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def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs,
) -> Tuple[torc... | 3,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
**kwargs,
)
... | 3,446 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2MLP(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=False)
self... | 3,447 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2DecoderLayer(nn.Module):
def __init__(self, config: Olmo2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = Olmo2Attention(config=config, layer_idx=layer_idx)
self.mlp = Olmo2MLP(config)
self.post_attention_layernorm... | 3,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cac... | 3,448 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2RotaryEmbedding(nn.Module):
def __init__(self, config: Olmo2Config, 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... | 3,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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 ... | 3,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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... | 3,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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... | 3,449 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2PreTrainedModel(PreTrainedModel):
config_class = Olmo2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Olmo2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | 3,450 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2Model(Olmo2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Olmo2DecoderLayer`]
Args:
config: Olmo2Config
"""
def __init__(self, config: Olmo2Config):
super().__init__(config)
self.padding_idx = config.p... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
@add_start_docstrings_to_model_forward(OLMO2_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Cache] = None,
inputs_embeds... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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 and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
# create position embeddings to be shared across the decoder layers
position_embeddings = self.rotary_emb(hidden_states,... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
causal_mask,
position_ids,
past_key_values,
... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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,
... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
**kwargs,
):
... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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.
... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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(... | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.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
) | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
return causal_mask | 3,451 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,452 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2ForCausalLM(Olmo2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Olmo2Model(config)
self.vocab_size = config.vocab_size
self.lm_head ... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
@add_start_docstrings_to_model_forward(OLMO2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,453 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modeling_olmo2.py |
class Olmo2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
Args:
vocab_size (`int`, *optional*, defaults to 50304):
Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Olmo2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimens... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construc... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
The epsilon used by the rms normalization layers. | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
```python
>>> from transformers import Olmo2Model, Olmo2Config
>>> # Initializing a Olmo2 7B style configuration
>>> configuration = Olmo2Config()
>>> # Initializing a model from the Olmo2 7B style configuration
>>> model = Olmo2Model(configuration)
>>> # Accessing the model configuration
... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
def __init__(
self,
vocab_size=50304,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
use_ca... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.use_cache = use_cache
self.rope_... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
)
rope_scaling_type = self.rope_scaling.get("type", None)
rope_scal... | 3,454 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/configuration_olmo2.py |
class Olmo2Config(OlmoConfig):
r"""
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configu... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
Args:
vocab_size (`int`, *optional*, defaults to 50304):
Vocabulary size of the Olmo2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Olmo2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimens... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be construc... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum. See the following thread for more information... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
The epsilon used by the rms normalization layers. | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
```python
>>> from transformers import Olmo2Model, Olmo2Config
>>> # Initializing a Olmo2 7B style configuration
>>> configuration = Olmo2Config()
>>> # Initializing a model from the Olmo2 7B style configuration
>>> model = Olmo2Model(configuration)
>>> # Accessing the model configuration
... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
def __init__(
self,
vocab_size=50304,
hidden_size=4096,
intermediate_size=11008,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
use_ca... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
initializer_range=initializer_range,
use_cache=use_cache,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
rope_theta=rope_theta,
rope_scaling=rope_scaling,
... | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
self.rms_norm_eps = rms_norm_eps
del self.clip_qkv | 3,455 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Olmo2RMSNorm(LlamaRMSNorm):
pass | 3,456 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Olmo2Attention(OlmoAttention):
def __init__(self, config: Olmo2Config, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx=layer_idx)
self.q_norm = Olmo2RMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps)
self.k_norm = Olmo2RMSNorm(config.num_key_v... | 3,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
query_states = self.q_norm(self.q_proj(hidden_states))
key_states = self.k_norm(self.k_proj(hidden_states))
value_states = self.v_proj(hidden_states)
query_states = query_states.view(hidden_shape).transpose(1, 2)
key_states = key_states.view(hidden_shape).transpose(1, 2)
value_s... | 3,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attentio... | 3,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights | 3,457 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Olmo2DecoderLayer(OlmoDecoderLayer):
def __init__(self, config: Olmo2Config, layer_idx: int):
super().__init__(config, layer_idx=layer_idx)
self.post_attention_layernorm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.post_feedforward_layernorm = Olmo2RMSNorm(config.hi... | 3,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cac... | 3,458 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Olmo2Model(OlmoModel):
def __init__(self, config: Olmo2Config):
super().__init__(config)
self.norm = Olmo2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
self.layers = nn.ModuleList(
[Olmo2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)... | 3,459 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Olmo2ForCausalLM(OlmoForCausalLM):
pass | 3,460 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/olmo2/modular_olmo2.py |
class Emu3ImageProcessor(BaseImageProcessor):
r"""
Constructs a Emu3 image processor that dynamically resizes images based on the original images. | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
Resampling filter to use when resizing the image.
do_rescale (`bool`, *option... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad t... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
model_input_names = ["pixel_values"] | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
def __init__(
self,
do_resize: bool = True,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize: bool = True,
image_mean: Optional[Union[float, List[float]]] = None,
... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
self.max_pixels = max_pixels
self.spatial_factor = spatial_factor
self.size = {"min_pixels": min_pixels, "max_pixels": max_pixels}
self.do_convert_rgb = do_convert_rgb | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
def _preprocess(
self,
images: Union[ImageInput, VideoInput],
do_resize: bool = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, List[float]]] = N... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Args:
images (`ImageInput`):
Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
vision_info (`List[Dict]`, *optional*):
Optional list of dictionaries containing additional ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- Unset: Use the channel dimension format of the input image.
input_data_format (`Ch... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
if do_convert_rgb:
images = [convert_to_rgb(image) for image in images]
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if is_scaled_image(images[0]) and do_rescale:
logger.warning_once(
"It looks like you ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
height, width = get_image_size(images[0], channel_dim=input_data_format)
resized_height, resized_width = height, width
processed_images = []
for image in images:
if do_resize:
resized_height, resized_width = smart_resize(
height,
... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
processed_images.append(image)
images = np.array(processed_images)
return images
def _pad_for_batching(
self,
pixel_values: List[np.ndarray],
image_sizes: List[List[int]... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Args:
pixel_values (`List[np.ndarray]`):
An array of pixel values of each images of shape (`batch_size`, `num_patches`, `image_in_3D`)
image_sizes (`List[List[int]]`):
A list of sizes for each image in `pixel_values` in (height, width) format.
data_for... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
If unset, will use the inferred format of the input image. | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Returns:
List[`np.ndarray`]: The padded images.
"""
max_shape = (
max([size[0] for size in image_sizes]),
max([size[1] for size in image_sizes]),
)
pixel_values = [
pad(
image,
padding=((0, max_shape[0] - si... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_s... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
The type of tensors to return. Can be one of:
- Unset: Return a list of `np.ndarray`.
- `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
- `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
- `TensorType.NUMPY` or ... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels,... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_fac... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
if images is not None and not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
rescale_factor=rescale_fact... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
pixel_values = []
for image in images:
image = self._preprocess(
image,
do_resize=do_resize,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
def postprocess(
self,
images: ImageInput,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Optional[bool] = None,
image_mean: Optional[Union[float, List[float]]] = None,
image_std: Optional[Union[float, List[float]]] = None... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
Rescale factor to rescale the image by if `do_rescale` is set to `True`.
do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
Whether to normalize the image.
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
Image mea... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
input_data_format (`ChannelDimension` or `str`, *optional*):
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels,... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
images = make_list_of_images(images)
if isinstance(images[0], Image.Image):
return images if len(images) > 1 else images[0]
if input_data_format is None:
# We assume that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_f... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
if do_normalize and do_rescale and return_tensors == "PIL.Image.Image":
image = to_channel_dimension_format(image, ChannelDimension.LAST, input_channel_dim=input_data_format)
pixel_values.append(Image.fromarray(image))
else:
pixel_values.extend(image)
... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
def unnormalize(
self,
image: np.array,
image_mean: Union[float, Iterable[float]],
image_std: Union[float, Iterable[float]],
input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.array:
"""
Unnormalizes `image` using the mean and standard d... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimens... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
if isinstance(image_mean, Iterable):
if len(image_mean) != num_channels:
raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(image_mean)}")
else:
image_mean = [image_mean] * num_channels
if isinstance(image_std, Iterable):
... | 3,461 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/image_processing_emu3.py |
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