text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
outputs = self.roberta(
flat_input_ids,
lang_ids=flat_lang_ids,
position_ids=flat_position_ids,
token_type_ids=flat_token_type_ids,
attention_mask=flat_attention_mask,
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
o... | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
return MultipleChoiceModelOutput(
loss=loss,
logits=reshaped_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,539 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForTokenClassification(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XmodModel(c... | 9,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids... | 9,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 9,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = self.roberta(
input_ids,
lang_ids=lang_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_attent... | 9,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 9,540 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
classifier_dropout = (
config.classifier_dropout if config.classifier_dr... | 9,541 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodForQuestionAnswering(XmodPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Xmod
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.roberta = XmodModel(confi... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
@add_start_docstrings_to_model_forward(XMOD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
lang_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.Lo... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
outputs = self.roberta(
input_ids,
lang_ids=lang_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_attent... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if len(start_positions.size()) > 1:
start_positions = start_positions.squeeze(-1)
if len(end_positions.size()) > 1:
... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
... | 9,542 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/modeling_xmod.py |
class XmodConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`XmodModel`]. It is used to instantiate an X-MOD
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar con... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the X-MOD model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`XmodModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensi... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings,... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
Typ... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
Whether to reuse the second layer normalization and apply it before the adapter modules as well.
ln_before_adapter (`bool`, *optional*, defaults to `True`):
Whether to apply the layer normalization before the residual connection around the adapter module.
languages (`Iterable[str]`, *optiona... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
Examples:
```python
>>> from transformers import XmodConfig, XmodModel
>>> # Initializing an X-MOD facebook/xmod-base style configuration
>>> configuration = XmodConfig()
>>> # Initializing a model (with random weights) from the facebook/xmod-base style configuration
>>> model = XmodModel(con... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
def __init__(
self,
vocab_size=30522,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
... | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
self.languages = list(languages)
self.default_language = default_language | 9,543 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
class XmodOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | 9,544 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xmod/configuration_xmod.py |
class VitPoseBackboneConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitPoseBackbone`]. It is used to instantiate a
VitPose model according to the specified arguments, defining the model architecture. Instantiating a configuration
wit... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
Args:
image_size (`int`, *optional*, defaults to `[256, 192]`):
The size (resolution) of each image.
patch_size (`List[int]`, *optional*, defaults to `[16, 16]`):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number ... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
The number of experts in the MoE layer.
part_features (`int`, *optional*):
The number of part features to output. Only used in case `num_experts` is greater than 1.
hidden_act (`str`, *optional*, defaults to `"gelu"`):
The non-linear activation function in the encoder and pooler.... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
The epsilon used by the layer normalization layers.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether to add a bias to the queries, keys and values.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute. | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
Example:
```python
>>> from transformers import VitPoseBackboneConfig, VitPoseBackbone
>>> # Initializing a VitPose configuration
>>> configuration = VitPoseBackboneConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = VitPoseBackbone(configuration)
... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
def __init__(
self,
image_size=[256, 192],
patch_size=[16, 16],
num_channels=3,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
mlp_ratio=4,
num_experts=1,
part_features=256,
hidden_act="gelu",
hidden_dropout_... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.mlp_ratio = mlp_ratio
self.num_experts = num_experts
self.part_features = part_features
self.hidden_act = hidden_act
self.hidden_dropout_p... | 9,545 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/configuration_vitpose_backbone.py |
class VitPoseBackbonePatchEmbeddings(nn.Module):
"""Image to Patch Embedding."""
def __init__(self, config):
super().__init__()
image_size = config.image_size
patch_size = config.patch_size
num_channels = config.num_channels
embed_dim = config.hidden_size
image... | 9,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
height, width = pixel_values.shape[-2:]
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image... | 9,546 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneEmbeddings(nn.Module):
"""
Construct the position and patch embeddings.
"""
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.patch_embeddings = VitPoseBackbonePatchEmbeddings(config)
num_patches = self.patch_embeddings.num_... | 9,547 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneSelfAttention(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> 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.hidd... | 9,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
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(
s... | 9,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.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... | 9,548 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneSelfOutput(nn.Module):
"""
The residual connection is defined in VitPoseBackboneLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
... | 9,549 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneAttention(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.attention = VitPoseBackboneSelfAttention(config)
self.output = VitPoseBackboneSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads: S... | 9,550 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.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... | 9,550 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneMoeMLP(nn.Module):
def __init__(self, config: VitPoseBackboneConfig):
super().__init__()
in_features = out_features = config.hidden_size
hidden_features = int(config.hidden_size * config.mlp_ratio)
num_experts = config.num_experts
part_features = config... | 9,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
hidden_state = self.fc1(hidden_state)
hidden_state = self.act(hidden_state)
shared_hidden_state = self.fc2(hidden_state)
indices = indices.view(-1, 1, 1)
# to support ddp training
for i in range(self.num_experts):
selected_index = indices == i
current_hid... | 9,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneMLP(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> 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... | 9,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneLayer(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.num_experts = config.num_experts
self.attention = VitPoseBackboneAttention(config)
self.mlp = VitPoseBackboneMLP(config) if self.num_experts == 1 else VitPoseBack... | 9,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
def forward(
self,
hidden_states: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
# Validate dataset_index when u... | 9,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights | 9,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
# first residual connection
hidden_states = attention_output + hidden_states
layer_output = self.layernorm_after(hidden_states)
if self.num_experts == 1:
layer_output = self.mlp(layer_output)
else:
layer_output = self.mlp(layer_output, indices=dataset_index)
... | 9,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackboneEncoder(nn.Module):
def __init__(self, config: VitPoseBackboneConfig) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList([VitPoseBackboneLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
# I... | 9,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
dataset_index,
... | 9,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
) | 9,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackbonePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VitPoseBackboneConfig
base_model_prefix = "vit"
main_input_name = "pixel_values"
support... | 9,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm, VitPoseBackboneEmbeddings]) -> 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... | 9,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
std=self.config.initializer_range,
).to(module.position_embeddings.dtype) | 9,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class VitPoseBackbone(VitPoseBackbonePreTrainedModel, BackboneMixin):
def __init__(self, config: VitPoseBackboneConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = Vi... | 9,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
@add_start_docstrings_to_model_forward(VITPOSE_BACKBONE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
dataset_index: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Te... | 9,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
>>> pixel_values = torch.randn(1, 3, 256, 192)
>>> dataset_index = torch.tensor([1])
>>> outputs = model(pixel_values, dataset_index)
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
... | 9,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
outputs = self.encoder(
embedding_output,
dataset_index=dataset_index,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=True,
return_dict=return_dict,
)
hidden_states = outputs.hidden_states if return_d... | 9,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
) | 9,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vitpose_backbone/modeling_vitpose_backbone.py |
class PersimmonConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an
Persimmon model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
Args:
vocab_size (`int`, *optional*, defaults to 262144):
Vocabulary size of the Persimmon model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`PersimmonModel`]
hidden_size (`int`, *optional*, defaults to 4096):
... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-5):
... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
accordingly.
Expected contents:
`rope_type` (`str`):
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computation. If unspecified, it defaults to value recommended by the implementation, using the
`factor` field to infer the suggested va... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to l... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
Whether or not to normalize the Queries and Keys after projecting the hidden states
hidden_dropout (`float`, *optional*, default to 0.0):
The dropout ratio after applying the MLP to the hidden states.
attention_dropout (`float`, *optional*, default to 0.0):
The dropout ratio afte... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
Example:
```python
>>> from transformers import PersimmonModel, PersimmonConfig
>>> # Initializing a Persimmon persimmon-7b style configuration
>>> configuration = PersimmonConfig()
```"""
model_type = "persimmon"
keys_to_ignore_at_inference = ["past_key_values"] | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
def __init__(
self,
vocab_size=262144,
hidden_size=4096,
intermediate_size=16384,
num_hidden_layers=36,
num_attention_heads=64,
hidden_act="relu2",
max_position_embeddings=16384,
initializer_range=0.02,
layer_norm_eps=1e-5,
use_cach... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.qk_layernorm = qk_layernorm
self.hidden_dropout = hidden_dropout
self.attention_dropout = ... | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 9,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/configuration_persimmon.py |
class PersimmonRotaryEmbedding(nn.Module):
def __init__(self, config: PersimmonConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_t... | 9,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
def _dynamic_frequency_update(self, position_ids, device):
"""
dynamic RoPE layers should recompute `inv_freq` in the following situations:
1 - growing beyond the cached sequence length (allow scaling)
2 - the current sequence length is in the original scale (avoid losing precision with ... | 9,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: # reset
# This .to() is needed if the model has been moved to a device after being initialized (because
# the buffer is automatically moved, but not the original copy)
self.original_inv_f... | 9,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
position_ids_expanded = position_ids[:, None, :].float()
# Force float32 (see https://github.com/huggingface/transformers/pull/29285)
device_type = x.device.type
device... | 9,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size)
self.act = ACT2FN[config.hidden_act]
def forwar... | 9,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: PersimmonConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.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.query_key_value = nn.Linear(self.hidden_size, 3 *... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
"""
Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
storage as `fused_qkv`
Args:
fused_qkv (`torch.tensor`): [batch_si... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool = False,
use_cache: bool = False,
cache_position... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
# [batch_size, num_heads, seq_length, head_dim] -> [batch_size, seq_length, num_heads, head_dim]
query_states = query_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
cos, sin = position_embeddings
# Partial rotary embed... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if past_key_value is not None:
# Specific to RoPE models with partial rotation
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_ndims,
"cache_position": cache_position,
}
key_stat... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2).contiguou... | 9,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonDecoderLayer(nn.Module):
def __init__(self, config: PersimmonConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = PersimmonAttention(config=config, layer_idx=layer_idx)
self.mlp = PersimmonMLP(config)
self.input_lay... | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[boo... | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
`[0, config.n_positions - 1]`.
[What are position IDs?](../glossary#position-ids)
past_key_value (`Tuple(torch.FloatTensor)`, *optional*):
cached past key and ... | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
with `head_dim` being the embedding dimension of each attention head.
""" | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
... | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if use_cache:
outputs += (present_key_value,)
return outputs | 9,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonPreTrainedModel(PreTrainedModel):
config_class = PersimmonConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["PersimmonDecoderLayer"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_quantized_ca... | 9,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
class PersimmonModel(PersimmonPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PersimmonDecoderLayer`]
Args:
config: PersimmonConfig
"""
def __init__(self, config: PersimmonConfig):
super().__init__(config)
self.pa... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
@add_start_docstrings_to_model_forward(PERSIMMON_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_ca... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.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... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if cache_position is None:
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
cache_position = torch.arange(
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is No... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,) | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
if use_cache:
next_decoder_cache = layer_outputs[2 if output_attentions else 1]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.final_layernorm(hidden_states)
# add hidden states from the last decoder layer
if output... | 9,563 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/persimmon/modeling_persimmon.py |
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