2023-05-02 17:52:31 +05:30 committed by GitHub
parent b8648290d2
commit 85e3d7b6a0
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1 changed files with 68 additions and 62 deletions
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130
src/transformers/models/blip/modeling_blip_text.py
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@ -15,7 +15,7 @@
import math import math
from typing import Optional, Tuple from typing import List, Optional, Tuple, Union
import torch import torch
import torch.utils.checkpoint import torch.utils.checkpoint
@ -61,7 +61,13 @@ class BlipTextEmbeddings(nn.Module):
self.config = config self.config = config
def forward(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0): def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if input_ids is not None: if input_ids is not None:
input_shape = input_ids.size() input_shape = input_ids.size()
else: else:
@ -134,14 +140,14 @@ class BlipTextSelfAttention(nn.Module):
def forward( def forward(
self, self,
hidden_states, hidden_states: torch.Tensor,
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value=None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions=False, output_attentions: Optional[bool] = False,
): ) -> Tuple[torch.Tensor]:
mixed_query_layer = self.query(hidden_states) mixed_query_layer = self.query(hidden_states)
# If this is instantiated as a cross-attention module, the keys # If this is instantiated as a cross-attention module, the keys
@ -263,7 +269,7 @@ class BlipTextAttention(nn.Module):
encoder_attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions: Optional[bool] = False, output_attentions: Optional[bool] = False,
): ) -> Tuple[torch.Tensor]:
self_outputs = self.self( self_outputs = self.self(
hidden_states, hidden_states,
attention_mask, attention_mask,
@ -324,14 +330,14 @@ class BlipTextLayer(nn.Module):
def forward( def forward(
self, self,
hidden_states, hidden_states: torch.Tensor,
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_value=None, past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
output_attentions=False, output_attentions: Optional[bool] = False,
): ) -> Tuple[torch.Tensor]:
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2 # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
self_attention_outputs = self.attention( self_attention_outputs = self.attention(
@ -382,17 +388,17 @@ class BlipTextEncoder(nn.Module):
def forward( def forward(
self, self,
hidden_states, hidden_states: torch.Tensor,
attention_mask=None, attention_mask: Optional[torch.FloatTensor] = None,
head_mask=None, head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values=None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache=None, use_cache: Optional[bool] = None,
output_attentions=False, output_attentions: Optional[bool] = False,
output_hidden_states=False, output_hidden_states: Optional[bool] = False,
return_dict=True, return_dict: Optional[bool] = True,
): ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
if self.gradient_checkpointing and self.training: if self.gradient_checkpointing and self.training:
if use_cache: if use_cache:
logger.warn( logger.warn(
@ -663,21 +669,21 @@ class BlipTextModel(BlipTextPreTrainedModel):
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
encoder_embeds=None, encoder_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_values=None, past_key_values: Optional[List[torch.FloatTensor]] = None,
use_cache=None, use_cache: Optional[bool] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
is_decoder=False, is_decoder: Optional[bool] = False,
): ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
r""" r"""
encoder_hidden_states (`torch.FloatTensor`, *optional*): encoder_hidden_states (`torch.FloatTensor`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
@ -819,23 +825,23 @@ class BlipTextLMHeadModel(BlipTextPreTrainedModel):
def forward( def forward(
self, self,
input_ids=None, input_ids: Optional[torch.Tensor] = None,
attention_mask=None, attention_mask: Optional[torch.Tensor] = None,
position_ids=None, position_ids: Optional[torch.Tensor] = None,
head_mask=None, head_mask: Optional[torch.Tensor] = None,
inputs_embeds=None, inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states=None, encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask=None, encoder_attention_mask: Optional[torch.Tensor] = None,
labels=None, labels: Optional[torch.Tensor] = None,
past_key_values=None, past_key_values: Optional[List[torch.Tensor]] = None,
use_cache=None, use_cache: Optional[bool] = None,
output_attentions=None, output_attentions: Optional[bool] = None,
output_hidden_states=None, output_hidden_states: Optional[bool] = None,
return_dict=None, return_dict: Optional[bool] = None,
return_logits=False, return_logits: Optional[bool] = False,
is_decoder=True, is_decoder: Optional[bool] = True,
reduction="mean", reduction: Optional[str] = "mean",
): ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r""" r"""
encoder_hidden_states (`torch.FloatTensor`, *optional*): Sequence of encoder_hidden_states (`torch.FloatTensor`, *optional*): Sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is