text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
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class ConvNextEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.stages = nn.ModuleList()
drop_path_rates = [
x.tolist() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths)).split(config.depths)
]
prev_chs = config.hidden_size... | 3,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
def forward(
self,
hidden_states: torch.FloatTensor,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutputWithNoAttention]:
all_hidden_states = () if output_hidden_states else None
for i, layer_module in e... | 3,644 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = ConvNextConfig
base_model_prefix = "convnext"
main_input_name = "pixel_values"
_no_split_module... | 3,645 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextModel(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
# final layernorm layer
self.layernorm = nn.LayerNorm(config.h... | 3,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPoolingAndNoAttention,
config_class=_CONFIG_FOR_DOC,
modality="vision",
expected_output=_EXPECTED_OUTPUT_SHAPE,
)
... | 3,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_state = encoder_outputs[0]
# global average pooling, (N, C, H, W) -> (N, C)
pooled_output = self.layernorm(last_hidden... | 3,646 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextForImageClassification(ConvNextPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.convnext = ConvNextModel(config)
# Classifier head
self.classifier = (
nn.Linear(config.hidden_sizes[-1],... | 3,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT,
output_type=ImageClassifierOutputWithNoAttention,
config_class=_CONFIG_FOR_DOC,
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
)
def forward(
... | 3,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
outputs = self.convnext(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
pooled_output = outputs.pooler_output if return_dict else outputs[1]
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_... | 3,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "singl... | 3,647 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class ConvNextBackbone(ConvNextPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.embeddings = ConvNextEmbeddings(config)
self.encoder = ConvNextEncoder(config)
self.num_features = [config.hidden_sizes[0]... | 3,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
@add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
pixel_values: torch.Tensor,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
... | 3,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
>>> inputs = processor(image, return_tensors="pt")
>>> outputs = model(**inputs)
```"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.out... | 3,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
if not return_dict:
output = (feature_maps,)
if output_hidden_states:
output += (hidden_states,)
return output
return BackboneOutput(
feature_maps=feature_maps,
hidden_states=hidden_states if output_hidden_states else None,
... | 3,648 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/convnext/modeling_convnext.py |
class GPTNeoXTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
</Tip>
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
Args:
vocab_file (`str`):
Path to the vocabulary file.
merges_file (`str`):
Path to the merges file.
errors (`str`, *optional*, defaults to `"replace"`):
Paradigm to follow when decoding bytes to UTF-8. See
[bytes.decode](https://docs.python.org/3/... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
Whether or not to add an initial space to the input. This allows to treat the leading word just as any
other word. (GPTNeoX tokenizer detect beginning of words by the preceding space).
add_bos_token (`bool`, *optional*, defaults to `False`):
Whether or not to add a `bos_token` at the sta... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
unk_token="<|endoftext|>",
bos_token="<|endoftext|>",
eos_token="<|endoftext|>",
... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
@property
def add_eos_token(self):
return self._add_eos_token
@property
def add_bos_token(self):
return self._add_bos_token
@add_eos_token.setter
def add_eos_token(self, value):
self._add_eos_token = value
self.update_post_processor()
@add_bos_token.setter
... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
eos = self.eos_token
eos_token_id = self.eos_token_id
if eos is None and self.add_eos_token:
raise ValueError("add_eos_token = True but eos_token = None")
single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
pair = f"{sing... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
# Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_special_tokens_mask
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieve sequence ids from a ... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
Returns:
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=Tru... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
# Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.build_inputs_with_special_tokens
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
bos_token_id = [self.bos_token_id] if self.add_bos_token else []
eos_token_id = [self.eos_token_id] if self.a... | 3,649 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/tokenization_gpt_neox_fast.py |
class GPTNeoXConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an
GPTNeoX model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
Args:
vocab_size (`int`, *optional*, defaults to 50432):
Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTNeoXModel`].
hidden_size (`int`, *optional*, defaults to 6144):
D... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
`"relu"`, `"selu"` and `"gelu_new"` are supported.
rotary_pct (`float`, *optional*, defaults to 0.25):
percentage of hidden dimensions to allocate to rotary embeddings
rotary_emb_base (`int`, *optional*, defaults to 10000)
base for computing rotary embeddings frequency
at... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
The dropout ratio for the hidden layer.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, ... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
speedup at large scales (e.g. 20B).
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
`original_max_position_embeddings` (`int`, *optional*):
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The s... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
ramp function. If unspecified, it defaults to 1.
`short_factor` (`List[float]`, *optional*):
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
`original_max_position_embeddings`). Must be a list of numbers with the same length as the... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
attention_bias (`bool`, *optional*, defaults to `True`):
Whether to use a bias in the query, key, value and output projection layers during self-attention. | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
Example:
```python
>>> from transformers import GPTNeoXConfig, GPTNeoXModel
>>> # Initializing a GPTNeoX gpt-neox-20b style configuration
>>> configuration = GPTNeoXConfig()
>>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration
>>> model = GPTNeoXModel(con... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
def __init__(
self,
vocab_size=50432,
hidden_size=6144,
num_hidden_layers=44,
num_attention_heads=64,
intermediate_size=24576,
hidden_act="gelu",
rotary_pct=0.25,
rotary_emb_base=10000,
attention_dropout=0.0,
hidden_dropout=0.0,
... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.rotary_pct = rotary_pct
self.partial_rotary_factor = rotary_pct
self.rotary_emb_base = rotary_emb_base
self.rope_theta = rotary_emb_base
self.attention_dropout = attention_dropout
self.hi... | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self) | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"The hidden size is not divisble by the number of attention heads! Make sure to update them!"
) | 3,650 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/configuration_gpt_neox.py |
class GPTNeoXPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GPTNeoXConfig
base_model_prefix = "gpt_neox"
supports_gradient_checkpointing = True
_no_split_mo... | 3,651 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embeddin... | 3,651 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXAttention(nn.Module):
def __init__(self, config, layer_idx=None):
super().__init__()
self.config = config
self.num_attention_heads = config.num_attention_heads
self.hidden_size = config.hidden_size
if self.hidden_size % self.num_attention_heads != 0:
... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if layer_idx is None:
logger.warning_once(
f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
"when creat... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def _init_bias(self, max_positions, device=None):
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
1, 1, max_positions, max_positions
),
persistent=False,
)
if device is no... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# Apply attention-specific projections and rope
query, key, value, present = self._attn_projections_and_rope(
hidden_states=hidden_states,
position_ids=position_ids,
layer_past=layer_past,
use_cache=use_cache,
cache_position=cache_position,
... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
elif (
self.training
and self.config.attention_dropout > 0
and self.config._attn_implementation == "flex_attention"
):
logger.warning_once(
f"Setting `attention_type` to `eager` because `dropout` is not supported in `{attention_type}`."
... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# Reshape outputs and final projection
attn_output = attn_output.contiguous()
attn_output = attn_output.view(bsz, seq_len, -1)
attn_output = self.dense(attn_output)
outputs = (attn_output, present)
if output_attentions:
outputs += (attn_weights,)
return outp... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@classmethod
def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
"""
Merges attn_head_size dim and num_attn_heads dim into hidden dim
"""
# tensor [bs, num_attention_heads, seq_len, attn_head_size]
tensor = tensor.permute(0, 2, 1, 3).contiguous()
# -> ... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def _attn_projections_and_rope(
self,
hidden_states: torch.FloatTensor,
position_ids: torch.LongTensor,
layer_past: Optional[Tuple[torch.Tensor]] = None,
use_cache: Optional[bool] = False,
cache_position: Optional[torch.LongTensor] = None,
position_embeddings: Opt... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
query = qkv[..., : self.head_size].permute(0, 2, 1, 3)
key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3)
value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3)
... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# Cache QKV values
if layer_past is not None:
cache_kwargs = {
"sin": sin,
"cos": cos,
"partial_rotation_size": self.rotary_ndims,
"cache_position": cache_position,
}
key, value = layer_past.update(key, value, se... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def _fa_peft_dtype_check(self, value):
"""
PEFT can silently cast the dtype to float32 - this method returns the target dtype to which
FA should convert back to (if necessary). For now, we can not move this to the forward pass
itself due to the dependency on checking on some part of its ... | 3,652 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXRotaryEmbedding(nn.Module):
def __init__(self, config: GPTNeoXConfig, 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"... | 3,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.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 ... | 3,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.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... | 3,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.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... | 3,653 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXMLP(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 forward(... | 3,654 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.use_parallel_residual = config.use_parallel_residual
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_attention_layernorm = nn.LayerNorm(config.hi... | 3,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def forward(
self,
hidden_states: Optional[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = False,
layer_past: Option... | 3,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights)
attn_output = self.post_attention_dropout(attn_output)
outputs = attention_layer_outputs[1:] | 3,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if self.use_parallel_residual:
# pseudocode:
# x = x + attn(ln1(x)) + mlp(ln2(x))
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
mlp_output = self.post_mlp_dropout(mlp_output)
hidden_states = mlp_output + attn_output + hidden_states
... | 3,655 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXModel(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
self.emb_dropout = nn.Dropout(config.hidden_dropout)
self.layers = nn.ModuleList([GPTNeoX... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
r"""
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
`past_key_values`).
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentio... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
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_cache:
logger.warning_once(
"`use_cache=True` is incompatible... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.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... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
seq_length = inputs_embeds.shape[1]
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 + seq_length, device=inputs_embeds.device)
if position_ids is... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_lengt... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
next_decoder_cache = None
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
for i, layer in enumerate(
self.layers,
):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidde... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if self.gradient_checkpointing and self.training:
outputs = self._gradient_checkpointing_func(
layer.__call__,
hidden_states,
causal_mask,
position_ids,
head_mask[i],
use_cache,
... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
hidden_states = outputs[0]
if use_cache is True:
next_decoder_cache = outputs[1]
if output_attentions:
all_attentions = all_attentions + (outputs[2 if use_cache else 1],) | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
hidden_states = self.final_layer_norm(hidden_states)
# Add last hidden state
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
if return_legacy_cache:
next_cache = next_cache.t... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask,
... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@staticmethod
# Copied from transformers.models.llama.modeling_llama.LlamaModel._prepare_4d_causal_attention_mask_with_cache_position
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dt... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
`(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
causal_mask = attention_mask
else:
min_dtype = torch.finfo(dtype).min
causal_mask = torch.full(... | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
padding_mask = padding_mask == 0
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
) | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
return causal_mask | 3,656 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["embed_out.weight"]
def __init__(self, config):
super().__init__(config)
self.gpt_neox = GPTNeoXModel(config)
self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[t... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
>>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b")
... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
outputs = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_a... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
lm_loss = None
if labels is not None:
# move labels to correct device to enable model parallelism
labels = labels.to(lm_logits.device)
# we are doing next-token prediction; shift prediction scores and input ids by one
shift_logits = lm_logits[:, :-1, :].contiguous... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
def _reorder_cache(self, past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past[:2])
+ layer_past[2:],
)
... | 3,657 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize ... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=SequenceClassifierOutputWithPast,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_lab... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
outputs = self.gpt_neox(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_a... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token foun... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
loss = None
if labels is not None:
labels = labels.to(logits.device)
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_typ... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
if self.config.problem_type == "regression":
loss_fct = MSELoss()
if self.num_labels == 1:
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(pooled_logits, labels)
elif self.config.problem_... | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,658 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.gpt_neox = GPTNeoXModel(config)
self.dropout = nn.Dropout(config.classifier_dropout)
self.classifier = nn.Linear(config.... | 3,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint="LarsJonasson/pythia-410m-deduped-sft-swedish",
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
expected_loss=0.25,
)
def forward(
self,
... | 3,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
) -> Union[Tuple, TokenClassifierOutput]:
r"""
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a... | 3,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
outputs = self.gpt_neox(
input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_a... | 3,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,659 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gpt_neox/modeling_gpt_neox.py |
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