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"""PyTorch GPTNeoX model.""" |
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|
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from typing import Optional, Tuple, Union |
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|
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import torch |
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import torch.utils.checkpoint |
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from packaging import version |
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from torch import nn |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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|
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from transformers.activations import ACT2FN |
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from transformers.cache_utils import Cache, DynamicCache, StaticCache |
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from transformers.file_utils import ( |
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add_code_sample_docstrings, |
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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 transformers.generation import GenerationMixin |
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from transformers.modeling_attn_mask_utils import AttentionMaskConverter |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPast, |
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CausalLMOutputWithPast, |
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QuestionAnsweringModelOutput, |
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SequenceClassifierOutputWithPast, |
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TokenClassifierOutput, |
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) |
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from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS |
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from transformers.modeling_utils import PreTrainedModel |
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from transformers.utils import ( |
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get_torch_version, |
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is_flash_attn_2_available, |
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is_flash_attn_greater_or_equal_2_10, |
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logging, |
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) |
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from .config_custom import GPTNeoXConfig |
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|
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if is_flash_attn_2_available(): |
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from transformers.modeling_flash_attention_utils import _flash_attention_forward |
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|
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logger = logging.get_logger(__name__) |
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_CHECKPOINT_FOR_DOC = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM" |
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_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b" |
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_CONFIG_FOR_DOC = "GPTNeoXConfig" |
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def _prepare_4d_causal_attention_mask_with_cache_position( |
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attention_mask: torch.Tensor, |
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sequence_length: int, |
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target_length: int, |
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dtype: torch.dtype, |
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device: torch.device, |
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min_dtype: float, |
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cache_position: torch.Tensor, |
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batch_size: int, |
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): |
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""" |
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Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape |
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`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. |
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|
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Args: |
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attention_mask (`torch.Tensor`): |
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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)`. |
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sequence_length (`int`): |
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The sequence length being processed. |
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target_length (`int`): |
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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. |
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dtype (`torch.dtype`): |
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The dtype to use for the 4D attention mask. |
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device (`torch.device`): |
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The device to plcae the 4D attention mask on. |
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min_dtype (`float`): |
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The minimum value representable with the dtype `dtype`. |
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cache_position (`torch.Tensor`): |
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Indices depicting the position of the input sequence tokens in the sequence. |
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batch_size (`torch.Tensor`): |
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Batch size. |
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""" |
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if attention_mask is not None and attention_mask.dim() == 4: |
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|
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causal_mask = attention_mask |
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else: |
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causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) |
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if sequence_length != 1: |
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causal_mask = torch.triu(causal_mask, diagonal=1) |
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causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) |
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causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) |
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if attention_mask is not None: |
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causal_mask = causal_mask.clone() |
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mask_length = attention_mask.shape[-1] |
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padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] |
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padding_mask = padding_mask == 0 |
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causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( |
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padding_mask, min_dtype |
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) |
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return causal_mask |
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class GPTNeoXPreTrainedModel(PreTrainedModel): |
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""" |
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained |
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models. |
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""" |
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|
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config_class = GPTNeoXConfig |
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base_model_prefix = "gpt_neox" |
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supports_gradient_checkpointing = True |
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_no_split_modules = ["GPTNeoXLayer"] |
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_skip_keys_device_placement = "past_key_values" |
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_supports_flash_attn_2 = True |
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_supports_cache_class = True |
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_supports_quantized_cache = True |
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_supports_static_cache = True |
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_supports_sdpa = True |
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|
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def _init_weights(self, module): |
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"""Initialize the weights""" |
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if isinstance(module, nn.Linear): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.bias is not None: |
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module.bias.data.zero_() |
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elif isinstance(module, nn.Embedding): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.padding_idx is not None: |
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module.weight.data[module.padding_idx].zero_() |
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elif isinstance(module, nn.LayerNorm): |
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module.bias.data.zero_() |
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module.weight.data.fill_(1.0) |
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|
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class GPTNeoXAttention(nn.Module): |
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def __init__(self, config, layer_idx=None): |
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super().__init__() |
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self.config = config |
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self.num_attention_heads = config.num_attention_heads |
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self.hidden_size = config.hidden_size |
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if self.hidden_size % self.num_attention_heads != 0: |
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raise ValueError( |
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"The hidden size is not divisble by the number of attention heads! Make sure to update them" |
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) |
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self.head_size = self.hidden_size // self.num_attention_heads |
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self.rotary_ndims = int(self.head_size * config.rotary_pct) |
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self.rope_theta = config.rotary_emb_base |
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self._init_bias(config.max_position_embeddings) |
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|
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self.register_buffer("masked_bias", torch.tensor(-1e9), persistent=False) |
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self.rotary_emb = GPTNeoXRotaryEmbedding(config=self.config) |
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|
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if layer_idx is None: |
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logger.warning_once( |
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " |
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " |
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"when creating this class." |
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) |
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self.norm_factor = self.head_size**-0.5 |
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self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size, bias=config.attention_bias) |
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self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=config.attention_bias) |
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self.attention_dropout = nn.Dropout(config.attention_dropout) |
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self.is_causal = True |
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self.layer_idx = layer_idx |
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|
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def _init_bias(self, max_positions, device=None): |
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self.register_buffer( |
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"bias", |
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torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( |
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1, 1, max_positions, max_positions |
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), |
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persistent=False, |
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) |
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if device is not None: |
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self.bias = self.bias.to(device) |
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|
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def forward( |
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self, |
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hidden_states: torch.FloatTensor, |
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attention_mask: torch.FloatTensor, |
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position_ids: torch.LongTensor, |
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head_mask: Optional[torch.FloatTensor] = None, |
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layer_past: Optional[Cache] = None, |
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use_cache: Optional[bool] = False, |
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output_attentions: Optional[bool] = False, |
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padding_mask: Optional[torch.Tensor] = None, |
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cache_position: Optional[torch.LongTensor] = None, |
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, |
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): |
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|
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query, key, value, present = self._attn_projections_and_rope( |
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hidden_states=hidden_states, |
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position_ids=position_ids, |
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layer_past=layer_past, |
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use_cache=use_cache, |
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position_embeddings=position_embeddings, |
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) |
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attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) |
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attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) |
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attn_output = self.dense(attn_output) |
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outputs = (attn_output, present) |
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if output_attentions: |
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outputs += (attn_weights,) |
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return outputs |
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|
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@classmethod |
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def _split_heads(cls, tensor, num_attention_heads, attn_head_size): |
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""" |
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Splits hidden dim into attn_head_size and num_attention_heads |
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""" |
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|
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new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) |
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tensor = tensor.view(new_shape) |
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tensor = tensor.permute(0, 2, 1, 3) |
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return tensor |
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|
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@classmethod |
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def _merge_heads(cls, tensor, num_attention_heads, attn_head_size): |
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""" |
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Merges attn_head_size dim and num_attn_heads dim into hidden dim |
|
""" |
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|
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tensor = tensor.permute(0, 2, 1, 3).contiguous() |
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tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size) |
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return tensor |
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|
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def _attn_projections_and_rope( |
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self, |
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hidden_states: torch.FloatTensor, |
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position_ids: torch.LongTensor, |
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layer_past: Optional[Tuple[torch.Tensor]] = None, |
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use_cache: Optional[bool] = False, |
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cache_position: Optional[torch.LongTensor] = None, |
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, |
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): |
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qkv = self.query_key_value(hidden_states) |
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new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size) |
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qkv = qkv.view(*new_qkv_shape) |
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query = qkv[..., : self.head_size].permute(0, 2, 1, 3) |
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key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3) |
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value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3) |
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query_rot = query[..., : self.rotary_ndims] |
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query_pass = query[..., self.rotary_ndims :] |
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key_rot = key[..., : self.rotary_ndims] |
|
key_pass = key[..., self.rotary_ndims :] |
|
|
|
if position_embeddings is None: |
|
logger.warning_once( |
|
"The attention layers in this model are transitioning from computing the RoPE embeddings internally " |
|
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed " |
|
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be " |
|
"removed and `position_embeddings` will be mandatory." |
|
) |
|
cos, sin = self.rotary_emb(value, position_ids) |
|
else: |
|
cos, sin = position_embeddings |
|
query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin) |
|
query = torch.cat((query, query_pass), dim=-1) |
|
key = torch.cat((key, key_pass), dim=-1) |
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|
|
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if layer_past is not None: |
|
cache_kwargs = { |
|
"sin": sin, |
|
"cos": cos, |
|
"partial_rotation_size": self.rotary_ndims, |
|
"cache_position": cache_position, |
|
} |
|
key, value = layer_past.update(key, value, self.layer_idx, cache_kwargs) |
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|
|
return query, key, value, layer_past |
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|
|
def _attn(self, query, key, value, attention_mask=None, head_mask=None): |
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|
|
|
|
batch_size, num_attention_heads, query_length, attn_head_size = query.size() |
|
key_length = key.size(-2) |
|
|
|
|
|
if key_length > self.bias.shape[-1]: |
|
self._init_bias(key_length, device=key.device) |
|
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] |
|
|
|
query = query.view(batch_size * num_attention_heads, query_length, attn_head_size) |
|
key = key.view(batch_size * num_attention_heads, key_length, attn_head_size) |
|
attn_scores = torch.zeros( |
|
batch_size * num_attention_heads, |
|
query_length, |
|
key_length, |
|
dtype=query.dtype, |
|
device=key.device, |
|
) |
|
attn_scores = torch.baddbmm( |
|
attn_scores, |
|
query, |
|
key.transpose(1, 2), |
|
beta=1.0, |
|
alpha=self.norm_factor, |
|
) |
|
attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) |
|
|
|
mask_value = torch.finfo(attn_scores.dtype).min |
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|
|
|
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mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) |
|
attn_scores = torch.where(causal_mask, attn_scores, mask_value) |
|
|
|
if attention_mask is not None: |
|
causal_mask = attention_mask[:, :, :, : key.shape[-2]] |
|
attn_scores = attn_scores + causal_mask |
|
|
|
attn_weights = nn.functional.softmax(attn_scores, dim=-1) |
|
attn_weights = attn_weights.to(value.dtype) |
|
|
|
|
|
if head_mask is not None: |
|
attn_weights = attn_weights * head_mask |
|
|
|
attn_weights = self.attention_dropout(attn_weights) |
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|
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attn_output = torch.matmul(attn_weights, value) |
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return attn_output, attn_weights |
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|
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class GPTNeoXFlashAttention2(GPTNeoXAttention): |
|
""" |
|
GPTNeoX flash attention module. This module inherits from `GPTNeoXAttention` as the weights of the module stays |
|
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of |
|
flash attention and deal with padding tokens in case the input contains any of them. |
|
""" |
|
|
|
|
|
def __init__(self, *args, **kwargs): |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
|
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|
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.FloatTensor, |
|
attention_mask: torch.FloatTensor, |
|
position_ids: torch.LongTensor, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
layer_past: Optional[Cache] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, |
|
): |
|
|
|
query, key, value, present = self._attn_projections_and_rope( |
|
hidden_states=hidden_states, |
|
position_ids=position_ids, |
|
layer_past=layer_past, |
|
use_cache=use_cache, |
|
cache_position=cache_position, |
|
position_embeddings=position_embeddings, |
|
) |
|
|
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query_length = query.shape[-2] |
|
|
|
|
|
target_dtype = value.dtype |
|
if query.dtype != target_dtype: |
|
query = query.to(target_dtype) |
|
if key.dtype != target_dtype: |
|
key = key.to(target_dtype) |
|
|
|
|
|
query = query.transpose(1, 2) |
|
key = key.transpose(1, 2) |
|
value = value.transpose(1, 2) |
|
|
|
attention_dropout = self.config.attention_dropout if self.training else 0.0 |
|
|
|
|
|
attn_weights = _flash_attention_forward( |
|
query, |
|
key, |
|
value, |
|
attention_mask=attention_mask, |
|
dropout=attention_dropout, |
|
is_causal=True, |
|
query_length=query_length, |
|
) |
|
|
|
|
|
attn_output = attn_weights.transpose(1, 2).contiguous() |
|
attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) |
|
attn_output = self.dense(attn_output) |
|
|
|
outputs = (attn_output, layer_past) |
|
if output_attentions: |
|
outputs += (attn_weights,) |
|
|
|
return outputs |
|
|
|
|
|
class GPTNeoXSdpaAttention(GPTNeoXAttention): |
|
""" |
|
GPTNeoX attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from |
|
`GPTNeoXAttention` as the weights of the module stays untouched. The only changes are on the forward pass |
|
to adapt to the SDPA API. |
|
""" |
|
|
|
def __init__(self, config, layer_idx=None): |
|
super().__init__(config, layer_idx=layer_idx) |
|
|
|
|
|
|
|
|
|
self.require_contiguous_qkv = version.parse(get_torch_version()) < version.parse("2.2.0") |
|
|
|
def forward( |
|
self, |
|
hidden_states: torch.FloatTensor, |
|
attention_mask: torch.FloatTensor, |
|
position_ids: torch.LongTensor, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
layer_past: Optional[Tuple[torch.Tensor]] = None, |
|
use_cache: Optional[bool] = False, |
|
output_attentions: Optional[bool] = False, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, |
|
): |
|
if output_attentions or head_mask is not None: |
|
logger.warning_once( |
|
"`GPTNeoXSdpaAttention` is used but `torch.nn.functional.scaled_dot_product_attention` does not support " |
|
"`output_attentions=True` or `head_mask`. Falling back to the manual attention implementation, but " |
|
"specifying the manual implementation will be required from Transformers version v5.0.0 onwards. " |
|
'This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' |
|
) |
|
return super().forward( |
|
hidden_states=hidden_states, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
layer_past=layer_past, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
cache_position=cache_position, |
|
) |
|
|
|
bsz, q_len, _ = hidden_states.size() |
|
|
|
|
|
query, key, value, present = self._attn_projections_and_rope( |
|
hidden_states=hidden_states, |
|
position_ids=position_ids, |
|
layer_past=layer_past, |
|
use_cache=use_cache, |
|
cache_position=cache_position, |
|
position_embeddings=position_embeddings, |
|
) |
|
|
|
causal_mask = attention_mask |
|
if attention_mask is not None: |
|
causal_mask = causal_mask[:, :, :, : key.shape[-2]] |
|
|
|
|
|
target_dtype = value.dtype |
|
if query.dtype != target_dtype: |
|
query = query.to(target_dtype) |
|
if key.dtype != target_dtype: |
|
key = key.to(target_dtype) |
|
|
|
|
|
if self.require_contiguous_qkv and query.device.type == "cuda" and attention_mask is not None: |
|
query = query.contiguous() |
|
key = key.contiguous() |
|
value = value.contiguous() |
|
|
|
|
|
|
|
is_causal = True if causal_mask is None and q_len > 1 else False |
|
|
|
attn_output = torch.nn.functional.scaled_dot_product_attention( |
|
query=query, |
|
key=key, |
|
value=value, |
|
attn_mask=causal_mask, |
|
dropout_p=self.attention_dropout.p if self.training else 0.0, |
|
is_causal=is_causal, |
|
) |
|
|
|
|
|
attn_output = attn_output.transpose(1, 2).contiguous() |
|
attn_output = attn_output.view(bsz, q_len, self.hidden_size) |
|
|
|
attn_output = self.dense(attn_output) |
|
|
|
return attn_output, present, None |
|
|
|
|
|
def attention_mask_func(attention_scores, ltor_mask): |
|
attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min) |
|
return attention_scores |
|
|
|
|
|
|
|
class GPTNeoXRotaryEmbedding(nn.Module): |
|
def __init__( |
|
self, |
|
dim=None, |
|
max_position_embeddings=2048, |
|
base=10000, |
|
device=None, |
|
scaling_factor=1.0, |
|
rope_type="default", |
|
config: Optional[GPTNeoXConfig] = None, |
|
): |
|
super().__init__() |
|
|
|
self.rope_kwargs = {} |
|
if config is None: |
|
logger.warning_once( |
|
"`GPTNeoXRotaryEmbedding` can now be fully parameterized by passing the model config through the " |
|
"`config` argument. All other arguments will be removed in v4.46" |
|
) |
|
self.rope_kwargs = { |
|
"rope_type": rope_type, |
|
"factor": scaling_factor, |
|
"dim": dim, |
|
"base": base, |
|
"max_position_embeddings": max_position_embeddings, |
|
} |
|
self.rope_type = rope_type |
|
self.max_seq_len_cached = max_position_embeddings |
|
self.original_max_seq_len = max_position_embeddings |
|
else: |
|
|
|
if config.rope_scaling is not None: |
|
self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) |
|
else: |
|
self.rope_type = "default" |
|
self.max_seq_len_cached = config.max_position_embeddings |
|
self.original_max_seq_len = config.max_position_embeddings |
|
|
|
self.config = config |
|
self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] |
|
|
|
inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs) |
|
self.register_buffer("inv_freq", inv_freq, persistent=False) |
|
self.original_inv_freq = self.inv_freq |
|
|
|
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 small sequences) |
|
""" |
|
seq_len = torch.max(position_ids) + 1 |
|
if seq_len > self.max_seq_len_cached: |
|
inv_freq, self.attention_scaling = self.rope_init_fn( |
|
self.config, device, seq_len=seq_len, **self.rope_kwargs |
|
) |
|
self.register_buffer("inv_freq", inv_freq, persistent=False) |
|
self.max_seq_len_cached = seq_len |
|
|
|
if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len: |
|
self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) |
|
self.max_seq_len_cached = self.original_max_seq_len |
|
|
|
@torch.no_grad() |
|
def forward(self, x, position_ids): |
|
if "dynamic" in self.rope_type: |
|
self._dynamic_frequency_update(position_ids, device=x.device) |
|
|
|
|
|
inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) |
|
position_ids_expanded = position_ids[:, None, :].float() |
|
|
|
device_type = x.device.type |
|
device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" |
|
with torch.autocast(device_type=device_type, enabled=False): |
|
freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) |
|
emb = torch.cat((freqs, freqs), dim=-1) |
|
cos = emb.cos() |
|
sin = emb.sin() |
|
|
|
|
|
cos = cos * self.attention_scaling |
|
sin = sin * self.attention_scaling |
|
|
|
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) |
|
|
|
|
|
|
|
class GPTNeoXLinearScalingRotaryEmbedding(GPTNeoXRotaryEmbedding): |
|
"""GPTNeoXRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" |
|
|
|
def __init__(self, *args, **kwargs): |
|
logger.warning_once( |
|
"`GPTNeoXLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " |
|
"`GPTNeoXRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)." |
|
) |
|
kwargs["rope_type"] = "linear" |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
|
|
class GPTNeoXDynamicNTKScalingRotaryEmbedding(GPTNeoXRotaryEmbedding): |
|
"""GPTNeoXRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" |
|
|
|
def __init__(self, *args, **kwargs): |
|
logger.warning_once( |
|
"`GPTNeoXDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use " |
|
"`GPTNeoXRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to " |
|
"__init__)." |
|
) |
|
kwargs["rope_type"] = "dynamic" |
|
super().__init__(*args, **kwargs) |
|
|
|
|
|
def rotate_half(x): |
|
"""Rotates half the hidden dims of the input.""" |
|
x1 = x[..., : x.shape[-1] // 2] |
|
x2 = x[..., x.shape[-1] // 2 :] |
|
return torch.cat((-x2, x1), dim=-1) |
|
|
|
|
|
|
|
def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): |
|
"""Applies Rotary Position Embedding to the query and key tensors. |
|
|
|
Args: |
|
q (`torch.Tensor`): The query tensor. |
|
k (`torch.Tensor`): The key tensor. |
|
cos (`torch.Tensor`): The cosine part of the rotary embedding. |
|
sin (`torch.Tensor`): The sine part of the rotary embedding. |
|
position_ids (`torch.Tensor`, *optional*): |
|
Deprecated and unused. |
|
unsqueeze_dim (`int`, *optional*, defaults to 1): |
|
The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and |
|
sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note |
|
that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and |
|
k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes |
|
cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have |
|
the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. |
|
Returns: |
|
`tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. |
|
""" |
|
cos = cos.unsqueeze(unsqueeze_dim) |
|
sin = sin.unsqueeze(unsqueeze_dim) |
|
q_embed = (q * cos) + (rotate_half(q) * sin) |
|
k_embed = (k * cos) + (rotate_half(k) * sin) |
|
return q_embed, k_embed |
|
|
|
|
|
class GPTNeoXMLP(nn.Module): |
|
def __init__(self, config): |
|
super().__init__() |
|
self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) |
|
self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size) |
|
self.act = ACT2FN[config.hidden_act] |
|
|
|
def forward(self, hidden_states): |
|
hidden_states = self.dense_h_to_4h(hidden_states) |
|
hidden_states = self.act(hidden_states) |
|
hidden_states = self.dense_4h_to_h(hidden_states) |
|
return hidden_states |
|
|
|
|
|
GPT_NEOX_ATTENTION_CLASSES = { |
|
"eager": GPTNeoXAttention, |
|
"flash_attention_2": GPTNeoXFlashAttention2, |
|
"sdpa": GPTNeoXSdpaAttention, |
|
} |
|
|
|
|
|
class GPTNeoXLayer(nn.Module): |
|
def __init__(self, config, layer_idx): |
|
super().__init__() |
|
self.use_parallel_residual = config.use_parallel_residual |
|
self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
self.post_attention_dropout = nn.Dropout(config.hidden_dropout) |
|
self.post_mlp_dropout = nn.Dropout(config.hidden_dropout) |
|
self.attention = GPT_NEOX_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx) |
|
self.mlp = GPTNeoXMLP(config) |
|
|
|
def forward( |
|
self, |
|
hidden_states: Optional[torch.FloatTensor], |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
use_cache: Optional[bool] = False, |
|
layer_past: Optional[Cache] = None, |
|
output_attentions: Optional[bool] = False, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, |
|
): |
|
attention_layer_outputs = self.attention( |
|
self.input_layernorm(hidden_states), |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
layer_past=layer_past, |
|
head_mask=head_mask, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
cache_position=cache_position, |
|
position_embeddings=position_embeddings, |
|
) |
|
attn_output = attention_layer_outputs[0] |
|
attn_output = self.post_attention_dropout(attn_output) |
|
outputs = attention_layer_outputs[1:] |
|
|
|
if self.use_parallel_residual: |
|
|
|
|
|
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) |
|
mlp_output = self.post_mlp_dropout(mlp_output) |
|
hidden_states = mlp_output + attn_output + hidden_states |
|
else: |
|
|
|
|
|
|
|
attn_output = attn_output + hidden_states |
|
mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) |
|
mlp_output = self.post_mlp_dropout(mlp_output) |
|
hidden_states = mlp_output + attn_output |
|
|
|
if use_cache: |
|
outputs = (hidden_states,) + outputs |
|
else: |
|
outputs = (hidden_states,) + outputs[1:] |
|
|
|
return outputs |
|
|
|
|
|
GPT_NEOX_START_DOCSTRING = r""" |
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use |
|
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and |
|
behavior. |
|
|
|
Parameters: |
|
config ([`~GPTNeoXConfig`]): Model configuration class with all the parameters of the model. |
|
Initializing with a config file does not load the weights associated with the model, only the |
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. |
|
""" |
|
|
|
GPT_NEOX_INPUTS_DOCSTRING = r""" |
|
Args: |
|
input_ids (`torch.LongTensor` of shape `({0})`): |
|
Indices of input sequence tokens in the vocabulary. |
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and |
|
[`PreTrainedTokenizer.__call__`] for details. |
|
|
|
[What are input IDs?](../glossary#input-ids) |
|
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): |
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: |
|
|
|
- 1 for tokens that are **not masked**, |
|
- 0 for tokens that are **masked**. |
|
|
|
[What are attention masks?](../glossary#attention-mask) |
|
position_ids (`torch.LongTensor` of shape `({0})`, *optional*): |
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, |
|
config.n_positions - 1]`. |
|
|
|
[What are position IDs?](../glossary#position-ids) |
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): |
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: |
|
|
|
- 1 indicates the head is **not masked**, |
|
- 0 indicates the head is **masked**. |
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*): |
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This |
|
is useful if you want more control over how to convert *input_ids* indices into associated vectors than the |
|
model's internal embedding lookup matrix. |
|
past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): |
|
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention |
|
blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` |
|
returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. |
|
|
|
Two formats are allowed: |
|
- a [`~cache_utils.Cache`] instance, see our |
|
[kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); |
|
- Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of |
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy |
|
cache format. |
|
|
|
The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the |
|
legacy cache format will be returned. |
|
|
|
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't |
|
have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` |
|
of shape `(batch_size, sequence_length)`. |
|
output_attentions (`bool`, *optional*): |
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned |
|
tensors for more detail. |
|
output_hidden_states (`bool`, *optional*): |
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for |
|
more detail. |
|
return_dict (`bool`, *optional*): |
|
Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. |
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): |
|
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, |
|
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer |
|
the complete sequence length. |
|
""" |
|
|
|
|
|
@add_start_docstrings( |
|
"The bare GPTNeoX Model transformer outputting raw hidden-states without any specific head on top.", |
|
GPT_NEOX_START_DOCSTRING, |
|
) |
|
class GPTNeoXModel(GPTNeoXPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.config = config |
|
|
|
self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) |
|
self.emb_dropout = nn.Dropout(config.hidden_dropout) |
|
self.layers = nn.ModuleList([GPTNeoXLayer(config, i) for i in range(config.num_hidden_layers)]) |
|
self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) |
|
self.rotary_emb = GPTNeoXRotaryEmbedding(config=config) |
|
|
|
self._attn_implementation = config._attn_implementation |
|
|
|
self.gradient_checkpointing = False |
|
|
|
|
|
self.post_init() |
|
|
|
def get_input_embeddings(self): |
|
return self.embed_in |
|
|
|
def set_input_embeddings(self, value): |
|
self.embed_in = value |
|
|
|
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, |
|
output_type=BaseModelOutputWithPast, |
|
config_class=_CONFIG_FOR_DOC, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Union[Tuple, BaseModelOutputWithPast]: |
|
r""" |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
|
""" |
|
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 else self.config.use_return_dict |
|
use_cache = use_cache if use_cache is not None else self.config.use_cache |
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None): |
|
raise ValueError( |
|
"You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" |
|
) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
logger.warning_once( |
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." |
|
) |
|
use_cache = False |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.embed_in(input_ids) |
|
|
|
|
|
return_legacy_cache = False |
|
if use_cache and not isinstance(past_key_values, Cache): |
|
return_legacy_cache = True |
|
if past_key_values is None: |
|
past_key_values = DynamicCache() |
|
else: |
|
past_key_values = DynamicCache.from_legacy_cache(past_key_values) |
|
logger.warning_once( |
|
"We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " |
|
"will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " |
|
"(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" |
|
) |
|
|
|
seq_length = inputs_embeds.shape[1] |
|
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 + seq_length, device=inputs_embeds.device) |
|
|
|
if position_ids is None: |
|
position_ids = cache_position.unsqueeze(0) |
|
|
|
causal_mask = self._update_causal_mask( |
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions |
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) |
|
hidden_states = self.emb_dropout(inputs_embeds) |
|
|
|
|
|
position_embeddings = self.rotary_emb(hidden_states, position_ids) |
|
|
|
next_decoder_cache = None |
|
all_attentions = () if output_attentions else None |
|
all_hidden_states = () if output_hidden_states else None |
|
for i, layer in enumerate( |
|
self.layers, |
|
): |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if self.gradient_checkpointing and self.training: |
|
outputs = self._gradient_checkpointing_func( |
|
layer.__call__, |
|
hidden_states, |
|
causal_mask, |
|
position_ids, |
|
head_mask[i], |
|
use_cache, |
|
None, |
|
output_attentions, |
|
cache_position, |
|
position_embeddings, |
|
) |
|
else: |
|
outputs = layer( |
|
hidden_states, |
|
attention_mask=causal_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask[i], |
|
layer_past=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
cache_position=cache_position, |
|
position_embeddings=position_embeddings, |
|
) |
|
hidden_states = outputs[0] |
|
if use_cache is True: |
|
next_decoder_cache = outputs[1] |
|
if output_attentions: |
|
all_attentions = all_attentions + (outputs[2 if use_cache else 1],) |
|
|
|
hidden_states = self.final_layer_norm(hidden_states) |
|
|
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
next_cache = next_decoder_cache if use_cache else None |
|
if return_legacy_cache: |
|
next_cache = next_cache.to_legacy_cache() |
|
|
|
if not return_dict: |
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_attentions] if v is not None) |
|
|
|
return BaseModelOutputWithPast( |
|
last_hidden_state=hidden_states, |
|
past_key_values=next_cache, |
|
hidden_states=all_hidden_states, |
|
attentions=all_attentions, |
|
) |
|
|
|
|
|
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 is not None and 0.0 in attention_mask: |
|
return attention_mask |
|
return None |
|
|
|
|
|
|
|
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 |
|
using_static_cache = isinstance(past_key_values, StaticCache) |
|
|
|
|
|
if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: |
|
if AttentionMaskConverter._ignore_causal_mask_sdpa( |
|
attention_mask, |
|
inputs_embeds=input_tensor, |
|
past_key_values_length=past_seen_tokens, |
|
is_training=self.training, |
|
): |
|
return None |
|
|
|
dtype, device = input_tensor.dtype, input_tensor.device |
|
min_dtype = torch.finfo(dtype).min |
|
sequence_length = input_tensor.shape[1] |
|
if using_static_cache: |
|
target_length = past_key_values.get_max_length() |
|
else: |
|
target_length = ( |
|
attention_mask.shape[-1] |
|
if isinstance(attention_mask, torch.Tensor) |
|
else past_seen_tokens + sequence_length + 1 |
|
) |
|
|
|
|
|
causal_mask = _prepare_4d_causal_attention_mask_with_cache_position( |
|
attention_mask, |
|
sequence_length=sequence_length, |
|
target_length=target_length, |
|
dtype=dtype, |
|
device=device, |
|
min_dtype=min_dtype, |
|
cache_position=cache_position, |
|
batch_size=input_tensor.shape[0], |
|
) |
|
|
|
if ( |
|
self.config._attn_implementation == "sdpa" |
|
and attention_mask is not None |
|
and attention_mask.device.type == "cuda" |
|
and not output_attentions |
|
): |
|
|
|
|
|
|
|
causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) |
|
|
|
return causal_mask |
|
|
|
|
|
@add_start_docstrings( |
|
"""GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING |
|
) |
|
class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel, GenerationMixin): |
|
_tied_weights_keys = ["embed_out.weight"] |
|
|
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
self.gpt_neox = GPTNeoXModel(config) |
|
self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
def get_output_embeddings(self): |
|
return self.embed_out |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.embed_out = new_embeddings |
|
|
|
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) |
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
cache_position: Optional[torch.LongTensor] = None, |
|
) -> Union[Tuple, CausalLMOutputWithPast]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in |
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are |
|
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. |
|
use_cache (`bool`, *optional*): |
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see |
|
`past_key_values`). |
|
|
|
Returns: |
|
|
|
Example: |
|
|
|
```python |
|
>>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig |
|
>>> import torch |
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") |
|
>>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b") |
|
>>> config.is_decoder = True |
|
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config) |
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") |
|
>>> outputs = model(**inputs) |
|
|
|
>>> prediction_logits = outputs.logits |
|
```""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
outputs = self.gpt_neox( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
past_key_values=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
cache_position=cache_position, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
lm_logits = self.embed_out(hidden_states) |
|
|
|
lm_loss = None |
|
if labels is not None: |
|
|
|
labels = labels.to(lm_logits.device) |
|
|
|
shift_logits = lm_logits[:, :-1, :].contiguous() |
|
labels = labels[:, 1:].contiguous() |
|
loss_fct = CrossEntropyLoss() |
|
lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) |
|
|
|
if not return_dict: |
|
output = (lm_logits,) + outputs[1:] |
|
return ((lm_loss,) + output) if lm_loss is not None else output |
|
|
|
return CausalLMOutputWithPast( |
|
loss=lm_loss, |
|
logits=lm_logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
|
|
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, |
|
**kwargs, |
|
): |
|
|
|
|
|
|
|
if past_key_values is not None: |
|
if inputs_embeds is not None: |
|
input_ids = input_ids[:, -cache_position.shape[0] :] |
|
elif input_ids.shape[1] != cache_position.shape[0]: |
|
input_ids = input_ids[:, cache_position] |
|
|
|
if attention_mask is not None and position_ids is None: |
|
|
|
position_ids = attention_mask.long().cumsum(-1) - 1 |
|
position_ids.masked_fill_(attention_mask == 0, 1) |
|
if past_key_values: |
|
position_ids = position_ids[:, -input_ids.shape[1] :] |
|
|
|
|
|
position_ids = position_ids.clone(memory_format=torch.contiguous_format) |
|
|
|
|
|
if inputs_embeds is not None and cache_position[0] == 0: |
|
model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} |
|
else: |
|
|
|
model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None} |
|
|
|
if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: |
|
if model_inputs["inputs_embeds"] is not None: |
|
batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape |
|
device = model_inputs["inputs_embeds"].device |
|
else: |
|
batch_size, sequence_length = model_inputs["input_ids"].shape |
|
device = model_inputs["input_ids"].device |
|
|
|
dtype = self.embed_out.weight.dtype |
|
min_dtype = torch.finfo(dtype).min |
|
|
|
attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( |
|
attention_mask, |
|
sequence_length=sequence_length, |
|
target_length=past_key_values.get_max_length(), |
|
dtype=dtype, |
|
device=device, |
|
min_dtype=min_dtype, |
|
cache_position=cache_position, |
|
batch_size=batch_size, |
|
) |
|
|
|
model_inputs.update( |
|
{ |
|
"position_ids": position_ids, |
|
"cache_position": cache_position, |
|
"past_key_values": past_key_values, |
|
"use_cache": use_cache, |
|
"attention_mask": attention_mask, |
|
} |
|
) |
|
return model_inputs |
|
|
|
def _reorder_cache(self, 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[:2]) |
|
+ layer_past[2:], |
|
) |
|
return reordered_past |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The GPTNeoX Model transformer with a sequence classification head on top (linear layer). |
|
|
|
[`GPTNeoXForSequenceClassification`] uses the last token in order to do the classification, as other causal models |
|
(e.g. GPT-1) do. |
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a |
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If |
|
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the |
|
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in |
|
each row of the batch). |
|
""", |
|
GPT_NEOX_START_DOCSTRING, |
|
) |
|
class GPTNeoXForSequenceClassification(GPTNeoXPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.gpt_neox = GPTNeoXModel(config) |
|
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
output_type=SequenceClassifierOutputWithPast, |
|
config_class=_CONFIG_FOR_DOC, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.FloatTensor]]]] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
|
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). |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
outputs = self.gpt_neox( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
past_key_values=past_key_values, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
hidden_states = outputs[0] |
|
logits = self.score(hidden_states) |
|
|
|
if input_ids is not None: |
|
batch_size, sequence_length = input_ids.shape[:2] |
|
else: |
|
batch_size, sequence_length = inputs_embeds.shape[:2] |
|
|
|
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: |
|
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 |
|
sequence_lengths = sequence_lengths % input_ids.shape[-1] |
|
sequence_lengths = sequence_lengths.to(logits.device) |
|
else: |
|
sequence_lengths = -1 |
|
logger.warning_once( |
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " |
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`" |
|
) |
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] |
|
|
|
loss = None |
|
if labels is not None: |
|
labels = labels.to(logits.device) |
|
if self.config.problem_type is None: |
|
if self.num_labels == 1: |
|
self.config.problem_type = "regression" |
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): |
|
self.config.problem_type = "single_label_classification" |
|
else: |
|
self.config.problem_type = "multi_label_classification" |
|
|
|
if self.config.problem_type == "regression": |
|
loss_fct = MSELoss() |
|
if self.num_labels == 1: |
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) |
|
else: |
|
loss = loss_fct(pooled_logits, labels) |
|
elif self.config.problem_type == "single_label_classification": |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) |
|
elif self.config.problem_type == "multi_label_classification": |
|
loss_fct = BCEWithLogitsLoss() |
|
loss = loss_fct(pooled_logits, labels) |
|
if not return_dict: |
|
output = (pooled_logits,) + outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return SequenceClassifierOutputWithPast( |
|
loss=loss, |
|
logits=pooled_logits, |
|
past_key_values=outputs.past_key_values, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
|
|
class GPTNeoXForTokenClassification(GPTNeoXPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
|
|
self.gpt_neox = GPTNeoXModel(config) |
|
self.dropout = nn.Dropout(config.classifier_dropout) |
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING) |
|
@add_code_sample_docstrings( |
|
checkpoint="LarsJonasson/pythia-410m-deduped-sft-swedish", |
|
output_type=TokenClassifierOutput, |
|
config_class=_CONFIG_FOR_DOC, |
|
expected_loss=0.25, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Union[Cache, Tuple[Tuple[torch.Tensor]]]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
labels: Optional[torch.LongTensor] = None, |
|
use_cache: Optional[bool] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, TokenClassifierOutput]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
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). |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
outputs = self.gpt_neox( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
hidden_states = outputs[0] |
|
hidden_states = self.dropout(hidden_states) |
|
logits = self.classifier(hidden_states) |
|
|
|
loss = None |
|
if labels is not None: |
|
labels = labels.to(logits.device) |
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) |
|
|
|
if not return_dict: |
|
output = (logits,) + outputs[2:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return TokenClassifierOutput( |
|
loss=loss, |
|
logits=logits, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|
|
|
|
@add_start_docstrings( |
|
""" |
|
The GPT-NeoX Model transformer with a span classification head on top for extractive question-answering tasks like |
|
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`). |
|
""", |
|
GPT_NEOX_START_DOCSTRING, |
|
) |
|
class GPTNeoXForQuestionAnswering(GPTNeoXPreTrainedModel): |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.num_labels = config.num_labels |
|
self.gpt_neox = GPTNeoXModel(config) |
|
self.qa_outputs = nn.Linear(config.hidden_size, 2) |
|
|
|
|
|
self.post_init() |
|
|
|
@add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) |
|
@add_code_sample_docstrings( |
|
checkpoint=_CHECKPOINT_FOR_DOC, |
|
output_type=QuestionAnsweringModelOutput, |
|
config_class=_CONFIG_FOR_DOC, |
|
real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, |
|
) |
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
start_positions: Optional[torch.LongTensor] = None, |
|
end_positions: Optional[torch.LongTensor] = None, |
|
output_attentions: Optional[bool] = None, |
|
output_hidden_states: Optional[bool] = None, |
|
return_dict: Optional[bool] = None, |
|
) -> Union[Tuple, QuestionAnsweringModelOutput]: |
|
r""" |
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): |
|
Labels for position (index) of the start of the labelled span for computing the token classification loss. |
|
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 span for computing the token classification loss. |
|
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. |
|
""" |
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
|
|
|
outputs = self.gpt_neox( |
|
input_ids, |
|
attention_mask=attention_mask, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
|
|
sequence_output = outputs[0] |
|
|
|
logits = self.qa_outputs(sequence_output) |
|
start_logits, end_logits = logits.split(1, dim=-1) |
|
start_logits = start_logits.squeeze(-1).contiguous() |
|
end_logits = end_logits.squeeze(-1).contiguous() |
|
|
|
total_loss = None |
|
if start_positions is not None and end_positions is not None: |
|
|
|
if len(start_positions.size()) > 1: |
|
start_positions = start_positions.squeeze(-1).to(start_logits.device) |
|
if len(end_positions.size()) > 1: |
|
end_positions = end_positions.squeeze(-1).to(end_logits.device) |
|
|
|
ignored_index = start_logits.size(1) |
|
start_positions = start_positions.clamp(0, ignored_index) |
|
end_positions = end_positions.clamp(0, ignored_index) |
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index) |
|
start_loss = loss_fct(start_logits, start_positions) |
|
end_loss = loss_fct(end_logits, end_positions) |
|
total_loss = (start_loss + end_loss) / 2 |
|
|
|
if not return_dict: |
|
output = (start_logits, end_logits) + outputs[2:] |
|
return ((total_loss,) + output) if total_loss is not None else output |
|
|
|
return QuestionAnsweringModelOutput( |
|
loss=total_loss, |
|
start_logits=start_logits, |
|
end_logits=end_logits, |
|
hidden_states=outputs.hidden_states, |
|
attentions=outputs.attentions, |
|
) |
|
|