text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
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[:, :, :, ... | 4,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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,
... | 4,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
if output_attentions:
outputs += (self_attn_weights,)
return outputs | 4,000 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2Model(GemmaModel):
def __init__(self, config: Gemma2Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Gemma2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
) | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.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[HybridCache] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Opti... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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
... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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,
... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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 ... | 4,001 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2ForCausalLM(GemmaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init()
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
positio... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
>>> model = GemmaForCausalLM.from_pretrained("google/gemma-2-9b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> # Generate
>>> generate_ids = model.gener... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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,
) | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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_... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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 ... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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_... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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`.
... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_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,
... | 4,002 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2ForSequenceClassification(GemmaForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init() | 4,003 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class Gemma2ForTokenClassification(GemmaForTokenClassification):
def __init__(self, config):
super().__init__(config)
self.model = Gemma2Model(config)
self.post_init() | 4,004 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma2/modular_gemma2.py |
class MixtralBlockSparseTop2MLP(nn.Module):
def __init__(self, config: MixtralConfig):
super().__init__()
self.ffn_dim = config.intermediate_size
self.hidden_dim = config.hidden_size
self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
self.w2 = nn.Linear(self.ffn_... | 4,005 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralSparseMoeBlock(nn.Module):
"""
This implementation is
strictly equivalent to standard MoE with full capacity (no
dropped tokens). It's faster since it formulates MoE operations
in terms of block-sparse operations to accommodate imbalanced
assignments of tokens to experts, whereas st... | 4,006 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
# Jitter parameters
self.jitter_noise = config.router_jitter_noise
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
""" """
batch_size, sequence_length, hidden_dim = hidden_states.shape
if self.training and self.jitter_noise > 0:
hidden_states *= torch.emp... | 4,006 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
final_hidden_states = torch.zeros(
(batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
)
# One hot encode the selected experts to create an expert mask
# this will be used to easily index which expert is going to be sollicitated
... | 4,006 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
# Index the correct hidden states and compute the expert hidden state for
# the current expert. We need to make sure to multiply the output hidden
# states by `routing_weights` on the corresponding tokens (top-1 and top-2)
current_state = hidden_states[None, top_x].reshape(-1, hidden... | 4,006 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MixtralRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
... | 4,007 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: MixtralConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config... | 4,008 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,008 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,008 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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,
sliding_window=getattr(self.c... | 4,008 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralDecoderLayer(nn.Module):
def __init__(self, config: MixtralConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MixtralAttention(config, layer_idx)
self.block_sparse_moe = MixtralSparseMoeBlock(config)
self.input_la... | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
output_router_logits: O... | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
... | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
into the model
""" | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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,
... | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
if output_router_logits:
outputs += (router_logits,)
return outputs | 4,009 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralRotaryEmbedding(nn.Module):
def __init__(self, config: MixtralConfig, 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"... | 4,010 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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 ... | 4,010 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,010 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,010 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralPreTrainedModel(PreTrainedModel):
config_class = MixtralConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["MixtralDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
... | 4,011 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralModel(MixtralPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MixtralDecoderLayer`]
Args:
config: MixtralConfig
"""
def __init__(self, config: MixtralConfig):
super().__init__(config)
self.padding_idx ... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
def set_input_embeddings(self, value):
self.embed_tokens = value | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
@add_start_docstrings_to_model_forward(MIXTRAL_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[List[torch.FloatTensor]] = None,
... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
output_router_logits if output_router_logits is not None else self.config.output_router_logits
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training:
if use_ca... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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 No... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
for decoder_layer in self.layers:
if output_hidden_states:
all_hidden_states += (hidden_states,) | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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,
... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
position_embeddings=position_embeddings,
**flash_attn_kwargs,
) | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if output_router_logits:
all_router_logits += (layer_outputs[-1],)
hidden_states = self.norm(hidden_states)
# add hidden states from the last decoder l... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_leng... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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 or using_sliding_window_cache)
and not output_attentions
):
if Atten... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
# SlidingWindowCache or StaticCache
if using_sliding_window_cache or using_static_cache:
target_length = past_key_values.get_max_cache_shape()
... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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,
config: MixtralCon... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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.
target... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
The model's configuration class
past_key_values (`Cache`):
The cache class that is being used currently to generate
"""
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 ... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
sliding_attend_mask = torch.arange(target_length, device=device) <= (
cache_position.reshape(-1, 1) - config.sliding_window
)
diagonal_attend_mas... | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask | 4,012 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 4,013 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralForCausalLM(MixtralPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = MixtralModel(config)
self.vocab_size = config.vocab_size
self.lm... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
def set_decoder(self, decoder):
self.model = decoder
def get_decoder(self):
return self.model | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
@add_start_docstrings_to_model_forward(MIXTRAL_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: Option... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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
... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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."
```"""
... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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,
... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
aux_loss = None
if output_router_logits:
aux_loss = load_balancing_loss_func(
outputs.router_logits if return_dict else outputs[-1],
self.num_experts,
self.num_experts_per_tok,
attention_mask,
)
if labels is not ... | 4,014 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralForSequenceClassification(MixtralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MixtralModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize wei... | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
@add_start_docstrings_to_model_forward(MIXTRAL_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.... | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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 | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,015 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralForTokenClassification(MixtralPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = MixtralModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.... | 4,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
@add_start_docstrings_to_model_forward(MIXTRAL_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,
at... | 4,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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).
"""
... | 4,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,016 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralForQuestionAnswering(MixtralPreTrainedModel):
base_model_prefix = "model"
def __init__(self, config):
super().__init__(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
self.model = MixtralModel(config) # diff with Llama: transformer->model
# Initialize w... | 4,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
@add_start_docstrings_to_model_forward(MIXTRAL_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[t... | 4,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.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... | 4,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
outputs = self.model(
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_states,
... | 4,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
return QuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
) | 4,017 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/modeling_mixtral.py |
class MixtralConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
Mixtral model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a sim... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Mixtral model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MixtralModel`]
hidden_size (`int`, *optional*, defaults to 4096):
Di... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.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... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
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 ... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 1000000.0):
The base period of the RoPE embeddings.
sliding_window (`int`, *optional*):
Sliding window attention window size. If not specified, will default to `4096`... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
allow the model to output the auxiliary loss. See [here]() for more details
router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
The aux loss factor for the total loss.
router_jitter_noise (`float`, *optional*, defaults to 0.0):
Amount of noise to add to the router. | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
```python
>>> from transformers import MixtralModel, MixtralConfig
>>> # Initializing a Mixtral 7B style configuration
>>> configuration = MixtralConfig()
>>> # Initializing a model from the Mixtral 7B style configuration
>>> model = MixtralModel(configuration)
>>> # Accessing the model confi... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
def __init__(
self,
vocab_size=32000,
hidden_size=4096,
intermediate_size=14336,
num_hidden_layers=32,
num_attention_heads=32,
num_key_value_heads=8,
head_dim=None,
hidden_act="silu",
max_position_embeddings=4096 * 32,
initializer_r... | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
self.num_attention_heads = num_attention_heads
self.sliding_window = sliding_window | 4,018 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mixtral/configuration_mixtral.py |
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