text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def __init__(self, vocab_file, merges_file, unk_token="<unk>", **kwargs):
try:
import ftfy
from spacy.lang.en import English
_nlp = English()
self.nlp = _nlp.tokenizer
self.fix_text = ftfy.fix_text
except ImportError:
logger.warnin... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
@property
def do_lower_case(self):
return True
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
return dict(self.encoder, **self.added_tokens_encoder)
def bpe(self, token):
word = tuple(token[:-1]) + (token[-1] + "</w>",)
if tok... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
while True:
bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
if bigram not in self.bpe_ranks:
break
first, second = bigram
new_word = []
i = 0
while i < len(word):
try:
... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
new_word.append(first + second)
i += 2
else:
new_word.append(word[i])
i += 1
new_word = tuple(new_word)
word = new_word
... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def _tokenize(self, text):
"""Tokenize a string."""
split_tokens = []
if self.fix_text is None:
# Using BERT's BasicTokenizer
text = self.nlp.tokenize(text)
for token in text:
split_tokens.extend(list(self.bpe(token).split(" ")))
else:
... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
def convert_tokens_to_string(self, tokens):
"""Converts a sequence of tokens (string) in a single string."""
out_string = "".join(tokens).replace("</w>", " ").strip()
return out_string
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
index = 0
with open(merge_file, "w", encoding="utf-8") as writer:
writer.write("#version: 0.2\n")
for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
if index != token_index:
logger.warning(
f"Sa... | 9,799 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai.py |
class OpenAIGPTTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with
the following peculiarities:
- lower case all inputs
- uses BERT's BasicTokenizer for pre-BPE tokenization
This tokenizer... | 9,800 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai_fast.py |
def __init__(self, vocab_file=None, merges_file=None, tokenizer_file=None, unk_token="<unk>", **kwargs):
super().__init__(vocab_file, merges_file, tokenizer_file=tokenizer_file, unk_token=unk_token, **kwargs)
@property
def do_lower_case(self):
return True
def save_vocabulary(self, save_dir... | 9,800 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai_fast.py |
class OpenAIGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is
used to instantiate a GPT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the d... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
Args:
vocab_size (`int`, *optional*, defaults to 40478):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`OpenAIGPTModel`] or [`TFOpenAIGPTModel`].
n_positions (`int`, *optional*, defaults... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`]. | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
Has to be one of the following options:
- `"last"`: Take the last token hidden state (like XLNet).
- `"first"`: Take the first token hidden state (like BERT).
- `"mean"`: Take the mean of all tokens hidden states.
- `"cls_index"`: Supply a Tensor of class... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
Argument used when doing sequence summary, used in the models [`OpenAIGPTDoubleHeadsModel`] and
[`OpenAIGPTDoubleHeadsModel`].... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
>>> # Initializing a model (with random weights) from the configuration
>>> model = OpenAIGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "openai-gpt"
attribute_map = {
"max_position_embeddings": "n_positions",
... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
def __init__(
self,
vocab_size=40478,
n_positions=512,
n_embd=768,
n_layer=12,
n_head=12,
afn="gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
summary_type="cl... | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
super().__init__(**kwargs) | 9,801 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py |
class Attention(nn.Module):
def __init__(self, nx, n_positions, config, scale=False):
super().__init__()
n_state = nx # in Attention: n_state=768 (nx=n_embd)
# [switch nx => n_state from Block to Attention to keep identical to TF implementation]
if n_state % config.n_head != 0:
... | 9,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.n_head, self.split_size // self.n_head, self.pruned_heads
)
index_attn = torch.cat([index, index + self.split_size, index + (2 * self.split_size)]... | 9,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
def _attn(self, q, k, v, attention_mask=None, head_mask=None, output_attentions=False):
w = torch.matmul(q, k)
if self.scale:
w = w / math.sqrt(v.size(-1))
# w = w * self.bias + -1e9 * (1 - self.bias) # TF implementation method: mask_attn_weights
# XD: self.b may be larger t... | 9,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
def merge_heads(self, x):
x = x.permute(0, 2, 1, 3).contiguous()
new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
return x.view(*new_x_shape) # in Tensorflow implementation: fct merge_states
def split_heads(self, x, k=False):
new_x_shape = x.size()[:-1] + (self.n_head, x.si... | 9,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
a = self.merge_heads(a)
a = self.c_proj(a)
a = self.resid_dropout(a)
outputs = [a] + attn_outputs[1:]
return outputs # a, (attentions) | 9,802 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class MLP(nn.Module):
def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd)
super().__init__()
nx = config.n_embd
self.c_fc = Conv1D(n_state, nx)
self.c_proj = Conv1D(nx, n_state)
self.act = ACT_FNS[config.afn]
self.dropout = nn.Dropout(config.resi... | 9,803 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class Block(nn.Module):
def __init__(self, n_positions, config, scale=False):
super().__init__()
nx = config.n_embd
self.attn = Attention(nx, n_positions, config, scale)
self.ln_1 = nn.LayerNorm(nx, eps=config.layer_norm_epsilon)
self.mlp = MLP(4 * nx, config)
self.ln... | 9,804 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = OpenAIGPTConfig
load_tf_weights = load_tf_weights_in_openai_gpt
base_model_prefix = "transformer" | 9,805 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
def _init_weights(self, module):
"""Initialize the weights."""
if isinstance(module, (nn.Linear, Conv1D)):
# Slightly different from the TF version which uses truncated_normal for initialization
# cf https://github.com/pytorch/pytorch/pull/5617
module.weight.data.norm... | 9,805 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not. | 9,806 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mc_labels` is provided):
Multiple choice classification loss.
logits (`torch.... | 9,806 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
shape `(batch_size, sequence_length, hidden_size)`. | 9,806 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch... | 9,806 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.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}
"""
for layer, heads in heads_to_prune.items():
self.h[layer].attn.prune_heads(heads) | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
atten... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.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()
... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
# Attention mask.
if attention_mask is not None:
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more sim... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
# Since we are adding it to the raw scores before the ... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
if inputs_embeds is None:
inputs_embeds = self.tokens_embed(input_ids)
position_embeds = self.positions_embed(position_ids)
if token_type_ids is not None:
token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
token_type_embeds = self.tokens_embed(token_typ... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
outputs = block(hidden_states, attention_mask, head_mask[i], output_attentions=output_attentions)
hidden_states = outputs[0]
if output_attentions:
all_attentions = all_attentions + (outputs[1],)
hidden_states = hidden_states.view(*output_shape)
# Add last layer
... | 9,807 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
#... | 9,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attent... | 9,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_siz... | 9,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 9,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutput(
loss=loss,
logits=lm_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=t... | 9,808 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
config.num_labels = 1
self.transformer = OpenAIGPTModel(config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bia... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=OpenAIGPTDoubleHeadsModelOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
labels (`torch.LongTensor` of shape `(batch_... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
Return:
Examples:
```python
>>> from transformers import AutoTokenizer, OpenAIGPTDoubleHeadsModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("openai-community/openai-gpt")
>>> model = OpenAIGPTDoubleHeadsModel.from_pretrained("openai-community/opena... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
>>> outputs = model(input_ids, mc_token_ids=mc_token_ids)
>>> lm_logits = outputs.logits
>>> mc_logits = outputs.mc_logits
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids,
... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
lm_loss, mc_loss = None, None
if mc_labels is not None:
loss_fct = CrossEntropyLoss()
mc_loss = loss_fct(mc_logits.view(-1, mc_logits.size(-1)), mc_labels.view(-1))
if labels is not None:
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = la... | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
return OpenAIGPTDoubleHeadsModelOutput(
loss=lm_loss,
mc_loss=mc_loss,
logits=lm_logits,
mc_logits=mc_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,809 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class OpenAIGPTForSequenceClassification(OpenAIGPTPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = OpenAIGPTModel(config)
self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)
# Initial... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
@add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
transformer_outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
sel... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
return SequenceClassifierOutput(
loss=loss,
logits=pooled_logits,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
) | 9,810 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py |
class Dinov2Embeddings(nn.Module):
"""
Construct the CLS token, mask token, position and patch embeddings.
"""
def __init__(self, config: Dinov2Config) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
self.mask_token = nn.Paramete... | 9,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.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 and i... | 9,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.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_embeddings
class_pos_embed = self.position_embeddings[:, :1]
patch_pos_embed = self.... | 9,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
batch_size, _, height, width = pixel_values.shape
... | 9,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
embeddings = self.dropout(embeddings)
return embeddings | 9,811 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2PatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | 9,812 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
num_channels = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that t... | 9,812 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2SelfAttention(nn.Module):
def __init__(self, config: Dinov2Config) -> 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.hidden_size,} is not a... | 9,813 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output... | 9,813 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.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... | 9,813 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2SdpaSelfAttention(Dinov2SelfAttention):
def __init__(self, config: Dinov2Config) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob | 9,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
if output_attentions:
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once ... | 9,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
mixed_query_layer = self.query(hidden_states)
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
query_layer = self.transpose_for_scores(mixed_query_layer)
context_layer = torch.nn.functional.scaled_dot_prod... | 9,814 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2SelfOutput(nn.Module):
"""
The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: Dinov2Config) -> None:
super().__init__()
self.dense = nn.Linea... | 9,815 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Attention(nn.Module):
def __init__(self, config: Dinov2Config) -> None:
super().__init__()
self.attention = Dinov2SelfAttention(config)
self.output = Dinov2SelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: Set[int]) -> None:
if len(hea... | 9,816 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
# Update hyper params and store pruned heads
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
self.pruned_heads = self.pruned_heads.union(heads)
def for... | 9,816 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2SdpaAttention(Dinov2Attention):
def __init__(self, config: Dinov2Config) -> None:
super().__init__(config)
self.attention = Dinov2SdpaSelfAttention(config) | 9,817 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2LayerScale(nn.Module):
def __init__(self, config) -> None:
super().__init__()
self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
return hidden_state * self.lambda1 | 9,818 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> tor... | 9,819 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2MLP(nn.Module):
def __init__(self, config) -> None:
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
if isinstance(config.h... | 9,820 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2SwiGLUFFN(nn.Module):
def __init__(self, config) -> None:
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
self.weights_in... | 9,821 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Layer(nn.Module):
"""This corresponds to the Block class in the original implementation."""
def __init__(self, config: Dinov2Config) -> None:
super().__init__()
self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.attention = DINOV2_ATTENTION_CLASSE... | 9,822 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.norm1(hidden_states), # in... | 9,822 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
# second residual connection
layer_output = self.drop_path(layer_output) + hidden_states
outputs = (layer_output,) + outputs
return outputs | 9,822 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Encoder(nn.Module):
def __init__(self, config: Dinov2Config) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([Dinov2Layer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
... | 9,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
output_attentions,
)
else:
... | 9,823 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Dinov2Config
base_model_prefix = "dinov2"
main_input_name = "pixel_values"
supports_gradient_chec... | 9,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv2d)):
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
# `trunc_normal_cpu` not implemented in `half`... | 9,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
).to(module.position_embeddings.dtype) | 9,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
module.cls_token.data = nn.init.trunc_normal_(
module.cls_token.data.to(torch.float32),
mean=0.0,
std=self.config.initializer_range,
).to(module.cls_token.dtype) | 9,824 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2Model(Dinov2PreTrainedModel):
def __init__(self, config: Dinov2Config):
super().__init__(config)
self.config = config
self.embeddings = Dinov2Embeddings(config)
self.encoder = Dinov2Encoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.lay... | 9,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
@add_start_docstrings_to_model_forward(DINOV2_BASE_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
def for... | 9,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# 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_head... | 9,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
if not return_dict:
head_outputs = (sequence_output, pooled_output)
return head_outputs + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_state... | 9,825 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
class Dinov2ForImageClassification(Dinov2PreTrainedModel):
def __init__(self, config: Dinov2Config) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.dinov2 = Dinov2Model(config)
# Classifier head
self.classifier = (
nn.Linear(config.hid... | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
@add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
self,
... | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
outputs = self.dinov2(
pixel_values,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0] # batch_size, sequence_length, hidden_size
... | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
... | 9,826 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py |
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