text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings | 9,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, layer_idx=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
self.num_attention_heads = config.num_attention_heads
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.image_patch_tokens = int((config.vision_config.image_size / config.vision_config.pat... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = position_embedding_type or getattr(
config, "position_embedding_type", "absolute"
)
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
pixel_values_present: Optional[boo... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
cutoff = self.image_patch_tokens if pixel_values_present else 0
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
if past_key_value is not None:
# NOTE: like in other caches, we store the text component. ... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query":
query_length, key_length = query_layer.shape[2], key_layer.shape[2]
if past_key_value is not None:
position_ids_l = torch.tensor(key_length - 1, dtype=torch.long, device=hid... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if self.position_embedding_type == "relative_key":
relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding)
attention_scores = attention_scores + relative_position_scores
elif self.position_embedding_type == "relative_key_query":
... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dro... | 9,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def fo... | 9,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitAttention(nn.Module):
def __init__(self, config, position_embedding_type=None, layer_idx=None):
super().__init__()
self.self = GIT_SELF_ATTENTION_CLASSES[config._attn_implementation](
config, position_embedding_type=position_embedding_type, layer_idx=layer_idx
)
... | 9,651 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# Prune linear layers
self.self.query = prune_linear_layer(self.self.query, index)
self.self.key = prune_linear_layer(self.self.key, index)
self.self.value = prune_linear_layer(self.self.value, index)
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
# Upda... | 9,651 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
pixel_values_present: Optional[boo... | 9,651 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.interme... | 9,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def ... | 9,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitLayer(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = GitAttention(config, layer_idx=layer_idx)
self.intermediate = GitIntermediate(config)
... | 9,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
pixel_values_present: Optional[boo... | 9,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
layer_output = apply_chunking_to_forward(
self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
)
outputs = (layer_output,) + outputs
# if decoder, return the attn key/values as the last output
outputs = outputs + (present_key_value,)
... | 9,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([GitLayer(config, i) for i in range(config.num_hidden_layers)])
self.gradient_checkpointing = False | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None,
use_cache: Optional[bool] = None,
ou... | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# kept for BC (non `Cache` `past_key_values` inputs)
return_legacy_cache = False
if use_cache and not isinstance(past_key_values, Cache):
return_legacy_cache = True
if past_key_values is None:
past_key_values = DynamicCache()
else:
past... | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
next_decoder_cache = None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
... | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
attention_mask,
layer_head_mask,
past_key_values,
... | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.to_legacy_cache()
if not return_dict:
return tuple(
v
... | 9,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GitConfig
base_model_prefix = "git"
supports_gradient_checkpointing = True
_supports_cache_class = T... | 9,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, GitVisionEmbeddings):
nn.init.normal_(module.class_embedding, mean=0.0, std=self.config.initializer_range)
nn.init.normal_(module.patch_embedding.weight, std=self.config.initializer_range)
... | 9,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
module.weight.data[module.padding_idx].zero_()
elif isinstance(module, nn.LayerNorm):
module.bias.data.zero_()
module.weight.data.fill_(1.0) | 9,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionEmbeddings(nn.Module):
def __init__(self, config: GitVisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parameter(... | 9,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
"""
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
images. This method is also adapted to support torch.jit tracing.
... | 9,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# always interpolate when tracing to ensure the exported model works for dynamic input shapes
if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
return self.position_embedding(self.position_ids)
class_pos_embed = position_embedding[:, :1]
patch_pos_e... | 9,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.FloatTensor, interpolate_pos_encoding=False) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
if not interpolate_pos_encoding and (height != self.image_size or width != self.image_siz... | 9,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
if interpolate_pos_encoding:
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
else:
embeddings = embeddings + self.p... | 9,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden_si... | 9,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = se... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: ... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
key_states = key_states.view(*proj_shape)
value_states = value_states.view(*proj_shape)
src_len = key_states.size(1)
attn_weights = torch.bmm(query_sta... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# apply the causal_attention_mask first
if causal_attention_mask is not None:
if causal_attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is"
f" {causal_a... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights.view(bsz, se... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if output_attentions:
# this operation is a bit akward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_weight... | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, tgt_len, embed_dim)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights_reshaped | 9,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionEncoderLayer(nn.Module):
def __init__(self, config: GitVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = GitVisionAttention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = GitVis... | 9,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the... | 9,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
hidden_states = self.layer_norm1(hidden_states)
hidden_states, attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
)
hidden_sta... | 9,660 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`GitVisionEncoderLayer`].
Args:
config: GitVisionConfig
"""
def __init__(self, config: GitVisionConfig):
super().__init__()
self.c... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Causal mask for the text model. Ma... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
[What are attention masks?](../glossary#attention-mask)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
output_hidden_states (`bool`, *optiona... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
encoder_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
hidden_states = inputs_embeds
for idx, encoder_layer in enumerate(self.layers):
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
encoder_states = encoder_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None)
... | 9,661 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionTransformer(nn.Module):
# Copied from transformers.models.altclip.modeling_altclip.AltCLIPVisionTransformer.__init__ with AltCLIPEncoder->GitVisionEncoder, AltCLIP->Git
def __init__(self, config: GitVisionConfig):
super().__init__()
self.config = config
embed_dim = config.... | 9,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
@add_start_docstrings_to_model_forward(GIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=GitVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_s... | 9,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
hidden_states = self.pre_layrnorm(hidden_states)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=ou... | 9,662 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitVisionModel(GitPreTrainedModel):
config_class = GitVisionConfig
main_input_name = "pixel_values"
# Copied from transformers.models.clip.modeling_clip.CLIPVisionModel.__init__ with CLIP->Git
def __init__(self, config: GitVisionConfig):
super().__init__(config)
self.vision_model ... | 9,663 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
@add_start_docstrings_to_model_forward(GIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BaseModelOutput, config_class=GitVisionConfig)
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_s... | 9,663 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return self.vision_model(
pixel_val... | 9,663 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitProjection(nn.Module):
def __init__(self, config: GitConfig):
super().__init__()
self.config = config
self.visual_projection = nn.Sequential(
nn.Linear(config.vision_config.hidden_size, config.hidden_size),
nn.LayerNorm(config.hidden_size, eps=config.vision_c... | 9,664 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitModel(GitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = GitEmbeddings(config)
self.image_encoder = GitVisionModel(config.vision_config)
self.encoder = GitEncoder(config)
self.visual_projection =... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def _prune_heads(self, heads_to_prune):
"""
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
class PreTrainedModel
"""
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_he... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def create_attention_mask(self, tgt, memory, tgt_mask, past_key_values_length, memory_key_padding_mask=None):
num_tgt = tgt.shape[1]
num_memory = memory.shape[1]
device = tgt.device
dtype = tgt.dtype
top_left = torch.zeros((num_memory, num_memory), device=device, dtype=dtype)
... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
full_attention_mask = torch.cat((left, right), dim=1)[None, :] | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if memory_key_padding_mask is None:
memory_key_padding_mask = torch.full((memory.shape[0], memory.shape[1]), fill_value=False, device=device)
# if it is False, it means valid. That is, it is not a padding
if memory_key_padding_mask.dtype != torch.bool:
raise ValueError("Memory ke... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# add axis for multi-head
full_attention_mask = full_attention_mask[:, None, :, :]
return full_attention_mask | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
@add_start_docstrings_to_model_forward(GIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`). | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> import requests
>>> from PIL import Image
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base")
>>> model = AutoModel.from_pretrained("microsoft/git-base")
... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None e... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
input_shape = input_ids.size()
... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
elif pixel_values.ndim == 5:
# here we assume pixel_values is of shape (batch_size, num_frames, num_channels, height, width)
visual_features = []
for frame_idx in range(pixel_values.shape[1]):
visual_features_frame = self.image_encoder(
... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
)
if projected_visual_features is None:
projected_visual_features = torch.zeros(
... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# By default, an additive causal mask is created
# for masking the future (one direction).
tgt_mask = self._generate_future_mask(seq_length, embedding_output.dtype, embedding_output.device)
# Create an attention mask of shape (batch_size, 1, tgt_seq_len, src_seq_len)
combined_attention_... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
if attention_mask is not None:
# if the user provides an attention mask, we add it to the default one
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _prepare_4d_attention_mask(
attention_mask, embedding_output.dtype, tgt_len=input_shape[-1... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
encoder_outputs = self.encoder(
hidden_states,
attention_mask=combined_attention_mask,
head_mask=head_mask,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_st... | 9,665 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class GitForCausalLM(GitPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["output.weight"]
def __init__(self, config):
super().__init__(config)
self.git = GitModel(config)
self.output = nn.Linear(config.hidden_size, config.vocab_size)
# Initialize weights and apply fina... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
@add_start_docstrings_to_model_forward(GIT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tens... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
Returns:
Examples:
Image captioning example:
```python
>>> from transformers import AutoProcessor, AutoModelForCausalLM
>>> import requests
>>> from PIL import Image
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base-coco")
>>> model = A... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
```python
>>> from transformers import AutoProcessor, AutoModelForCausalLM
>>> from huggingface_hub import hf_hub_download
>>> from PIL import Image
>>> processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa")
>>> model = AutoModelForCausalLM.from_pretrained("micr... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
>>> print(processor.batch_decode(generated_ids, skip_special_tokens=True))
['what does the front of the bus say at the top? special']
```
Video captioning example:
```python
... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Retu... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
... '''
... Sample a given number of frame indices from the video.
... Args:
... clip_len (`int`): Total number of frames to sample.
... frame_sample_rate (`int`): Sample every n-th fr... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> # load video
>>> file_path = hf_hub_download(
... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset"
... )
>>> container = av.open(file_path)
>>> # sample frames
>>> num_frames = model.config.num_image_with_embedding
>>> ind... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
>>> print("Generated caption:", processor.batch_decode(generated_ids, skip_special_tokens=True))
Generated caption: ['a woman is sitting at a table and she is talking about the food she is holding.']
```
"""
return_dict = return_dict if return_dict is not None else self.config.use_return... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
num_image_tokens = self.git.encoder.layer[0].attention.self.image_patch_tokens
shifted_logits = logits[:, num_image_tokens:-1, :].contiguous()
lab... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, **kwargs
):
# Overwritten -- `git` has special cache handling and doesn't support generating from `inputs_embeds` atm
# cut decoder_input_ids if past_key_values is used
if ... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
input_shape = input_ids.shape
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
return {
"input_ids": input_ids,
"attention_ma... | 9,666 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/git/modeling_git.py |
class TFMistralRMSNorm(keras.layers.Layer):
def __init__(self, hidden_size, eps=1e-6, **kwargs):
"""
TFMistralRMSNorm is equivalent to T5LayerNorm
"""
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.variance_epsilon = eps
def build(self, input_shap... | 9,667 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
class TFMistralRotaryEmbedding(keras.layers.Layer):
def __init__(self, dim, max_position_embeddings=2048, base=10000, **kwargs):
super().__init__(**kwargs)
self.dim = dim
self.max_position_embeddings = max_position_embeddings
self.base = base
self.inv_freq = 1.0 / (self.base ... | 9,668 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
class TFMistralMLP(keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = keras.layers.Dense(self.intermediate_size, us... | 9,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "gate_proj", None) is not None:
with tf.name_scope(self.gate_proj.name):
self.gate_proj.build((self.hidden_size,))
if getattr(self, "up_proj", None) is not ... | 9,669 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
class TFMistralAttention(keras.layers.Layer):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
def __init__(self, config: MistralConfig, layer_idx: Optional[int] = No... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.hidden_size // self.num_heads
self.num_key_value_heads = config.num_key_value_heads
self.num_key_value_groups = self.num_heads // self.num_key_value_heads
self.max_position_embe... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
if (self.head_dim * self.num_heads) != self.hidden_size:
raise ValueError(
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
f" and `num_heads`: {self.num_heads})."
)
self.q_proj = keras.layers.Dense(self.num_heads * self... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int):
tensor = tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim))
tensor = tf.transpose(tensor, perm=(0, 2, 1, 3))
return tensor
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[t... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = tf.transpose(
tf.reshape(query_states, (bsz, q_len, self.num_heads, self.head_dim)), perm=(0, 2, 1, 3)
)
key_states = tf.transpo... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
kv_seq_len = shape_list(key_states)[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
cos, sin = self.rotary_emb(
x=value_states,
seq_len=kv_seq_len,
)
query_states, key_states = apply_rotary_pos_emb(
q=query_stat... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
attn_weights = tf.matmul(query_states, key_states, transpose_b=True) / math.sqrt(self.head_dim)
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
# upcast attention to fp32
attn_weights = stable_softmax(attn_weights, axis=-1)
attn_weights = tf.cast... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
def build(self, input_shape=None):
if self.built:
return
self.built = True
if getattr(self, "q_proj", None) is not None:
with tf.name_scope(self.q_proj.name):
self.q_proj.build((self.hidden_size,))
if getattr(self, "k_proj", None) is not None:
... | 9,670 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
class TFMistralDecoderLayer(keras.layers.Layer):
def __init__(self, config: MistralConfig, layer_idx: int, **kwargs):
super().__init__(**kwargs)
self.hidden_size = config.hidden_size
self.self_attn = TFMistralAttention(config, layer_idx, name="self_attn")
self.mlp = TFMistralMLP(co... | 9,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[tf.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 9,671 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mistral/modeling_tf_mistral.py |
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