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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: b... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shap... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
if position_embeddings is None:
logger.warning_once(
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
"`posi... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_s... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
attn_output1 = _flash_attention_forward(
query_states,
key_states,
value_states1,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
use_... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_full = lambda_1 - l... | 3,522 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaSdpaAttention(DiffLlamaAttention):
"""
DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
# Adapted from DiffLlamaAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[C... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attent... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.v... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
value_states = value_states.repeat(1, 2, 1, 1)
causal_mask = attention_mask
if at... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
is_causal = True if ca... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_full = lambda_1 - l... | 3,523 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
DiffLlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states)... | 3,524 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaDecoderLayer(nn.Module):
def __init__(self, config: DiffLlamaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = Dif... | 3,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,
... | 3,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,525 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaPreTrainedModel(PreTrainedModel):
config_class = DiffLlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["DiffLlamaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = Tr... | 3,526 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaRotaryEmbedding(nn.Module):
def __init__(self, config: DiffLlamaConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_t... | 3,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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 ... | 3,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,527 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaModel(DiffLlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DiffLlamaDecoderLayer`]
Args:
config: DiffLlamaConfig
"""
def __init__(self, config: DiffLlamaConfig):
super().__init__(config)
self.pa... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
@add_start_docstrings_to_model_forward(DIFFLLAMA_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_em... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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 | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,
... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,)
... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,
... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,
):
... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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.
... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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(... | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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
) | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
return causal_mask | 3,528 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,529 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaForCausalLM(DiffLlamaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = DiffLlamaModel(config)
self.vocab_size = config.vocab_size
s... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
@add_start_docstrings_to_model_forward(DIFFLLAMA_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: Opti... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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
... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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]
"What is your favorite condiment?"
```"""
output_attentions = output_attentions if output_at... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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,
... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,530 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaForSequenceClassification(DiffLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = DiffLlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
@add_start_docstrings_to_model_forward(DIFFLLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torc... | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.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... | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_va... | 3,531 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaForQuestionAnswering(DiffLlamaPreTrainedModel):
base_model_prefix = "transformer"
def __init__(self, config):
super().__init__(config)
self.transformer = DiffLlamaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply fina... | 3,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
@add_start_docstrings_to_model_forward(DIFFLLAMA_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List... | 3,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the end of the labelled spa... | 3,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
outputs = self.transformer(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_state... | 3,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 3,532 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaForTokenClassification(DiffLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = DiffLlamaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = c... | 3,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
@add_start_docstrings_to_model_forward(DIFFLLAMA_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 3,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
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_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 3,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden... | 3,533 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py |
class DiffLlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults
will yield... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the DiffLlama model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DiffLlamaModel`]
hidden_size (`int`, *optional*, defaults to 2048):
... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.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... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model s... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
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
accordingly.
Expected contents:
`rope_type` (`str`):
... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
Used with 'dynamic', 'longrope' and 'diffllama3'. The original max position embeddings used during
pretraining.
`attention_factor` (`float`, *optional*):
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
computa... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
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 hidden
size divided by the number of attention heads divided by 2
`long_factor` (`List[float... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
lambda_std_dev... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
```python
>>> from transformers import DiffLlamaModel, DiffLlamaConfig
>>> # Initializing a DiffLlama diffllama-7b style configuration
>>> configuration = DiffLlamaConfig()
>>> # Initializing a model from the diffllama-7b style configuration
>>> model = DiffLlamaModel(configuration)
>>> # Acc... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
def __init__(
self,
vocab_size=32000,
hidden_size=2048,
intermediate_size=8192,
num_hidden_layers=16,
num_attention_heads=32,
num_key_value_heads=None,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_nor... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.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.rms_norm_eps = rms_norm_eps
self... | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
) | 3,534 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py |
class DiffLlamaMLP(MistralMLP):
pass | 3,535 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
self.attention_dropout = config.attention_dropout
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
self.num_key_value_heads = config.num_key_value_heads
self.num_key_v... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=c... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
self.lambda_init = lambda_init_fn(layer_idx)
self.lambda_q1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
self.lambda_k1 = nn.Parameter(torch.normal(0, config.lambda_std_dev, size=(self.head_dim,)))
self.lambda_q2 = nn.Parameter(torch.normal(0, config.lambda_std_d... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: bool ... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
cos... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
key_states = repeat_kv(key_states, self.num_key_value_groups)
value_states = repeat_kv(value_states, self.num_key_value_groups)
value_states = torch.cat(torch.chunk(value_states, 2, dim=1), dim=-1)
value_states = value_states.repeat(1, 2, 1, 1)
attn_weights = torch.matmul(query_states, ... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, d... | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights | 3,536 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaFlashAttention2(DiffLlamaAttention):
"""
DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attention ... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: b... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
# Flash attention requires the input to have the shape
# batch_size x seq_length x head_dim x hidden_dim
# therefore we just need to keep the original shap... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
if position_embeddings is None:
logger.warning_once(
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
"`posi... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.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... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
# therefore the input hidden states gets silently casted in float32. Hence, we need
# cast them back in the correct dtype just to be sure everything works as expected.
# This might slowdown training & inference ... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
logger.warning_once(
f"The input hidden states seems to be silently casted in float32, this might be related to"
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
f" {target_dtype}."
)
query_s... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
attn_output1 = _flash_attention_forward(
query_states,
key_states,
value_states1,
attention_mask,
q_len,
position_ids=position_ids,
dropout=dropout_rate,
sliding_window=getattr(self, "sliding_window", None),
use_... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1, dtype=torch.float32)).to(
query_states.dtype
)
lambda_full = lambda_1 - l... | 3,537 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
class DiffLlamaSdpaAttention(DiffLlamaAttention):
"""
DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
""" | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
# Adapted from DiffLlamaAttention.forward
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[C... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
)
return super().forward(
hidden_states=hidden_states,
attent... | 3,538 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py |
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