[tests] expand flex-attn test for vision models (#38434)
* expand the test for VLMs * typo * mark models `supports_flex` + expand test for additional kwargs * flex attn for refactored vision models * fix copies * fix * unskip * style * address comments
This commit is contained in:
Raushan Turganbay
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de4cf5a38e
commit
bf68dd9e6e
@@ -12,24 +12,52 @@ from torch import nn
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from ...cache_utils import Cache, HybridCache, StaticCache
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from ...generation import GenerationMixin
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_outputs import BaseModelOutputWithPast
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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ModelOutput,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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replace_return_docstrings,
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)
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from ..auto import AutoModel, AutoModelForCausalLM
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from ...processing_utils import Unpack
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from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torchdynamo_compiling
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from ..auto import AutoModel
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from .configuration_new_task_model import NewTaskModelConfig
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_CONFIG_FOR_DOC = "NewTaskModelConfig"
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@dataclass
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class NewTaskModelModelOutputWithPast(BaseModelOutputWithPast):
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"""
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Base class for NewTaskModel outputs, with hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`)
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Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
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`past_key_values` input) to speed up sequential decoding.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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image_hidden_states (`torch.FloatTensor`, *optional*):
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A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
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image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
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"""
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image_hidden_states: Optional[torch.FloatTensor] = None
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@dataclass
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class NewTaskModelCausalLMOutputWithPast(ModelOutput):
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"""
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Base class for NewTaskModelcausal language model (or autoregressive) outputs.
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Base class for NewTaskModel causal language model (or autoregressive) outputs.
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Args:
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loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
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@@ -77,30 +105,10 @@ class NewTaskModelMultiModalProjector(nn.Module):
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return hidden_states
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NEW_TASK_MODEL_START_DOCSTRING = r"""
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This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
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library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
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etc.)
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This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
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Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
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and behavior.
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Parameters:
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config ([`NewTaskModelConfig`] or [`NewTaskModelVisionConfig`]):
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Model configuration class with all the parameters of the model. Initializing with a config file does not
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load the weights associated with the model, only the configuration. Check out the
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[`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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@add_start_docstrings(
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"The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
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NEW_TASK_MODEL_START_DOCSTRING,
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)
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@auto_docstring
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class NewTaskModelPreTrainedModel(PreTrainedModel):
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config_class = NewTaskModelConfig
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base_model_prefix = "model"
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base_model_prefix = ""
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supports_gradient_checkpointing = True
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_no_split_modules = ["NewTaskModelMultiModalProjector"]
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_skip_keys_device_placement = "past_key_values"
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@@ -109,6 +117,8 @@ class NewTaskModelPreTrainedModel(PreTrainedModel):
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_supports_static_cache = True
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_supports_flash_attn_2 = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_supports_attention_backend = True
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def _init_weights(self, module):
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# important: this ported version of NewTaskModelisn't meant for training from scratch - only
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@@ -121,102 +131,24 @@ class NewTaskModelPreTrainedModel(PreTrainedModel):
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module.bias.data.zero_()
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NEW_TASK_MODEL_INPUTS_DOCSTRING = r"""
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
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it.
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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[What are input IDs?](../glossary#input-ids)
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)):
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The tensors corresponding to the input images. Pixel values can be obtained using
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[`AutoImageProcessor`]. See [`SiglipImageProcessor.__call__`] for details ([]`NewTaskModelProcessor`] uses
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[`SiglipImageProcessor`] for processing images).
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attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
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Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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[What are attention masks?](../glossary#attention-mask)
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Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
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[`PreTrainedTokenizer.__call__`] for details.
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If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
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`past_key_values`).
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If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
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and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
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information on the default strategy.
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- 1 indicates the head is **not masked**,
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- 0 indicates the head is **masked**.
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position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
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config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
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past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
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Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
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`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
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`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
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Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
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blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
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If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
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`decoder_input_ids` of shape `(batch_size, sequence_length)`.
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inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
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Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
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is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
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model's internal embedding lookup matrix.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
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`past_key_values`).
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
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tensors for more detail.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
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this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
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the complete sequence length.
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"""
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@add_start_docstrings(
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"""The NEW_TASK_MODEL model which consists of a vision backbone and a language model.""",
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NEW_TASK_MODEL_START_DOCSTRING,
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@auto_docstring(
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custom_intro="""
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The Base NewTaskModel model which consists of a vision backbone and a language model withou language modeling head.,
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"""
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)
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class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
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class NewTaskModelModel(NewTaskModelPreTrainedModel):
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_checkpoint_conversion_mapping = {"language_model.model": "language_model"}
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def __init__(self, config):
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def __init__(self, config: NewTaskModelConfig):
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super().__init__(config)
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self.vision_tower = AutoModel.from_config(config=config.vision_config)
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self.multi_modal_projector = NewTaskModelMultiModalProjector(config)
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self.vocab_size = config.text_config.vocab_size
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language_model = AutoModelForCausalLM.from_config(config=config.text_config)
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if language_model._tied_weights_keys is not None:
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self._tied_weights_keys = [f"language_model.{k}" for k in language_model._tied_weights_keys]
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language_model = AutoModel.from_config(config=config.text_config)
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self.language_model = language_model
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self.pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else -1
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self.embedding_dim = self.config.embedding_dim
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self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
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if self.language_model._tied_weights_keys is not None:
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self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
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self.post_init()
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def get_input_embeddings(self):
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@@ -225,18 +157,6 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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def set_input_embeddings(self, value):
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self.language_model.set_input_embeddings(value)
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def get_output_embeddings(self):
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return self.language_model.get_output_embeddings()
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def set_output_embeddings(self, new_embeddings):
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self.language_model.set_output_embeddings(new_embeddings)
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def set_decoder(self, decoder):
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self.language_model.set_decoder(decoder)
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def get_decoder(self):
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return self.language_model.get_decoder()
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def _update_causal_mask(
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self,
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attention_mask,
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@@ -321,8 +241,191 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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image_features = image_features / (self.config.text_config.hidden_size**0.5)
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return image_features
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@add_start_docstrings_to_model_forward(NEW_TASK_MODEL_INPUTS_DOCSTRING)
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@replace_return_docstrings(output_type=NewTaskModelCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
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@can_return_tuple
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@auto_docstring
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def forward(
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self,
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input_ids: torch.LongTensor = None,
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pixel_values: torch.FloatTensor = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None,
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token_type_ids: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.LongTensor] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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labels: Optional[torch.LongTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Union[Tuple, NewTaskModelModelOutputWithPast]:
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r"""
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.text_config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
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(masked), the loss is only computed for the tokens with labels in `[0, ..., config.text_config.vocab_size]`.
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Example:
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import AutoProcessor, NewTaskModelForConditionalGeneration
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>>> model = NewTaskModelForConditionalGeneration.from_pretrained("google/new_task_model2-3b-mix-224")
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>>> processor = AutoProcessor.from_pretrained("google/new_task_model2-3b-mix-224")
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>>> prompt = "Where is the cat standing?"
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>>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt")
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>>> # Generate
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>>> generate_ids = model.generate(**inputs,)
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>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
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"Where is the cat standing?\nsnow"
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```"""
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if (input_ids is None) ^ (inputs_embeds is not None):
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raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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is_training = token_type_ids is not None and labels is not None
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# Replace image id woth PAD if the image token if OOV, to avoid index-errors
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if input_ids is not None and self.config.image_token_id >= self.vocab_size:
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special_image_mask = input_ids == self.config.image_token_id
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llm_input_ids = input_ids.clone()
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llm_input_ids[special_image_mask] = 0
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else:
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llm_input_ids = input_ids
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if inputs_embeds is None:
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inputs_embeds = self.get_input_embeddings()(llm_input_ids)
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if cache_position is None:
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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cache_position = torch.arange(
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past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
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)
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if position_ids is None:
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position_ids = cache_position.unsqueeze(0) + 1 # NewTaskModel positions are 1-indexed
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# Merge text and images
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if pixel_values is not None:
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image_features = self.get_image_features(pixel_values)
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if input_ids is None:
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special_image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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)
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else:
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special_image_mask = (input_ids == self.config.image_token_id).unsqueeze(-1)
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special_image_mask = special_image_mask.expand_as(inputs_embeds).to(inputs_embeds.device)
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if not is_torchdynamo_compiling() and inputs_embeds[special_image_mask].numel() != image_features.numel():
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image_tokens_in_text = (special_image_mask).sum(dim=1).sum(dim=0)[0]
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raise ValueError(
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f"Number of images does not match number of special image tokens in the input text. "
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f"Got {image_tokens_in_text} image tokens in the text but {image_features.shape[0] * image_features.shape[1]} "
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"tokens from image embeddings."
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)
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image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
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inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
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causal_mask = self._update_causal_mask(
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attention_mask, token_type_ids, past_key_values, cache_position, inputs_embeds, is_training
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)
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outputs = self.language_model(
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attention_mask=causal_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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inputs_embeds=inputs_embeds,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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cache_position=cache_position,
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**kwargs,
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)
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return NewTaskModelModelOutputWithPast(
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last_hidden_state=outputs.last_hidden_state,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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image_hidden_states=image_features if pixel_values is not None else None,
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)
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@auto_docstring(
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custom_intro="""
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The Base NewTaskModel model which consists of a vision backbone and a language model without language modeling head.,
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"""
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)
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class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
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_checkpoint_conversion_mapping = {
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"^language_model.model": "model.language_model",
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"^vision_tower": "model.vision_tower",
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"^multi_modal_projector": "model.multi_modal_projector",
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"^language_model.lm_head": "lm_head",
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}
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_tied_weights_keys = ["lm_head.weight"]
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||||
main_input_name: ClassVar[str] = "doc_input_ids" # transformers-related
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = NewTaskModelModel(config)
|
||||
self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
|
||||
|
||||
self.embedding_dim = self.config.embedding_dim
|
||||
self.custom_text_proj = nn.Linear(self.config.text_config.hidden_size, self.embedding_dim)
|
||||
|
||||
if self.language_model._tied_weights_keys is not None:
|
||||
self._tied_weights_keys = [f"model.language_model.{k}" for k in self.language_model._tied_weights_keys]
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.get_input_embeddings()
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.set_input_embeddings(value)
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
# Make modules available throught conditional class for BC
|
||||
@property
|
||||
def language_model(self):
|
||||
return self.model.language_model
|
||||
|
||||
@property
|
||||
def vision_tower(self):
|
||||
return self.model.vision_tower
|
||||
|
||||
@property
|
||||
def multi_modal_projector(self):
|
||||
return self.model.multi_modal_projector
|
||||
|
||||
@can_return_tuple
|
||||
@auto_docstring
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
@@ -341,19 +444,10 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
|
||||
num_logits_to_keep: int = 0,
|
||||
) -> Union[Tuple, NewTaskModelCausalLMOutputWithPast]:
|
||||
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.text_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.text_config.vocab_size]`.
|
||||
|
||||
logits_to_keep (`int` or `torch.Tensor`, *optional*):
|
||||
If an `int`, compute logits for the last `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, which becomes pretty significant for long sequences or large vocabulary size.
|
||||
If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension.
|
||||
This is useful when using packed tensor format (single dimension for batch and sequence length).
|
||||
|
||||
Returns:
|
||||
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.text_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.text_config.vocab_size]`.
|
||||
|
||||
Example:
|
||||
|
||||
@@ -400,7 +494,8 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
|
||||
# L2 normalization
|
||||
embeddings = proj / proj.norm(dim=-1, keepdim=True) # (batch_size, sequence_length, dim)
|
||||
|
||||
embeddings = embeddings * attention_mask.unsqueeze(-1) # (batch_size, sequence_length, dim)
|
||||
if attention_mask is not None:
|
||||
embeddings = embeddings * attention_mask.unsqueeze(-1) # (batch_size, sequence_length, dim)
|
||||
|
||||
return (embeddings,) + vlm_outputs
|
||||
|
||||
@@ -420,7 +515,7 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
|
||||
**kwargs,
|
||||
):
|
||||
# Overwritten -- custom `position_ids` and `pixel_values` handling
|
||||
model_inputs = self.language_model.prepare_inputs_for_generation(
|
||||
model_inputs = super().prepare_inputs_for_generation(
|
||||
input_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
@@ -443,13 +538,68 @@ class NewTaskModelForNewTask(NewTaskModelPreTrainedModel, GenerationMixin):
|
||||
is_training = token_type_ids is not None and labels is not None
|
||||
if cache_position[0] == 0 and isinstance(past_key_values, HybridCache):
|
||||
input_tensor = inputs_embeds if inputs_embeds is not None else input_ids
|
||||
causal_mask = self._update_causal_mask(
|
||||
causal_mask = self.model._update_causal_mask(
|
||||
attention_mask, token_type_ids, past_key_values, cache_position, input_tensor, is_training
|
||||
)
|
||||
model_inputs["attention_mask"] = causal_mask
|
||||
|
||||
return model_inputs
|
||||
|
||||
@staticmethod
|
||||
def _prepare_4d_causal_attention_mask_with_cache_position(
|
||||
attention_mask: torch.Tensor,
|
||||
sequence_length: int,
|
||||
target_length: int,
|
||||
dtype: torch.dtype,
|
||||
cache_position: torch.Tensor,
|
||||
batch_size: int,
|
||||
**kwargs,
|
||||
):
|
||||
"""
|
||||
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
||||
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
||||
|
||||
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_length (`int`):
|
||||
The target length: when generating with static cache, the mask should be as long as the static cache,
|
||||
to account for the 0 padding, the part of the cache that is not filled yet.
|
||||
dtype (`torch.dtype`):
|
||||
The dtype to use for the 4D attention mask.
|
||||
cache_position (`torch.Tensor`):
|
||||
Indices depicting the position of the input sequence tokens in the sequence.
|
||||
batch_size (`torch.Tensor`):
|
||||
Batch size.
|
||||
"""
|
||||
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(
|
||||
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
||||
)
|
||||
if sequence_length != 1:
|
||||
causal_mask = torch.triu(causal_mask, diagonal=1)
|
||||
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
||||
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
||||
if attention_mask is not None:
|
||||
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
||||
mask_length = attention_mask.shape[-1]
|
||||
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
||||
causal_mask.device
|
||||
)
|
||||
padding_mask = padding_mask == 0
|
||||
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
||||
padding_mask, min_dtype
|
||||
)
|
||||
|
||||
return causal_mask
|
||||
|
||||
def resize_token_embeddings(
|
||||
self, new_num_tokens: Optional[int] = None, pad_to_multiple_of=None, mean_resizing=True
|
||||
) -> nn.Embedding:
|
||||
|
||||
Reference in New Issue
Block a user