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 FlaxLongT5BlockCollection(nn.Module):
config: LongT5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.causal = self.config.causal
if self.gradient_checkpointing:
FlaxLongT5CheckpointLayer = ... | class_definition | 60,180 | 63,405 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,400 |
class FlaxLongT5Stack(nn.Module):
config: LongT5Config
embed_tokens: nn.Embed
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def setup(self):
self.causal = self.config.causal
self.block = FlaxLongT5BlockCollection(
se... | class_definition | 63,490 | 65,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,401 |
class FlaxLongT5PreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LongT5Config
base_model_prefix = "transformer"
module_class: nn.Module = None
def __init... | class_definition | 73,721 | 86,140 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,402 |
class FlaxLongT5Module(nn.Module):
config: LongT5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
def setup(self):
... | class_definition | 88,999 | 92,092 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,403 |
class FlaxLongT5Model(FlaxLongT5PreTrainedModel):
module_class = FlaxLongT5Module | class_definition | 92,177 | 92,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,404 |
class FlaxLongT5ForConditionalGenerationModule(nn.Module):
config: LongT5Config
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
gradient_checkpointing: bool = False
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
... | class_definition | 93,498 | 97,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,405 |
class FlaxLongT5ForConditionalGeneration(FlaxLongT5PreTrainedModel):
module_class = FlaxLongT5ForConditionalGenerationModule
@add_start_docstrings(LONGT5_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=LongT5Config)
def decode(
... | class_definition | 97,555 | 104,660 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | null | 4,406 |
class Phi3MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self... | class_definition | 1,486 | 2,133 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,407 |
class Phi3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_d... | class_definition | 2,136 | 5,860 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,408 |
class Phi3DecoderLayer(MistralDecoderLayer):
def __init__(self, config: Phi3Config, layer_idx: int):
super().__init__(config, layer_idx)
self.config = config
self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx)
self.mlp = Phi3MLP(config)
self.resid_attn_dropout ... | class_definition | 5,863 | 9,466 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,409 |
class Phi3RotaryEmbedding(MistralRotaryEmbedding):
def __init__(self, config: Phi3Config, device=None):
super().__init__(config, device)
def _longrope_frequency_update(self, position_ids, device):
"""Longrope uses long factor if sequence is larger than original pretraining length, short otherwi... | class_definition | 9,469 | 12,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,410 |
class Phi3PreTrainedModel(MistralPreTrainedModel):
_version = "0.0.5" | class_definition | 12,056 | 12,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,411 |
class Phi3ForCausalLM(MistralForCausalLM, Phi3PreTrainedModel):
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
num_logit... | class_definition | 12,132 | 13,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,412 |
class Phi3ForSequenceClassification(MistralForSequenceClassification):
pass | class_definition | 13,659 | 13,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,413 |
class Phi3ForTokenClassification(MistralForTokenClassification):
pass | class_definition | 13,741 | 13,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modular_phi3.py | null | 4,414 |
class Phi3MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.gate_up_proj = nn.Linear(config.hidden_size, 2 * config.intermediate_size, bias=False)
self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
self... | class_definition | 2,516 | 3,163 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,415 |
class Phi3Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Phi3Config, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_d... | class_definition | 6,442 | 10,166 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,416 |
class Phi3RMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Phi3RMSNorm 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 | 10,169 | 10,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,417 |
class Phi3DecoderLayer(nn.Module):
def __init__(self, config: Phi3Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Phi3Attention(config=config, layer_idx=layer_idx)
self.mlp = Phi3MLP(config)
self.input_layernorm = Phi3RMSNorm... | class_definition | 10,890 | 14,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,418 |
class Phi3RotaryEmbedding(nn.Module):
def __init__(self, config: Phi3Config, 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", conf... | class_definition | 14,700 | 19,201 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,419 |
class Phi3PreTrainedModel(PreTrainedModel):
config_class = Phi3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Phi3DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_support... | class_definition | 20,219 | 21,162 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,420 |
class Phi3Model(Phi3PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Phi3DecoderLayer`]
Args:
config: Phi3Config
"""
def __init__(self, config: Phi3Config):
super().__init__(config)
self.padding_idx = config.pad_to... | class_definition | 25,963 | 39,109 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,421 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 39,112 | 39,174 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,422 |
class Phi3ForCausalLM(Phi3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Phi3Model(config)
self.vocab_size = config.vocab_size
self.lm_head = n... | class_definition | 39,177 | 45,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,423 |
class Phi3ForSequenceClassification(Phi3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Phi3Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights and ... | class_definition | 46,532 | 50,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,424 |
class Phi3ForTokenClassification(Phi3PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Phi3Model(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classifie... | class_definition | 50,585 | 53,793 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/modeling_phi3.py | null | 4,425 |
class Phi3Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar conf... | class_definition | 797 | 10,608 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/phi3/configuration_phi3.py | null | 4,426 |
class TFRotaryEmbedding(keras.layers.Layer):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int, n... | class_definition | 2,431 | 4,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,427 |
class TFEsmContactPredictionHead(keras.layers.Layer):
"""Performs symmetrization, apc, and computes a logistic regression on the output features"""
def __init__(
self,
in_features: int,
bias=True,
eos_idx: int = 2,
name=None,
):
super().__init__(name=name)
... | class_definition | 4,485 | 6,099 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,428 |
class TFEsmEmbeddings(keras.layers.Layer):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config, name=None):
super().__init__(name=name)
self.word_embeddings = keras.layers.Embedding(
config.vocab_size,
co... | class_definition | 6,102 | 11,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,429 |
class TFEsmSelfAttention(keras.layers.Layer):
def __init__(self, config, position_embedding_type=None, name=None):
super().__init__(name=name)
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden... | class_definition | 11,579 | 20,539 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,430 |
class TFEsmSelfOutput(keras.layers.Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Drop... | class_definition | 20,542 | 21,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,431 |
class TFEsmAttention(keras.layers.Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.self = TFEsmSelfAttention(config, name="self")
self.output_layer = TFEsmSelfOutput(config, name="output")
self.pruned_heads = set()
self.LayerNorm = keras.layers.... | class_definition | 21,461 | 23,338 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,432 |
class TFEsmIntermediate(keras.layers.Layer):
def __init__(self, config: EsmConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.intermediate_size,
kernel_initializer=get_initializer(config.initializer_range),
name="dense",
... | class_definition | 23,341 | 24,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,433 |
class TFEsmOutput(keras.layers.Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.dropout = keras.layers.Dropout(... | class_definition | 24,179 | 25,097 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,434 |
class TFEsmLayer(keras.layers.Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = TFEsmAttention(config, name="attention")
self.is_decoder = config.is_de... | class_definition | 25,100 | 29,781 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,435 |
class TFEsmEncoder(keras.layers.Layer):
def __init__(self, config, name=None):
super().__init__(name=name)
self.config = config
self.layer = [TFEsmLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
self.emb_layer_norm_after = keras.layers.LayerNormalization(... | class_definition | 29,784 | 33,248 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,436 |
class TFEsmPooler(keras.layers.Layer):
def __init__(self, config: EsmConfig, **kwargs):
super().__init__(**kwargs)
self.dense = keras.layers.Dense(
units=config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
... | class_definition | 33,335 | 34,302 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,437 |
class TFEsmPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EsmConfig
base_model_prefix = "esm" | class_definition | 34,305 | 34,556 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,438 |
class TFEsmMainLayer(keras.layers.Layer):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/ab... | class_definition | 37,790 | 49,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,439 |
class TFEsmModel(TFEsmPreTrainedModel):
def __init__(self, config: EsmConfig, add_pooling_layer=True, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.esm = TFEsmMainLayer(config, add_pooling_layer=add_pooling_layer, name="esm")
@unpack_inputs
@add_start_docstrings_to_m... | class_definition | 49,277 | 53,199 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,440 |
class TFEsmForMaskedLM(TFEsmPreTrainedModel, TFMaskedLanguageModelingLoss):
_keys_to_ignore_on_load_missing = [r"position_ids"]
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
... | class_definition | 53,302 | 57,873 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,441 |
class TFEsmLMHead(keras.layers.Layer):
"""ESM Head for masked language modeling."""
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
... | class_definition | 57,876 | 60,064 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,442 |
class TFEsmForSequenceClassification(TFEsmPreTrainedModel, TFSequenceClassificationLoss):
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.esm = TFEsmMainLayer(... | class_definition | 60,284 | 63,424 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,443 |
class TFEsmForTokenClassification(TFEsmPreTrainedModel, TFTokenClassificationLoss):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids"]
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.esm... | class_definition | 63,651 | 66,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,444 |
class TFEsmClassificationHead(keras.layers.Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config, name=None):
super().__init__(name=name)
self.dense = keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initia... | class_definition | 66,842 | 68,308 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_tf_esm.py | null | 4,445 |
class EsmConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ESMModel`]. It is used to instantiate a ESM model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | class_definition | 880 | 8,354 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/configuration_esm.py | null | 4,446 |
class EsmFoldConfig:
esm_type: str = None
fp16_esm: bool = True
use_esm_attn_map: bool = False
esm_ablate_pairwise: bool = False
esm_ablate_sequence: bool = False
esm_input_dropout: float = 0
embed_aa: bool = True
bypass_lm: bool = False
lddt_head_hid_dim: int = 128
trunk: "Tru... | class_definition | 8,368 | 9,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/configuration_esm.py | null | 4,447 |
class TrunkConfig:
num_blocks: int = 48
sequence_state_dim: int = 1024
pairwise_state_dim: int = 128
sequence_head_width: int = 32
pairwise_head_width: int = 32
position_bins: int = 32
dropout: float = 0
layer_drop: float = 0
cpu_grad_checkpoint: bool = False
max_recycles: int = ... | class_definition | 9,291 | 12,172 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/configuration_esm.py | null | 4,448 |
class StructureModuleConfig:
"""
Args:
sequence_dim:
Single representation channel dimension
pairwise_dim:
Pair representation channel dimension
ipa_dim:
IPA hidden channel dimension
resnet_dim:
Angle resnet (Alg. 23 lines 11-14) hi... | class_definition | 12,186 | 13,875 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/configuration_esm.py | null | 4,449 |
class EsmTokenizer(PreTrainedTokenizer):
"""
Constructs an ESM tokenizer.
"""
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
unk_token="<unk>",
cls_token="<cls>",
pad_token="<pad>... | class_definition | 1,043 | 5,355 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/tokenization_esm.py | null | 4,450 |
class RotaryEmbedding(torch.nn.Module):
"""
Rotary position embeddings based on those in
[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer). Query and keys are transformed by rotation
matrices which depend on their relative positions.
"""
def __init__(self, dim: int):
... | class_definition | 2,385 | 4,182 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,451 |
class EsmContactPredictionHead(nn.Module):
"""Performs symmetrization, apc, and computes a logistic regression on the output features"""
def __init__(
self,
in_features: int,
bias=True,
eos_idx: int = 2,
):
super().__init__()
self.in_features = in_features
... | class_definition | 4,185 | 5,584 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,452 |
class EsmEmbeddings(nn.Module):
"""
Same as BertEmbeddings with a tiny tweak for positional embeddings indexing.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
if con... | class_definition | 5,587 | 10,053 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,453 |
class EsmSelfAttention(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}) is... | class_definition | 10,056 | 17,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,454 |
class EsmSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_s... | class_definition | 17,818 | 18,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,455 |
class EsmAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = EsmSelfAttention(config)
self.output = EsmSelfOutput(config)
self.pruned_heads = set()
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def prune_heads(s... | class_definition | 18,281 | 20,181 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,456 |
class EsmIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = gelu(hi... | class_definition | 20,184 | 20,545 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,457 |
class EsmOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden... | class_definition | 20,548 | 21,010 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,458 |
class EsmLayer(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 = EsmAttention(config)
self.is_decoder = config.is_decoder
self.add_cross_attention = config.add... | class_definition | 21,013 | 24,715 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,459 |
class EsmEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([EsmLayer(config) for _ in range(config.num_hidden_layers)])
self.emb_layer_norm_after = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
sel... | class_definition | 24,718 | 28,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,460 |
class EsmPooler(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 hidden... | class_definition | 28,505 | 29,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,461 |
class EsmPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = EsmConfig
base_model_prefix = "esm"
supports_gradient_checkpointing = True
_no_split_modules = ["Esm... | class_definition | 29,066 | 30,351 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,462 |
class EsmModel(EsmPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706... | class_definition | 33,634 | 43,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,463 |
class EsmForMaskedLM(EsmPreTrainedModel):
_tied_weights_keys = ["lm_head.decoder.weight"]
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `EsmForMaskedLM` make sure `config.is_decoder=False` for "
... | class_definition | 43,253 | 46,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,464 |
class EsmLMHead(nn.Module):
"""ESM Head for masked language modeling."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.decoder = nn... | class_definition | 46,909 | 47,593 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,465 |
class EsmForSequenceClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.esm = EsmModel(config, add_pooling_layer=False)
self.classifier = EsmClassificationHead(config)
... | class_definition | 47,813 | 51,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,466 |
class EsmForTokenClassification(EsmPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.esm = EsmModel(config, add_pooling_layer=False)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linea... | class_definition | 51,676 | 54,267 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,467 |
class EsmClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Line... | class_definition | 54,270 | 54,895 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esm.py | null | 4,468 |
class EsmForProteinFoldingOutput(ModelOutput):
"""
Output type of [`EsmForProteinFoldingOutput`].
Args:
frames (`torch.FloatTensor`):
Output frames.
sidechain_frames (`torch.FloatTensor`):
Output sidechain frames.
unnormalized_angles (`torch.FloatTensor`):
... | class_definition | 1,718 | 5,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,469 |
class EsmFoldLinear(nn.Linear):
"""
A Linear layer with built-in nonstandard initializations. Called just like torch.nn.Linear.
Implements the initializers in 1.11.4, plus some additional ones found in the code.
"""
def __init__(
self,
in_dim: int,
out_dim: int,
bia... | class_definition | 10,563 | 12,209 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,470 |
class EsmFoldLayerNorm(nn.Module):
def __init__(self, c_in, eps=1e-5):
super().__init__()
self.c_in = (c_in,)
self.eps = eps
self.weight = nn.Parameter(torch.ones(c_in))
self.bias = nn.Parameter(torch.zeros(c_in))
def forward(self, x):
d = x.dtype
if d ... | class_definition | 12,212 | 12,886 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,471 |
class EsmFoldAttention(nn.Module):
"""
Standard multi-head attention using AlphaFold's default layer initialization. Allows multiple bias vectors.
"""
def __init__(
self,
c_q: int,
c_k: int,
c_v: int,
c_hidden: int,
no_heads: int,
gating: bool = T... | class_definition | 13,344 | 18,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,472 |
class EsmFoldTriangleAttention(nn.Module):
def __init__(self, c_in, c_hidden, no_heads, starting=True, inf=1e9):
"""
Args:
c_in:
Input channel dimension
c_hidden:
Overall hidden channel dimension (not per-head)
no_heads:
... | class_definition | 18,501 | 21,616 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,473 |
class EsmFoldTriangleMultiplicativeUpdate(nn.Module):
"""
Implements Algorithms 11 and 12.
"""
def __init__(self, config, _outgoing=True):
super().__init__()
c_hidden = config.pairwise_state_dim
self._outgoing = _outgoing
self.linear_a_p = EsmFoldLinear(c_hidden, c_hidd... | class_definition | 21,619 | 36,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,474 |
class EsmFoldPreTrainedModel(EsmPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
# Subclass `EsMPreTrainedModel` to deal with special init
def _init_weights(self, module):
"""Initialize the... | class_definition | 36,147 | 38,815 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,475 |
class EsmFoldSelfAttention(nn.Module):
def __init__(self, embed_dim, num_heads, head_width, gated=False):
super().__init__()
assert embed_dim == num_heads * head_width
self.embed_dim = embed_dim
self.num_heads = num_heads
self.head_width = head_width
self.proj = nn.... | class_definition | 38,818 | 40,916 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,476 |
class EsmFoldDropout(nn.Module):
"""
Implementation of dropout with the ability to share the dropout mask along a particular dimension.
"""
def __init__(self, r: float, batch_dim: Union[int, List[int]]):
super().__init__()
self.r = r
if isinstance(batch_dim, int):
b... | class_definition | 40,919 | 41,583 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,477 |
class EsmFoldSequenceToPair(nn.Module):
def __init__(self, sequence_state_dim, inner_dim, pairwise_state_dim):
super().__init__()
self.layernorm = nn.LayerNorm(sequence_state_dim)
self.proj = nn.Linear(sequence_state_dim, inner_dim * 2, bias=True)
self.o_proj = nn.Linear(2 * inner_d... | class_definition | 41,586 | 42,646 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,478 |
class EsmFoldPairToSequence(nn.Module):
def __init__(self, pairwise_state_dim, num_heads):
super().__init__()
self.layernorm = nn.LayerNorm(pairwise_state_dim)
self.linear = nn.Linear(pairwise_state_dim, num_heads, bias=False)
def forward(self, pairwise_state):
"""
Inpu... | class_definition | 42,649 | 43,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,479 |
class EsmFoldResidueMLP(nn.Module):
def __init__(self, embed_dim, inner_dim, dropout=0):
super().__init__()
self.mlp = nn.Sequential(
nn.LayerNorm(embed_dim),
nn.Linear(embed_dim, inner_dim),
nn.ReLU(),
nn.Linear(inner_dim, embed_dim),
nn.... | class_definition | 43,265 | 43,670 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,480 |
class EsmFoldTriangularSelfAttentionBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
sequence_state_dim = config.sequence_state_dim
pairwise_state_dim = config.pairwise_state_dim
sequence_num_heads = sequence_state_dim // config.sequence_... | class_definition | 43,673 | 48,755 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,481 |
class EsmCategoricalMixture:
def __init__(self, param, bins=50, start=0, end=1):
# All tensors are of shape ..., bins.
self.logits = param
bins = torch.linspace(start, end, bins + 1, device=self.logits.device, dtype=self.logits.dtype)
self.v_bins = (bins[:-1] + bins[1:]) / 2
def... | class_definition | 48,758 | 49,512 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,482 |
class EsmFoldRelativePosition(nn.Module):
def __init__(self, config):
super().__init__()
self.bins = config.position_bins
# Note an additional offset is used so that the 0th position
# is reserved for masked pairs.
self.embedding = torch.nn.Embedding(2 * self.bins + 2, confi... | class_definition | 50,209 | 51,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,483 |
class EsmFoldAngleResnetBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.linear_1 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="relu")
self.linear_2 = EsmFoldLinear(config.resnet_dim, config.resnet_dim, init="final")
self.relu = nn.ReLU()
def... | class_definition | 51,602 | 52,131 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,484 |
class EsmFoldAngleResnet(nn.Module):
"""
Implements Algorithm 20, lines 11-14
"""
def __init__(self, config):
super().__init__()
self.config = config
self.linear_in = EsmFoldLinear(config.sequence_dim, config.resnet_dim)
self.linear_initial = EsmFoldLinear(config.sequen... | class_definition | 52,134 | 54,045 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,485 |
class EsmFoldInvariantPointAttention(nn.Module):
"""
Implements Algorithm 22.
"""
def __init__(self, config):
super().__init__()
self.config = config
c_s = config.sequence_dim
c_z = config.pairwise_dim
self.hidden_dim = config.ipa_dim
self.num_heads = co... | class_definition | 54,048 | 60,784 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,486 |
class EsmFoldBackboneUpdate(nn.Module):
"""
Implements part of Algorithm 23.
"""
def __init__(self, config):
super().__init__()
self.linear = EsmFoldLinear(config.sequence_dim, 6, init="final")
def forward(self, s: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
"""
... | class_definition | 60,787 | 61,309 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,487 |
class EsmFoldStructureModuleTransitionLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.linear_1 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
self.linear_2 = EsmFoldLinear(config.sequence_dim, config.sequence_dim, init="relu")
self.linea... | class_definition | 61,312 | 61,969 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,488 |
class EsmFoldStructureModuleTransition(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layers = nn.ModuleList()
for _ in range(config.num_transition_layers):
l = EsmFoldStructureModuleTransitionLayer(config)
self.layers.ap... | class_definition | 61,972 | 62,568 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,489 |
class EsmFoldStructureModule(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
# Buffers to be lazily initialized later
# self.default_frames
# self.group_idx
# self.atom_mask
# self.lit_positions
self.layer_norm_s = Lay... | class_definition | 62,571 | 69,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,490 |
class EsmFoldingTrunk(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
c_s = config.sequence_state_dim
c_z = config.pairwise_state_dim
self.pairwise_positional_embedding = EsmFoldRelativePosition(config)
self.blocks = nn.ModuleList([E... | class_definition | 69,248 | 73,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,491 |
class EsmForProteinFolding(EsmPreTrainedModel):
_no_split_modules = ["EsmFoldStructureModule", "EsmFoldTriangularSelfAttentionBlock"]
def __init__(self, config):
super().__init__(config)
self.config = config
self.distogram_bins = 64
self.esm = EsmModel(config, add_pooling_lay... | class_definition | 74,592 | 86,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/modeling_esmfold.py | null | 4,492 |
class Rotation:
"""
A 3D rotation. Depending on how the object is initialized, the rotation is represented by either a rotation matrix
or a quaternion, though both formats are made available by helper functions. To simplify gradient computation, the
underlying format of the rotation cannot be changed in... | class_definition | 7,792 | 24,320 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/openfold_utils/rigid_utils.py | null | 4,493 |
class Rigid:
"""
A class representing a rigid transformation. Little more than a wrapper around two objects: a Rotation object and a
[*, 3] translation Designed to behave approximately like a single torch tensor with the shape of the shared batch
dimensions of its component parts.
"""
def __ini... | class_definition | 24,323 | 41,129 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/openfold_utils/rigid_utils.py | null | 4,494 |
class ChunkSizeTuner:
def __init__(
self,
# Heuristically, runtimes for most of the modules in the network
# plateau earlier than this on all GPUs I've run the model on.
max_chunk_size: int = 512,
):
self.max_chunk_size = max_chunk_size
self.cached_chunk_size: Opt... | class_definition | 11,212 | 14,389 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/openfold_utils/chunk_utils.py | null | 4,495 |
class Protein:
"""Protein structure representation."""
# Cartesian coordinates of atoms in angstroms. The atom types correspond to
# residue_constants.atom_types, i.e. the first three are N, CA, CB.
atom_positions: np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue repres... | class_definition | 993 | 2,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/esm/openfold_utils/protein.py | null | 4,496 |
class UnivNetConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`UnivNetModel`]. It is used to instantiate a
UnivNet vocoder model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yiel... | class_definition | 769 | 6,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/configuration_univnet.py | null | 4,497 |
class UnivNetFeatureExtractor(SequenceFeatureExtractor):
r"""
Constructs a UnivNet feature extractor.
This class extracts log-mel-filter bank features from raw speech using the short time Fourier Transform (STFT). The
STFT implementation follows that of TacoTron 2 and Hifi-GAN.
This feature extrac... | class_definition | 1,048 | 22,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/feature_extraction_univnet.py | null | 4,498 |
class UnivNetModelOutput(ModelOutput):
"""
Output class for the [`UnivNetModel`], which includes the generated audio waveforms and the original unpadded
lengths of those waveforms (so that the padding can be removed by [`UnivNetModel.batch_decode`]).
Args:
waveforms (`torch.FloatTensor` of shap... | class_definition | 1,159 | 1,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/univnet/modeling_univnet.py | null | 4,499 |
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