text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
@torch.no_grad()
def forward(self, position_ids: torch.Tensor = None, past_key_values_length: int = 0):
bsz, seq_len = position_ids.size()
position_ids += self.offset
# Expand embeddings if needed. `position_ids.max()` is NOT used to keep torch.fx compatibility.
max_pos = 2 + seq_le... | 10,550 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class XGLMAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
):
super().__init__()
se... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size() | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# get query proj
query_states = self.q_proj(hidden_states) * self.scaling
# get key, value proj
if is_cross_attention and past_key_value is not None:
# reuse k,v, cross_attentions
key_states = past_key_value[0]
value_states = past_key_value[1]
elif is_... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
if self.is_decoder:
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
# Further calls to cross_attention layer can then reuse all cross-attention
# key/value_states (first "if" case)
# if uni-directional self-attention (d... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
src_len = key_states.size(1)
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
if attn_weights.dtype == torch.float16:
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
else:
attn_weights = nn.functio... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
if output_attentions:
# this operation is a bit awkward, but it's required to
# make sure that attn_weights keeps its gradient.
# In order to do so, attn_weights have to be reshaped
# twice and have to be reused in the following
attn_weights_reshaped = attn_we... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
attn_output = attn_output.transpose(1, 2)
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned aross GPUs when using tensor-parallelism.
... | 10,551 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class XGLMDecoderLayer(nn.Module):
def __init__(self, config: XGLMConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = XGLMAttention(
embed_dim=self.embed_dim,
num_heads=config.attention_heads,
dropout=config.attention_dropout,
... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
self.final_layer_norm = nn.LayerNorm(self.embed_dim) | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# Copied from transformers.models.mbart.modeling_mbart.MBartDecoderLayer.forward
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor]... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
layer_head_mask (`torch.FloatT... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
hidden_states = self.self_attn_layer_norm(hidden_states) | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# Self Attention
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
# add present self-attn cache to positions 1,2 of present_key_value tuple
hidden_states, self_attn_w... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# Cross-Attention Block
cross_attn_present_key_value = None
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = hidden_states
hidden_states = self.encoder_attn_layer_norm(hidden_states) | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn(
hidden_states=hidden_st... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# Fully Connected
residual = hidden_states
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.activation_fn(self.fc1(hidden_states))
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
hidden_states = self... | 10,552 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class XGLMPreTrainedModel(PreTrainedModel):
config_class = XGLMConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["XGLMDecoderLayer"]
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module, nn.Linear):
... | 10,553 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class XGLMModel(XGLMPreTrainedModel):
"""
Transformer decoder consisting of *config.num_layers* layers. Each layer is a [`XGLMDecoderLayer`]
Args:
config: XGLMConfig
embed_tokens (nn.Embedding): output embedding
"""
def __init__(self, config: XGLMConfig, embed_tokens: Optional[nn.E... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
self.embed_positions = XGLMSinusoidalPositionalEmbedding(
config.max_position_embeddings,
config.d_model,
config.pad_token_id,
)
self.layers = nn.ModuleList([XGLMDecoderLayer(config) for _ in range(config.num_layers)])
self.layer_norm = nn.LayerNorm(config.d_m... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = Non... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidd... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# retrieve input_ids and inputs_embeds
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
if position_ids is None:
position_ids = torch.arange(
past_key_values_length,
input_shape[-1] + past_key_values_length,
dtype=torch.long,
device=input_ids.device if input_ids is not None else inputs_embeds.device,
)
posi... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length)
hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training)
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_head_mask"]):
if attn_mask is not None:
if attn_mask.size()[0] != len(self.layers):
... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
past_key_value = past_key_values[idx] if past_key_values is not None else None | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
decoder_layer.__call__,
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
cross_attn_layer_head_mask=(
cross_attn_head_mask[idx] if cross_attn_head_mask is not None else None
),
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
if use_cache:
next_decoder_cache += (layer_outputs[3 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
hid... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
next_cache = next_decoder_cache if use_cache else None
if not return_dict:
return tuple(
v
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_cross_attentions]
if v is not None
)
return BaseModelOutputWithPa... | 10,554 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class XGLMForCausalLM(XGLMPreTrainedModel, GenerationMixin):
base_model_prefix = "model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = XGLMModel(config)
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=F... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
@add_start_docstrings_to_model_forward(XGLM_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=CausalLMOutputWithCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
r"""
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 (se... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
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 els... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.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,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=enc... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
loss = None
if labels is not None:
# shift labels and add a pad token to the end
shift_labels = labels.new_zeros(labels.shape)
shift_labels[:, :-1] = labels[:, 1:].clone()
shift_labels[:, -1] = self.config.pad_token_id
loss_fct = CrossEntropyLoss()
... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
@staticmethod
def _reorder_cache(past_key_values, beam_idx):
reordered_past = ()
for layer_past in past_key_values:
reordered_past += (
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
)
return reordered_p... | 10,555 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/xglm/modeling_xglm.py |
class BitLinear(nn.Module):
def __init__(self, in_features: int, out_features: int, bias: bool, device=None, dtype=None):
super().__init__()
self.dtype = dtype
self.in_features = in_features
self.out_features = out_features
self.register_buffer(
"weight",
... | 10,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/bitnet.py |
@torch.compile
def activation_quant(self, input, num_bits=8):
"""
Activation function : Performs symmetric, per-token quantization on the input activations.
Parameters:
-----------
x : torch.Tensor
Input activations to be quantized.
num_bits : int, optiona... | 10,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/bitnet.py |
@torch.compile
def post_quant_process(self, input, input_scale, weight_scale):
out = input / (input_scale * weight_scale)
return out
def forward(self, input):
w = self.weight
w_quant = unpack_weights(w, dtype=self.dtype)
input_quant, input_scale = self.activation_quant(i... | 10,556 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/bitnet.py |
class HfDeepSpeedConfig(DeepSpeedConfig):
"""
This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
things like the Trainer object is ... | 10,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
def __init__(self, config_file_or_dict):
# set global weakref object
set_hf_deepspeed_config(self)
dep_version_check("accelerate")
dep_version_check("deepspeed")
super().__init__(config_file_or_dict) | 10,557 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
"""
The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
same lifespan as the latter.
"""
def __init__(self, config_file_or_dict):
super().__init__(config_file_or_dict)
... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
config values and if mismatched add the entry to `self.mismatched` - will assert during
`trainer_config_finalize` for one or more mismatches.
"""
config, ds_key = self.find_config_node(ds_key_long... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
def trainer_config_process(self, args, auto_find_batch_size=False):
"""
Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
creation.
"""
# DeepSpeed does:
# train_batch_size = world_size * train_micro_batch_size_per_gpu ... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm") | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
self.fill_match(
"optimizer.params.betas",
[args.adam_beta1, args.adam_beta2],
"adam_beta1+adam_beta2",
)
self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon")
... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
if args.save_on_each_node:
# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
self.config["checkpoint"] = self.config.get("checkpoint", {})
self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node
# amp: s... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval")
# deepspeed's default mode is fp16 unless there is a config that says differently
if self.is_true("bf16.enabled"):
self._dtype = torch.bfloat16
elif self.is_false("fp16.enabled"):
... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
# deal with config keys that use `auto` value and rely on model's hidden_size
hidden_size_based_keys = [
"zero_optimization.reduce_bucket_size",
"zero_optimization.stage3_prefetch_bucket_size",
"zero_optimization.stage3_param_persistence_threshold",
]
hidden_s... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
if len(hidden_size_auto_keys) > 0:
if hasattr(model.config, "hidden_size"):
hidden_size = model.config.hidden_size
elif hasattr(model.config, "hidden_sizes"):
# if there are many hidden sizes pick the largest one
hidden_size = max(model.config.hidd... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
"`auto` values for these keys with an integer value of your choice."
) | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
self.fill_only("zero_optimization.reduce_bucket_size", hidden_size * hidden_size)
if self.is_zero3():
# automatically assign the optimal config values based on model config
self.fill_only(
"zero_optimization.stage3_prefetch_bucket_size",
... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
if len(self.mismatches) > 0:
mismatches = "\n".join(self.mismatches)
raise ValueError(
"Please correct the following DeepSpeed config values that mismatch TrainingArguments"
f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'a... | 10,558 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/deepspeed.py |
class HiggsLinear(torch.nn.Module):
def __init__(
self,
in_features: int,
out_features: int,
num_bits: int,
bias=True,
dtype: torch.dtype = None,
device: torch.device = None,
group_size: int = 256,
hadamard_size: int = 1024,
):
supe... | 10,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/higgs.py |
self.weight = nn.Parameter(
torch.empty((out_features * num_bits // 16, in_features), dtype=torch.int16, device=device),
requires_grad=False,
)
self.scales = nn.Parameter(
torch.empty((out_features, in_features // group_size), dtype=dtype, device=device), requires_gra... | 10,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/higgs.py |
if self.workspace is None:
raise Exception("Workspace must be set before calling forward")
return flute.qgemm_hadamard(
x,
self.weight,
self.scales,
self.tables,
self.tables2.view(dtype=torch.float32),
self.workspace,
... | 10,559 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/higgs.py |
class PeftAdapterMixin:
"""
A class containing all functions for loading and using adapters weights that are supported in PEFT library. For
more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT
library: https://huggingface.co/docs/peft/index
... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
- Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model
- Attach new adapters in the model and train them with Trainer or by your own.
- Attach multiple adapters and iteratively activate / deactivate them
- Activate / deactivate all adapters from the model.
- G... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
def load_adapter(
self,
peft_model_id: Optional[str] = None,
adapter_name: Optional[str] = None,
revision: Optional[str] = None,
token: Optional[str] = None,
device_map: Optional[str] = "auto",
max_memory: Optional[str] = None,
offload_folder: Optional[str... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Args:
peft_model_id (`str`, *optional*):
The identifier of the model to look for on the Hub, or a local path to the saved adapter config file
and adapter weights.
adapter_name (`str`, *optional*):
The adapter name to use. If not set, will use the d... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
token (`str`, `optional`):
Whether to use authentication token to load the remote folder. Useful to load private repositories
that are on HuggingFace Hub. You might need to call `huggingface-cli login` and paste your tokens to
cache it.
device_map (`str` or `D... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
methods. This argument is used in case users directly pass PEFT state dicts
adapter_state_dict (`Dict[str, torch.Tensor]`, *optional*):
The state dict of the adapter to load. This argument is used in case users directly pass PEFT state
dicts
low_cpu_mem_usage (`bo... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
check_peft_version(min_version=MIN_PEFT_VERSION) | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# peft only supports low_cpu_mem_usage starting from v0.13.0
peft_load_kwargs = {}
if low_cpu_mem_usage:
min_version_lcmu = "0.13.0"
if version.parse(importlib.metadata.version("peft")) >= version.parse(min_version_lcmu):
peft_load_kwargs["low_cpu_mem_usage"] = lo... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
if self._hf_peft_config_loaded and adapter_name in self.peft_config:
raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
if peft_model_id is None and (adapter_state_dict is None and peft_config is None):
raise ValueError(
"Y... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# We keep `revision` in the signature for backward compatibility
if revision is not None and "revision" not in adapter_kwargs:
adapter_kwargs["revision"] = revision
elif revision is not None and "revision" in adapter_kwargs and revision != adapter_kwargs["revision"]:
logger.error... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
if adapter_config_file is None:
raise ValueError(
f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the "
"adapter model."
)
peft_config = PeftConfig.from_pretrained(
peft_... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# We need to pre-process the state dict to remove unneeded prefixes - for backward compatibility
processed_adapter_state_dict = {}
prefix = "base_model.model."
for key, value in adapter_state_dict.items():
if key.startswith(prefix):
new_key = key[len(prefix) :]
... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
if incompatible_keys is not None:
err_msg = ""
origin_name = peft_model_id if peft_model_id is not None else "state_dict"
# Check for unexpected keys.
if hasattr(incompatible_keys, "unexpected_keys") and len(incompatible_keys.unexpected_keys) > 0:
err_msg ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# Check for missing keys.
missing_keys = getattr(incompatible_keys, "missing_keys", None)
if missing_keys:
# Filter missing keys specific to the current adapter, as missing base model keys are expected.
lora_missing_keys = [k for k in missing_keys if "lora_" in k ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# Re-dispatch model and hooks in case the model is offloaded to CPU / Disk.
if (
(getattr(self, "hf_device_map", None) is not None)
and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0)
and len(self.peft_config) == 1
):
self._dispat... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Adds a fresh new adapter to the current model for training purpose. If no adapter name is passed, a default
name is assigned to the adapter to follow the convention of PEFT library (in PEFT we use "default" as the
default adapter name).
Args:
adapter_config (`~peft.PeftConfig`):
... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
if not self._hf_peft_config_loaded:
self._hf_peft_config_loaded = True
elif adapter_name in self.peft_config:
raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.")
if not isinstance(adapter_config, PeftConfig):
raise TypeE... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
def set_adapter(self, adapter_name: Union[List[str], str]) -> None:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Sets a specific adapter by forcing the model to use ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Args:
adapter_name (`Union[List[str], str]`):
The name of the adapter to set. Can be also a list of strings to set multiple adapters.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter load... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
from peft.tuners.tuners_utils import BaseTunerLayer
from peft.utils import ModulesToSaveWrapper
_adapters_has_been_set = False
for _, module in self.named_modules():
if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)):
# For backward compatbility with prev... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Disable all adapters that are attached to the model. This leads to inferring with the base model only.
"""
check_peft_version(min_version=MIN_PEFT_VERSION)
if not self._hf_peft_config_loaded:
raise ValueError("No adapter loaded. Please load an adapter first.")
from peft.tun... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
def enable_adapters(self) -> None:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Enable adapters that are attached to the model.
"""
check_peft_versio... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
def active_adapters(self) -> List[str]:
"""
If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT
official documentation: https://huggingface.co/docs/peft
Gets the current active adapters of the model. In case of multi-adapter inferenc... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
for _, module in self.named_modules():
if isinstance(module, BaseTunerLayer):
active_adapters = module.active_adapter
break
# For previous PEFT versions
if isinstance(active_adapters, str):
active_adapters = [active_adapters]
return activ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter.
If no adapter_name is passed, the active adapter is used.
Args:
adapter_name (`str`, *optional*):
The name of the adapter to get the state dict from. If no name is... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
def _dispatch_accelerate_model(
self,
device_map: str,
max_memory: Optional[int] = None,
offload_folder: Optional[str] = None,
offload_index: Optional[int] = None,
) -> None:
"""
Optional re-dispatch the model and attach new hooks to the model in case the mode... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
Args:
device_map (`str` or `Dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*):
A map that specifies where each submodule should go. It doesn't need to be refined to each
parameter/buffer name, once a given module name is inside, every submodule... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
more information about each option see [designing a device
map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
max_memory (`Dict`, ... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/
if "offload_index" in inspect.signature(dispatch_model).parameters:
dispatch_model_kwargs["offload_index"] = offload_index | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
no_split_module_classes = self._no_split_modules
if device_map != "sequential":
max_memory = get_balanced_memory(
self,
max_memory=max_memory,
no_split_module_classes=no_split_module_classes,
low_zero=(device_map == "balanced_low_0"),
... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16
).to("cuda")
pipeline.load_lora_weights(
"jbilcke-hf/sdxl-cinema... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# Check that all adapter names are present in the config
missing_adapters = [name for name in adapter_names if name not in self.peft_config]
if missing_adapters:
raise ValueError(
f"The following adapter(s) are not present and cannot be deleted: {', '.join(missing_adapters)}"... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
# For transformers integration - we need to pop the adapter from the config
if getattr(self, "_hf_peft_config_loaded", False) and hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None)
# In case all adapters are deleted, we need to delete the config
# and mak... | 10,560 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/peft.py |
class FbgemmFp8Linear(torch.nn.Module):
def __init__(self, in_features, out_features, bias, weight_dtype=torch.float32):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.register_buffer("weight", torch.zeros((out_features, in_features), dtype=t... | 10,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/fbgemm_fp8.py |
def forward(self, x):
num_tokens = None
# quantize_fp8_per_row will squash the leading dimensions, so save the desired shape here
output_shape = (*x.shape[:-1], -1)
# x_quantized and x_scale are not necessarily on the same device as x, this is an issue.
# https://github.com/pytor... | 10,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/fbgemm_fp8.py |
# The computation still happens on the device where self.weight is even if x_quantized is not on the same device as self.weight
output = torch.ops.fbgemm.f8f8bf16_rowwise(
x_quantized, self.weight, x_scale, self.weight_scale, use_fast_accum=True
)
output = output + self.bias if self.... | 10,561 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/fbgemm_fp8.py |
class GGUFTokenizerSkeleton:
def __init__(self, dict_):
for k, v in dict_.items():
setattr(self, k, v)
if not hasattr(self, "merges"):
if not hasattr(self, "tokens") or not hasattr(self, "scores"):
raise ValueError(
"tokens and scores need... | 10,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
logger.warning("Merges were not in checkpoint, building merges on the fly.")
merges = []
for merge, piece_score in tqdm(vocab.items()):
local = []
for index in range(1, len(merge)):
piece_l, piece_r = merge[:index], merge[index:]
... | 10,562 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/integrations/ggml.py |
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