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# 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... | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
rope_config_validation(self) | 3,979 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/configuration_granite.py |
class GraniteAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GraniteConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.scaling = config.attention_multiplier | 3,980 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
class GraniteDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: GraniteConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.residual_multiplier = config.residual_multiplier
self.self_attn = GraniteAttention(config=config, layer_idx=layer_idx) | 3,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.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,
... | 3,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are r... | 3,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
kwargs (`dict`, *optional*):
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
"""
residual = hidden_states | 3,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
hidden_states = self.input_layernorm(hidden_states)
# 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,
outpu... | 3,981 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
class GraniteModel(LlamaModel):
def __init__(self, config: GraniteConfig):
super().__init__(config)
self.embedding_multiplier = config.embedding_multiplier
self.layers = nn.ModuleList(
[GraniteDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
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: Optional[torch.FloatTensor] = None,
use_cache: Optional[b... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.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... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
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 shar... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.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,
... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.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,)
... | 3,982 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,983 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
class GraniteForCausalLM(LlamaForCausalLM):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
... | 3,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.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,
... | 3,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
loss = None
if labels is not None:
loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return Causal... | 3,984 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/granite/modular_granite.py |
class Gemma2RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | 3,985 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2MLP(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)
sel... | 3,986 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.h... | 3,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
self.q_proj = nn.Linear(
config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
)
self.k_proj = nn.Linear(
config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
)
self.v_proj = nn.Linear(
... | 3,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
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: Unpack[FlashAttenti... | 3,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=self.attention_dropout if self.training else 0.0,
scaling=self.scaling,
sliding_window=self.sliding_windo... | 3,987 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2DecoderLayer(nn.Module):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.is_sliding = not bool(layer_idx % 2)
self.self_attn = Gemma2Attention(config=config, layer_idx=lay... | 3,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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: Optio... | 3,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
torch.ones_like(attention_mask, dtype=torch.bool), diagonal=-self.sliding_window
)
attention_mask = torch.where(sliding_window_mask, min_dtype, attention_mask)
if attention_mask.shape[-1] <= 1: # when decoding
attention_mask = attention_mask[:, :, :, ... | 3,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
residual = hidden_states
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
position_embeddings=position_embeddings,
attention_mask=attention_mask,
... | 3,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
if output_attentions:
outputs += (self_attn_weights,)
return outputs | 3,988 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2RotaryEmbedding(nn.Module):
def __init__(self, config: Gemma2Config, 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", ... | 3,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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 ... | 3,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,989 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2PreTrainedModel(PreTrainedModel):
config_class = Gemma2Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Gemma2DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_s... | 3,990 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2Model(Gemma2PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Gemma2DecoderLayer`]
Args:
config: Gemma2Config
"""
def __init__(self, config: Gemma2Config):
super().__init__(config)
self.padding_idx = con... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
@add_start_docstrings_to_model_forward(GEMMA2_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[HybridCache] = None,
inputs... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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 | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
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 + inputs_embeds.shape[1], device=inputs_embeds.device
)
if position_ids is N... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
# normalized
# Gemma2 downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
# See https://github.com/huggingface/transformers/pull/29402
normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
hidden_states = hidden_states * normalizer
... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
position_embeddings,
causal_mask,
position_ids,
... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states)
if output_hidden_states:
all_hidden_states += (hidden_states,)
output = BaseModelOutputWithPast(
last_hidd... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
@torch.no_grad()
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: HybridCache,
output_attentions: bool,
):
# Flash Attention currently doesn't support static cache but G... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
dtype, device = input_tensor.dtype, input_tensor.device
sequence_length = input_tensor.shape[1]
if isinstance(past_key_values, HybridCache):
target_length = past_key_values.get_max_cache_shape()
else:
target_length = attention_mask.shape[-1] if attention_mask is not None ... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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,
):
... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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.
... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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(... | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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
) | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
return causal_mask | 3,991 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2ForCausalLM(Gemma2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.vocab_size = config.vocab_size
self.lm_he... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
@add_start_docstrings_to_model_forward(GEMMA2_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: Optiona... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
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
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_si... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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?"
```""" | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
if self.training and self.config._attn_implementation != "eager":
logger.warning_once(
"It is strongly recommended to train Gemma2 models with the `eager` attention implementation "
f"instead of `{self.config._attn_implementation}`. Use `eager` with `AutoModelForCausalLM.from... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
cache_position=cache_position,
) | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
hidden_states = outputs[0]
# Only compute necessary logits, and do not upcast them to float if we are not computing the loss
logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
if self.config.final_logit_softcapping is not None:
logits = logits / self.config.final_logit_... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
cache_position=None,
position_ids=None,
use_cache=True,
num_logits_to_keep=None,
**kwargs,
):
# Overwritten: has ... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
# If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
# Exception 1: when passing input_embeds, input_ids may be missing entries
# Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
if past_key_... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
# This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s
# `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride
# during the decoding. Here, simply using `.contiguous()` is not sufficient as in the
# batch s... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
if inputs_embeds is not None and cache_position[0] == 0:
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
else:
# The clone here is for the same reason as for `position_ids`.
... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=past_key_values.get_max_cache_shape(),
dtype=self.lm_head.weight.dtype,
device=device,
... | 3,992 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2ForSequenceClassification(Gemma2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weight... | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
@add_start_docstrings_to_model_forward(GEMMA2_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[torch.F... | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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 | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,993 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2ForTokenClassification(Gemma2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Gemma2Model(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.cla... | 3,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
@add_start_docstrings_to_model_forward(GEMMA2_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,
att... | 3,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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).
"""
... | 3,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.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... | 3,994 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modeling_gemma2.py |
class Gemma2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of ... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
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`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
eos_token_... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
The dropout ratio for the attention probabilities.
query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the
size of t... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
```python
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-7b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-7b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configu... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
def __init__(
self,
vocab_size=256000,
hidden_size=2304,
intermediate_size=9216,
num_hidden_layers=26,
num_attention_heads=8,
num_key_value_heads=4,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=8192,
... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim ... | 3,995 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/configuration_gemma2.py |
class Gemma2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Gemma2Model`]. It is used to instantiate an Gemma2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a simila... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
intermediate_size (`int`, *optional*, defaults to 9216):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 26):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of ... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
head_dim (`int`, *optional*, defaults to 256):
The attention head dimension.
hidden_activation (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
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`.
pad_token_id (`int`, *optional*, defaults to 0):
Padding token id.
eos_token_... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
The dropout ratio for the attention probabilities.
query_pre_attn_scalar (`float`, *optional*, defaults to 256): scaling factor used on the attention scores
sliding_window (`int`, *optional*, defaults to 4096): in Gemma2, every other layer uses sliding window attention. This is the
size of t... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
```python
>>> from transformers import Gemma2Model, Gemma2Config
>>> # Initializing a Gemma2 gemma2-7b style configuration
>>> configuration = Gemma2Config()
>>> # Initializing a model from the gemma2-7b style configuration
>>> model = Gemma2Model(configuration)
>>> # Accessing the model configu... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
def __init__(
self,
vocab_size=256000,
hidden_size=2304,
intermediate_size=9216,
num_hidden_layers=26,
num_attention_heads=8,
num_key_value_heads=4,
head_dim=256,
hidden_activation="gelu_pytorch_tanh",
max_position_embeddings=8192,
... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.head_dim ... | 3,996 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2RMSNorm(GemmaRMSNorm):
pass | 3,997 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2MLP(GemmaMLP):
def __init__(self, config):
super().__init__()
self.act_fn = ACT2FN[config.hidden_activation] | 3,998 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2Attention(GemmaAttention):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__(config, layer_idx)
self.attn_logit_softcapping = self.config.attn_logit_softcapping
self.attention_dropout = self.config.attention_dropout
self.is_causal = True
s... | 3,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = ... | 3,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.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... | 3,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=self.attention_dropout if self.training else 0.0,
scaling=self.scaling,
sliding_window=self.sliding_windo... | 3,999 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2DecoderLayer(nn.Module):
def __init__(self, config: Gemma2Config, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.config = config
self.is_sliding = not bool(layer_idx % 2)
self.self_attn = Gemma2Attention(config=config, layer_idx=lay... | 4,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.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: Optio... | 4,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
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