text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
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... | class_definition | 1,020 | 2,520 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/tokenization_openai_fast.py | null | 9,800 |
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... | class_definition | 848 | 7,076 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/configuration_openai.py | null | 9,801 |
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:
... | class_definition | 5,194 | 8,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,802 |
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... | class_definition | 8,888 | 9,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,803 |
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... | class_definition | 9,341 | 10,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,804 |
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"
... | class_definition | 10,165 | 11,306 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,805 |
class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.... | class_definition | 11,320 | 13,362 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,806 |
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)
... | class_definition | 17,176 | 23,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,807 |
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)
#... | class_definition | 23,234 | 26,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,808 |
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... | class_definition | 27,090 | 32,454 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,809 |
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... | class_definition | 33,264 | 38,363 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/openai/modeling_openai.py | null | 9,810 |
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... | class_definition | 1,824 | 5,385 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,811 |
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):
... | class_definition | 5,388 | 6,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,812 |
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... | class_definition | 7,048 | 9,894 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,813 |
class Dinov2SdpaSelfAttention(Dinov2SelfAttention):
def __init__(self, config: Dinov2Config) -> None:
super().__init__(config)
self.attention_probs_dropout_prob = config.attention_probs_dropout_prob
def forward(
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_atten... | class_definition | 9,897 | 11,931 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,814 |
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... | class_definition | 12,016 | 12,668 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,815 |
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... | class_definition | 12,752 | 14,441 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,816 |
class Dinov2SdpaAttention(Dinov2Attention):
def __init__(self, config: Dinov2Config) -> None:
super().__init__(config)
self.attention = Dinov2SdpaSelfAttention(config) | class_definition | 14,529 | 14,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,817 |
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 | class_definition | 14,719 | 15,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,818 |
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... | class_definition | 16,249 | 16,729 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,819 |
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... | class_definition | 16,732 | 17,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,820 |
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... | class_definition | 17,501 | 18,210 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,821 |
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... | class_definition | 18,309 | 20,284 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,822 |
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,
... | class_definition | 20,366 | 22,296 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,823 |
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... | class_definition | 22,299 | 23,987 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,824 |
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... | class_definition | 27,274 | 30,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,825 |
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... | class_definition | 30,875 | 34,660 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,826 |
class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = Dinov2Embeddings(config)
se... | class_definition | 34,807 | 38,683 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_dinov2.py | null | 9,827 |
class Dinov2Config(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Dinov2Model`]. It is used to instantiate an
Dinov2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the default... | class_definition | 1,009 | 7,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py | null | 9,828 |
class Dinov2OnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)... | class_definition | 7,641 | 8,040 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/configuration_dinov2.py | null | 9,829 |
class FlaxDinov2PatchEmbeddings(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
image_size = self.config.image_size
patch_size = self.config.patch_size
image_size = image_size if isinstance(image_size, collections.abc.... | class_definition | 4,268 | 5,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,830 |
class FlaxDinov2Embeddings(nn.Module):
"""Construct the CLS token, position and patch embeddings."""
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.cls_token = self.param(
"cls_token",
jax.nn.initializers.varian... | class_definition | 5,851 | 9,489 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,831 |
class FlaxDinov2SelfAttention(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0:
raise ValueError(
"`config.hidden_size`: {self.config.hidden_... | class_definition | 9,586 | 12,418 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,832 |
class FlaxDinov2SelfOutput(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.variance_scaling(
self.config.initializ... | class_definition | 12,512 | 13,242 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,833 |
class FlaxDinov2Attention(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxDinov2SelfAttention(self.config, dtype=self.dtype)
self.output = FlaxDinov2SelfOutput(self.config, dtype=self.dtype)
def __call__(self, hidden_states, determi... | class_definition | 13,335 | 14,084 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,834 |
class FlaxDinov2LayerScale(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.lambda1 = self.config.layerscale_value * self.param(
"lambda1",
jax.nn.initializers.ones,
(self.config.hidden_size,),
... | class_definition | 14,190 | 14,670 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,835 |
class FlaxDinov2DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
rate: float
@nn.module.compact
def __call__(self, inputs, deterministic: Optional[bool] = True):
if self.rate == 0.0:
return inputs
keep_prob ... | class_definition | 14,768 | 15,565 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,836 |
class FlaxDinov2MLP(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.fc1 = nn.Dense(
self.config.hidden_size * self.config.mlp_ratio,
kernel_init=jax.nn.initializers.variance_scaling(
self.c... | class_definition | 15,568 | 16,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,837 |
class FlaxDinov2SwiGLUFFN(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
hidden_features = int(self.config.hidden_size * self.config.mlp_ratio)
hidden_features = (int(self.hidden_features * 2 / 3) + 7) // 8 * 8
self.... | class_definition | 16,643 | 17,707 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,838 |
class FlaxDinov2Layer(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.norm1 = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
self.attention = FlaxDinov2Attention(self.config, dtype=self.dtype)
... | class_definition | 17,710 | 19,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,839 |
class FlaxDinov2LayerCollection(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxDinov2Layer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.num_hidden_layers)
]
def _... | class_definition | 19,651 | 21,054 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,840 |
class FlaxDinov2Encoder(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layer = FlaxDinov2LayerCollection(self.config, dtype=self.dtype)
def __call__(
self,
hidden_states,
deterministic: bool = True,
... | class_definition | 21,145 | 21,836 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,841 |
class FlaxDinov2PreTrainedModel(FlaxPreTrainedModel):
"""
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"
module_class: ... | class_definition | 21,839 | 24,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,842 |
class FlaxDinov2Module(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.embeddings = FlaxDinov2Embeddings(self.config, dtype=self.dtype)
self.encoder = FlaxDinov2Encoder(self.config, dtype=self.dtype)
self.layernor... | class_definition | 24,972 | 26,472 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,843 |
class FlaxDinov2Model(FlaxDinov2PreTrainedModel):
module_class = FlaxDinov2Module | class_definition | 26,632 | 26,717 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,844 |
class FlaxDinov2ForImageClassificationModule(nn.Module):
config: Dinov2Config
dtype: jnp.dtype = jnp.float32
def setup(self):
self.dinov2 = FlaxDinov2Module(config=self.config, dtype=self.dtype)
self.classifier = nn.Dense(
self.config.num_labels,
dtype=self.dtype,
... | class_definition | 27,584 | 29,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,845 |
class FlaxDinov2ForImageClassification(FlaxDinov2PreTrainedModel):
module_class = FlaxDinov2ForImageClassificationModule | class_definition | 29,374 | 29,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/dinov2/modeling_flax_dinov2.py | null | 9,846 |
class LiltConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LiltModel`]. It is used to instantiate a LiLT
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | class_definition | 775 | 6,693 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/configuration_lilt.py | null | 9,847 |
class LiltTextEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
... | class_definition | 1,442 | 5,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,848 |
class LiltLayoutEmbeddings(nn.Module):
def __init__(self, config):
super().__init__()
# we divide the hidden_size by 6 here as there are 6 different layout embeddings,
# namely left_position, upper_position, right_position, lower_position, height, width
self.x_position_embeddings = n... | class_definition | 5,406 | 8,265 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,849 |
class LiltSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=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}) i... | class_definition | 8,268 | 15,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,850 |
class LiltSelfOutput(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 f... | class_definition | 15,495 | 16,101 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,851 |
class LiltAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = LiltSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = LiltSelfOutput(config)
self.pruned_heads = set()
ori_hidden_size ... | class_definition | 16,104 | 18,344 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,852 |
class LiltIntermediate(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.interm... | class_definition | 18,417 | 18,982 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,853 |
class LiltOutput(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... | class_definition | 19,049 | 19,657 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,854 |
class LiltLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = LiltAttention(config)
self.intermediate = LiltIntermediate(config)
self.output = LiltOutput(c... | class_definition | 19,660 | 22,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,855 |
class LiltEncoder(nn.Module):
# Copied from transformers.models.bert.modeling_bert.BertEncoder.__init__ with Bert->Lilt
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([LiltLayer(config) for _ in range(config.num_hidden_layers)])
sel... | class_definition | 22,192 | 24,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,856 |
class LiltPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the hidde... | class_definition | 24,816 | 25,375 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,857 |
class LiltPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LiltConfig
base_model_prefix = "lilt"
supports_gradient_checkpointing = True
_no_split_modules = []... | class_definition | 25,378 | 26,600 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,858 |
class LiltModel(LiltPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.embeddings = LiltTextEmbeddings(config)
self.layout_embeddings = LiltLayoutEmbeddings(config)
self.encoder = LiltEncoder(config)
... | class_definition | 30,768 | 36,897 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,859 |
class LiltForSequenceClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForSequenceClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.c... | class_definition | 37,119 | 42,014 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,860 |
class LiltForTokenClassification(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForTokenClassification.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt =... | class_definition | 42,243 | 46,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,861 |
class LiltClassificationHead(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... | class_definition | 46,262 | 47,032 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,862 |
class LiltForQuestionAnswering(LiltPreTrainedModel):
# Copied from transformers.models.roberta.modeling_roberta.RobertaForQuestionAnswering.__init__ with Roberta->Lilt, roberta->lilt
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.lilt = Lil... | class_definition | 47,319 | 52,703 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/lilt/modeling_lilt.py | null | 9,863 |
class LlamaTokenizer(PreTrainedTokenizer):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` ... | class_definition | 2,091 | 18,634 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama.py | null | 9,864 |
class LlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 1,076 | 11,732 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/configuration_llama.py | null | 9,865 |
class FlaxLlamaRMSNorm(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.epsilon = self.config.rms_norm_eps
self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size)
def __call__(self, hidden_states):
varian... | class_definition | 7,414 | 8,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,866 |
class FlaxLlamaRotaryEmbedding(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
head_dim = self.config.hidden_size // self.config.num_attention_heads
self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
def __call__(sel... | class_definition | 8,109 | 8,809 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,867 |
class FlaxLlamaAttention(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.hea... | class_definition | 8,812 | 15,617 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,868 |
class FlaxLlamaMLP(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim
kernel_init = jax.nn.initializers.normal(... | class_definition | 15,620 | 16,585 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,869 |
class FlaxLlamaDecoderLayer(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.input_layernorm = FlaxLlamaRMSNorm(self.config, dtype=self.dtype)
self.self_attn = FlaxLlamaAttention(self.config, dtype=self.dtype)
self.post_attention_layernorm = FlaxL... | class_definition | 16,588 | 18,001 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,870 |
class FlaxLlamaPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LlamaConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(
... | class_definition | 18,149 | 23,462 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,871 |
class FlaxLlamaLayerCollection(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxLlamaDecoderLayer(self.config, dtype=self.dtype, name=str(i))
for i in range(self.config.num_hidden_layers)
]
def __call__(
... | class_definition | 23,465 | 24,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,872 |
class FlaxLlamaModule(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.hidden_size = self.config.hidden_size
embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
self.embed_tokens = nn.Embed(
self.config.vo... | class_definition | 24,943 | 26,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,873 |
class FlaxLlamaModel(FlaxLlamaPreTrainedModel):
module_class = FlaxLlamaModule | class_definition | 26,993 | 27,075 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,874 |
class FlaxLlamaForCausalLMModule(nn.Module):
config: LlamaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxLlamaModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,... | class_definition | 27,249 | 28,615 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,875 |
class FlaxLlamaForCausalLM(FlaxLlamaPreTrainedModel):
module_class = FlaxLlamaForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values ... | class_definition | 28,867 | 30,392 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_flax_llama.py | null | 9,876 |
class Llama3Converter(TikTokenConverter):
def __init__(self, vocab_file, special_tokens=None, instruct=False, llama_version="3.2", **kwargs):
super().__init__(vocab_file, additional_special_tokens=special_tokens, **kwargs)
tokenizer = self.converted()
# References for chat templates in inst... | class_definition | 16,966 | 19,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/convert_llama_weights_to_hf.py | null | 9,877 |
class LlamaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no normalization.
```python
>>> from transformers import LlamaTokenizerFast
>>> tokenizer = LlamaTokenizerFast.from_pretrained("hf-int... | class_definition | 1,817 | 11,146 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/tokenization_llama_fast.py | null | 9,878 |
class LlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
LlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | class_definition | 2,083 | 2,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,879 |
class LlamaRotaryEmbedding(nn.Module):
def __init__(self, config: LlamaConfig, 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_type", co... | class_definition | 2,850 | 6,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,880 |
class LlamaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
... | class_definition | 7,773 | 8,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,881 |
class LlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: LlamaConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hid... | class_definition | 10,025 | 13,592 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,882 |
class LlamaDecoderLayer(nn.Module):
def __init__(self, config: LlamaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = LlamaAttention(config=config, layer_idx=layer_idx)
self.mlp = LlamaMLP(config)
self.input_layernorm = Llama... | class_definition | 13,595 | 15,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,883 |
class LlamaPreTrainedModel(PreTrainedModel):
config_class = LlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlamaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | class_definition | 16,687 | 17,610 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,884 |
class LlamaModel(LlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
Args:
config: LlamaConfig
"""
def __init__(self, config: LlamaConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 22,414 | 33,639 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,885 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 33,642 | 33,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,886 |
class LlamaForCausalLM(LlamaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = LlamaModel(config)
self.vocab_size = config.vocab_size
self.lm_head ... | class_definition | 33,707 | 38,834 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,887 |
class LlamaForSequenceClassification(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights a... | class_definition | 39,627 | 43,439 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,888 |
class LlamaForQuestionAnswering(LlamaPreTrainedModel):
base_model_prefix = "transformer"
# Copied from transformers.models.bloom.modeling_bloom.BloomForQuestionAnswering.__init__ with Bloom->Llama
def __init__(self, config):
super().__init__(config)
self.transformer = LlamaModel(config)
... | class_definition | 43,735 | 47,239 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,889 |
class LlamaForTokenClassification(LlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = LlamaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classi... | class_definition | 47,486 | 50,698 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llama/modeling_llama.py | null | 9,890 |
class WhisperTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" Whisper tokenizer (backed by HuggingFace's *tokenizers* library).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information rega... | class_definition | 1,392 | 30,205 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/tokenization_whisper_fast.py | null | 9,891 |
class WhisperProcessor(ProcessorMixin):
r"""
Constructs a Whisper processor which wraps a Whisper feature extractor and a Whisper tokenizer into a single
processor.
[`WhisperProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and [`WhisperTokenizer`]. See
the [`~WhisperProcess... | class_definition | 698 | 3,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/processing_whisper.py | null | 9,892 |
class WhisperPositionalEmbedding(nn.Embedding):
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
super().__init__(num_positions, embedding_dim)
def forward(self, input_ids, past_key_values_length=0, position_ids=None):
if position_ids is None:
... | class_definition | 8,451 | 8,925 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,893 |
class WhisperAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | class_definition | 8,928 | 14,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,894 |
class WhisperFlashAttention2(WhisperAttention):
"""
Whisper flash attention module. This module inherits from `WhisperAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention and deal... | class_definition | 14,748 | 21,148 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,895 |
class WhisperSdpaAttention(WhisperAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optiona... | class_definition | 21,151 | 26,540 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,896 |
class WhisperEncoderLayer(nn.Module):
def __init__(self, config: WhisperConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_h... | class_definition | 26,800 | 29,941 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,897 |
class WhisperDecoderLayer(nn.Module):
def __init__(self, config: WhisperConfig, layer_idx: int = None):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = WHISPER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=con... | class_definition | 29,944 | 35,487 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,898 |
class WhisperPreTrainedModel(PreTrainedModel):
config_class = WhisperConfig
base_model_prefix = "model"
main_input_name = "input_features"
supports_gradient_checkpointing = True
_no_split_modules = ["WhisperEncoderLayer", "WhisperDecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = ... | class_definition | 35,490 | 36,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/whisper/modeling_whisper.py | null | 9,899 |
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