d-Matrix
commited on
Update modeling_opt.py
Browse files- modeling_opt.py +614 -313
modeling_opt.py
CHANGED
@@ -13,15 +13,16 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch OPT model."""
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import random
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from transformers.activations import ACT2FN
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from transformers.modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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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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logging,
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replace_return_docstrings,
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)
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from .configuration_opt import OPTConfig
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
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_CONFIG_FOR_DOC = "OPTConfig"
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_TOKENIZER_FOR_DOC = "GPT2Tokenizer"
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# Base model docstring
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_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
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# See all OPT models at https://huggingface.co/models?filter=opt
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]
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def _make_causal_mask(input_ids_shape: torch.Size, dtype: torch.dtype, past_key_values_length: int = 0):
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"""
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Make causal mask used for bi-directional self-attention.
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"""
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bsz, tgt_len = input_ids_shape
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mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min))
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mask_cond = torch.arange(mask.size(-1))
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mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
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mask = mask.to(dtype)
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class OPTLearnedPositionalEmbedding(nn.
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"""
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This module learns positional embeddings up to a fixed maximum size.
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"""
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def __init__(self, num_embeddings: int, embedding_dim: int):
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# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
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# and adjust num_embeddings appropriately. Other models don't have this hack
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self.offset = 2
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def forward(
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"""`input_ids_shape` is expected to be [bsz x seqlen]."""
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attention_mask = attention_mask.long()
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# create positions depending on attention_mask
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positions = (
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# cut positions if `past_key_values_length` is > 0
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positions = positions[:, past_key_values_length:]
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return
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class OPTAttention(nn.Module):
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def __init__(
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self,
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num_heads: int,
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dropout: float = 0.0,
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is_decoder: bool = False,
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):
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super().__init__()
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self.
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raise ValueError(
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f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
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f" and `num_heads`: {num_heads})."
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)
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self.scaling = self.head_dim**-0.5
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self.is_decoder = is_decoder
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self.k_proj = nn.Linear(embed_dim, embed_dim, bias=
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self.v_proj = nn.Linear(embed_dim, embed_dim, bias=
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self.q_proj = nn.Linear(embed_dim, embed_dim, bias=
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=
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self.softmax = nn.Softmax(dim=-1)
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self.dropout = nn.Dropout(p=dropout)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return
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def forward(
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self,
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raise ValueError(
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f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
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)
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attn_weights =
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
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if attn_weights.dtype == torch.float16:
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attn_weights =
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else:
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attn_weights =
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if layer_head_mask is not None:
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if layer_head_mask.size() != (self.num_heads,):
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f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
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f" {layer_head_mask.size()}"
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)
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attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
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attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
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if output_attentions:
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# make sure that attn_weights keeps its gradient.
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# In order to do so, attn_weights have to be reshaped
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# twice and have to be reused in the following
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attn_weights_reshaped = attn_weights.view(
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else:
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attn_weights_reshaped = None
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attn_probs =
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attn_output = torch.bmm(attn_probs, value_states)
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if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
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return attn_output, attn_weights_reshaped, past_key_value
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class OPTDecoderLayer(nn.Module):
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def __init__(self, config: OPTConfig):
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super().__init__()
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self.embed_dim = config.hidden_size
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dropout=config.attention_dropout,
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is_decoder=True,
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)
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self.do_layer_norm_before = config.do_layer_norm_before
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self.activation_fn = ACT2FN[config.activation_function]
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self.self_attn_layer_norm = nn.LayerNorm(
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self.
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self.
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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layer_head_mask: Optional[torch.Tensor] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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)
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hidden_states =
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hidden_states = residual + hidden_states
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# 350m applies layer norm AFTER attention
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hidden_states = self.activation_fn(hidden_states)
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hidden_states = self.fc2(hidden_states)
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hidden_states =
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hidden_states = (residual + hidden_states).view(hidden_states_shape)
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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 ([`OPTConfig`]):
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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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base_model_prefix = "model"
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supports_gradient_checkpointing = True
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_no_split_modules = ["OPTDecoderLayer"]
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def _init_weights(self, module):
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std = self.config.init_std
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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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def _set_gradient_checkpointing(self, module, value=False):
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if isinstance(module, (OPTDecoder)):
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module.gradient_checkpointing = value
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OPT_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 [`GPT2Tokenizer`]. 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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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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-
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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 [`OPTTokenizer`]. 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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head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
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Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
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-
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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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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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class OPTDecoder(OPTPreTrainedModel):
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"""
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Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
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Args:
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config: OPTConfig
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"""
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self.max_target_positions = config.max_position_embeddings
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(
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if config.word_embed_proj_dim != config.hidden_size:
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self.project_out = nn.Linear(
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else:
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self.project_out = None
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if config.word_embed_proj_dim != config.hidden_size:
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self.project_in = nn.Linear(
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else:
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self.project_in = None
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@@ -510,11 +832,17 @@ class OPTDecoder(OPTPreTrainedModel):
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# with checkpoints that have been fine-tuned before transformers v4.20.1
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# see https://github.com/facebookresearch/metaseq/pull/164
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if config.do_layer_norm_before and not config._remove_final_layer_norm:
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self.final_layer_norm = nn.LayerNorm(
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|
|
|
|
514 |
else:
|
515 |
self.final_layer_norm = None
|
516 |
|
517 |
-
self.layers = nn.ModuleList(
|
|
|
|
|
|
|
518 |
|
519 |
self.gradient_checkpointing = False
|
520 |
# Initialize weights and apply final processing
|
@@ -526,29 +854,6 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
526 |
def set_input_embeddings(self, value):
|
527 |
self.embed_tokens = value
|
528 |
|
529 |
-
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
530 |
-
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
531 |
-
# create causal mask
|
532 |
-
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
533 |
-
combined_attention_mask = None
|
534 |
-
if input_shape[-1] > 1:
|
535 |
-
combined_attention_mask = _make_causal_mask(
|
536 |
-
input_shape,
|
537 |
-
inputs_embeds.dtype,
|
538 |
-
past_key_values_length=past_key_values_length,
|
539 |
-
)
|
540 |
-
|
541 |
-
if attention_mask is not None:
|
542 |
-
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
543 |
-
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
544 |
-
inputs_embeds.device
|
545 |
-
)
|
546 |
-
combined_attention_mask = (
|
547 |
-
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask.to(expanded_attn_mask.device)
|
548 |
-
)
|
549 |
-
|
550 |
-
return combined_attention_mask
|
551 |
-
|
552 |
def forward(
|
553 |
self,
|
554 |
input_ids: torch.LongTensor = None,
|
@@ -566,35 +871,26 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
566 |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
567 |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
568 |
provide it.
|
569 |
-
|
570 |
-
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
571 |
[`PreTrainedTokenizer.__call__`] for details.
|
572 |
-
|
573 |
[What are input IDs?](../glossary#input-ids)
|
574 |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
575 |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
576 |
-
|
577 |
- 1 for tokens that are **not masked**,
|
578 |
- 0 for tokens that are **masked**.
|
579 |
-
|
580 |
[What are attention masks?](../glossary#attention-mask)
|
581 |
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
582 |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
583 |
-
|
584 |
- 1 indicates the head is **not masked**,
|
585 |
- 0 indicates the head is **masked**.
|
586 |
-
|
587 |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
588 |
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
589 |
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
590 |
-
|
591 |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
592 |
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
593 |
-
|
594 |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
595 |
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
596 |
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
597 |
-
|
598 |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
599 |
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
600 |
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
@@ -608,44 +904,89 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
608 |
return_dict (`bool`, *optional*):
|
609 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
610 |
"""
|
611 |
-
output_attentions =
|
|
|
|
|
|
|
|
|
612 |
output_hidden_states = (
|
613 |
-
output_hidden_states
|
|
|
|
|
614 |
)
|
615 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
616 |
|
617 |
-
return_dict =
|
|
|
|
|
618 |
|
619 |
# retrieve input_ids and inputs_embeds
|
620 |
if input_ids is not None and inputs_embeds is not None:
|
621 |
-
raise ValueError(
|
|
|
|
|
622 |
elif input_ids is not None:
|
623 |
input_shape = input_ids.size()
|
624 |
input_ids = input_ids.view(-1, input_shape[-1])
|
625 |
elif inputs_embeds is not None:
|
626 |
input_shape = inputs_embeds.size()[:-1]
|
627 |
else:
|
628 |
-
raise ValueError(
|
629 |
-
|
630 |
-
|
631 |
|
632 |
if inputs_embeds is None:
|
633 |
inputs_embeds = self.embed_tokens(input_ids)
|
634 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
635 |
# embed positions
|
636 |
-
if
|
637 |
-
|
638 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
639 |
|
640 |
-
|
641 |
-
attention_mask, input_shape, inputs_embeds, past_key_values_length
|
642 |
-
)
|
643 |
|
644 |
if self.project_in is not None:
|
645 |
inputs_embeds = self.project_in(inputs_embeds)
|
646 |
|
647 |
hidden_states = inputs_embeds + pos_embeds
|
648 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
649 |
# decoder layers
|
650 |
all_hidden_states = () if output_hidden_states else None
|
651 |
all_self_attns = () if output_attentions else None
|
@@ -665,39 +1006,29 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
665 |
if output_hidden_states:
|
666 |
all_hidden_states += (hidden_states,)
|
667 |
|
668 |
-
|
669 |
-
|
670 |
-
|
|
|
671 |
|
672 |
-
past_key_value =
|
|
|
|
|
673 |
|
674 |
if self.gradient_checkpointing and self.training:
|
675 |
-
|
676 |
-
|
677 |
-
logger.warning(
|
678 |
-
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
679 |
-
)
|
680 |
-
use_cache = False
|
681 |
-
|
682 |
-
def create_custom_forward(module):
|
683 |
-
def custom_forward(*inputs):
|
684 |
-
# None for past_key_value
|
685 |
-
return module(*inputs, output_attentions, None)
|
686 |
-
|
687 |
-
return custom_forward
|
688 |
-
|
689 |
-
layer_outputs = torch.utils.checkpoint.checkpoint(
|
690 |
-
create_custom_forward(decoder_layer),
|
691 |
hidden_states,
|
692 |
-
|
693 |
head_mask[idx] if head_mask is not None else None,
|
694 |
None,
|
|
|
|
|
695 |
)
|
696 |
else:
|
697 |
-
|
698 |
layer_outputs = decoder_layer(
|
699 |
hidden_states,
|
700 |
-
attention_mask=
|
701 |
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
702 |
past_key_value=past_key_value,
|
703 |
output_attentions=output_attentions,
|
@@ -712,12 +1043,6 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
712 |
if output_attentions:
|
713 |
all_self_attns += (layer_outputs[1],)
|
714 |
|
715 |
-
# Model Parallel: If it's the last layer for that device, put things on the next device
|
716 |
-
if self.model_parallel:
|
717 |
-
for k, v in self.device_map.items():
|
718 |
-
if idx == v[-1] and "cuda:" + str(k) != self.last_device:
|
719 |
-
hidden_states = hidden_states.to("cuda:" + str(k + 1))
|
720 |
-
|
721 |
if self.final_layer_norm is not None:
|
722 |
hidden_states = self.final_layer_norm(hidden_states)
|
723 |
|
@@ -730,7 +1055,11 @@ class OPTDecoder(OPTPreTrainedModel):
|
|
730 |
|
731 |
next_cache = next_decoder_cache if use_cache else None
|
732 |
if not return_dict:
|
733 |
-
return tuple(
|
|
|
|
|
|
|
|
|
734 |
return BaseModelOutputWithPast(
|
735 |
last_hidden_state=hidden_states,
|
736 |
past_key_values=next_cache,
|
@@ -747,46 +1076,9 @@ class OPTModel(OPTPreTrainedModel):
|
|
747 |
def __init__(self, config: OPTConfig):
|
748 |
super().__init__(config)
|
749 |
self.decoder = OPTDecoder(config)
|
750 |
-
|
751 |
-
# Model parallel
|
752 |
-
self.decoder.model_parallel = False
|
753 |
-
self.decoder.device_map = None
|
754 |
-
self.decoder.gradient_checkpointing = False
|
755 |
-
|
756 |
# Initialize weights and apply final processing
|
757 |
self.post_init()
|
758 |
|
759 |
-
def parallelize(self, device_map=None):
|
760 |
-
# Check validity of device_map
|
761 |
-
self.decoder.device_map = (
|
762 |
-
get_device_map(len(self.decoder.layers), range(torch.cuda.device_count())) if device_map is None else device_map
|
763 |
-
)
|
764 |
-
assert_device_map(self.decoder.device_map, len(self.decoder.layers))
|
765 |
-
self.decoder.model_parallel = True
|
766 |
-
self.decoder.first_device = "cpu" if "cpu" in self.decoder.device_map.keys() else "cuda:" + str(min(self.decoder.device_map.keys()))
|
767 |
-
self.decoder.last_device = "cuda:" + str(max(self.decoder.device_map.keys()))
|
768 |
-
self.decoder.embed_tokens = self.decoder.embed_tokens.to(self.decoder.first_device)
|
769 |
-
self.decoder.embed_positions = self.decoder.embed_positions.to(self.decoder.first_device)
|
770 |
-
# Load onto devices
|
771 |
-
for k, v in self.decoder.device_map.items():
|
772 |
-
for block in v:
|
773 |
-
cuda_device = "cuda:" + str(k)
|
774 |
-
self.decoder.layers[block] = self.decoder.layers[block].to(cuda_device)
|
775 |
-
# final_layer_norm to last
|
776 |
-
self.decoder.final_layer_norm = self.decoder.final_layer_norm.to(self.decoder.last_device)
|
777 |
-
|
778 |
-
def deparallelize(self):
|
779 |
-
self.decoder.model_parallel = False
|
780 |
-
self.decoder.device_map = None
|
781 |
-
self.decoder.first_device = "cpu"
|
782 |
-
self.decoder.last_device = "cpu"
|
783 |
-
self.decoder.embed_tokens = self.decoder.embed_tokens.to("cpu")
|
784 |
-
self.decoder.embed_positions = self.decoder.embed_positions.to("cpu")
|
785 |
-
for index in range(len(self.decoder)):
|
786 |
-
self.decoder.layers[index] = self.decoder.layers[index].to("cpu")
|
787 |
-
self.decoder.final_layer_norm = self.decoder.final_layer_norm.to("cpu")
|
788 |
-
torch.cuda.empty_cache()
|
789 |
-
|
790 |
def get_input_embeddings(self):
|
791 |
return self.decoder.embed_tokens
|
792 |
|
@@ -798,7 +1090,6 @@ class OPTModel(OPTPreTrainedModel):
|
|
798 |
|
799 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
800 |
@add_code_sample_docstrings(
|
801 |
-
processor_class=_TOKENIZER_FOR_DOC,
|
802 |
checkpoint=_CHECKPOINT_FOR_DOC,
|
803 |
output_type=BaseModelOutputWithPast,
|
804 |
config_class=_CONFIG_FOR_DOC,
|
@@ -816,13 +1107,20 @@ class OPTModel(OPTPreTrainedModel):
|
|
816 |
output_hidden_states: Optional[bool] = None,
|
817 |
return_dict: Optional[bool] = None,
|
818 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
819 |
-
|
820 |
-
|
|
|
|
|
|
|
821 |
output_hidden_states = (
|
822 |
-
output_hidden_states
|
|
|
|
|
823 |
)
|
824 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
825 |
-
return_dict =
|
|
|
|
|
826 |
|
827 |
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
|
828 |
decoder_outputs = self.decoder(
|
@@ -849,40 +1147,20 @@ class OPTModel(OPTPreTrainedModel):
|
|
849 |
|
850 |
|
851 |
class OPTForCausalLM(OPTPreTrainedModel):
|
852 |
-
|
853 |
|
854 |
def __init__(self, config):
|
855 |
super().__init__(config)
|
856 |
self.model = OPTModel(config)
|
857 |
|
858 |
# the lm_head weight is automatically tied to the embed tokens weight
|
859 |
-
self.lm_head = nn.Linear(
|
860 |
-
|
861 |
-
|
862 |
-
self.model_parallel = False
|
863 |
-
self.device_map = None
|
864 |
|
865 |
# Initialize weights and apply final processing
|
866 |
self.post_init()
|
867 |
|
868 |
-
def parallelize(self, device_map=None):
|
869 |
-
self.model.decoder.device_map = (
|
870 |
-
get_device_map(len(self.model.decoder.layers), range(torch.cuda.device_count()))
|
871 |
-
if device_map is None
|
872 |
-
else device_map
|
873 |
-
)
|
874 |
-
assert_device_map(self.model.decoder.device_map, len(self.model.decoder.layers))
|
875 |
-
self.model.parallelize(self.model.decoder.device_map)
|
876 |
-
self.lm_head = self.lm_head.to(self.model.decoder.first_device)
|
877 |
-
self.model_parallel = True
|
878 |
-
|
879 |
-
def deparallelize(self):
|
880 |
-
self.model.deparallelize()
|
881 |
-
self.model = self.model.to("cpu")
|
882 |
-
self.lm_head = self.lm_head.to("cpu")
|
883 |
-
self.model_parallel = False
|
884 |
-
torch.cuda.empty_cache()
|
885 |
-
|
886 |
def get_input_embeddings(self):
|
887 |
return self.model.decoder.embed_tokens
|
888 |
|
@@ -901,7 +1179,9 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
901 |
def get_decoder(self):
|
902 |
return self.model.decoder
|
903 |
|
904 |
-
@replace_return_docstrings(
|
|
|
|
|
905 |
def forward(
|
906 |
self,
|
907 |
input_ids: torch.LongTensor = None,
|
@@ -920,33 +1200,25 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
920 |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
921 |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
922 |
provide it.
|
923 |
-
|
924 |
-
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
925 |
[`PreTrainedTokenizer.__call__`] for details.
|
926 |
-
|
927 |
[What are input IDs?](../glossary#input-ids)
|
928 |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
929 |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
930 |
-
|
931 |
- 1 for tokens that are **not masked**,
|
932 |
- 0 for tokens that are **masked**.
|
933 |
-
|
934 |
[What are attention masks?](../glossary#attention-mask)
|
935 |
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
936 |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
937 |
-
|
938 |
- 1 indicates the head is **not masked**,
|
939 |
- 0 indicates the head is **masked**.
|
940 |
-
|
941 |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
942 |
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
943 |
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
944 |
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
|
945 |
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
|
946 |
-
|
947 |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
948 |
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
949 |
-
|
950 |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
951 |
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
952 |
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
@@ -969,31 +1241,33 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
969 |
for more detail.
|
970 |
return_dict (`bool`, *optional*):
|
971 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
972 |
-
|
973 |
Returns:
|
974 |
-
|
975 |
Example:
|
976 |
-
|
977 |
```python
|
978 |
-
>>> from transformers import
|
979 |
-
|
980 |
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
|
981 |
-
>>> tokenizer =
|
982 |
-
|
983 |
-
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
984 |
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
985 |
-
|
986 |
>>> # Generate
|
987 |
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
988 |
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
989 |
-
"Hey, are you
|
990 |
```"""
|
991 |
|
992 |
-
output_attentions =
|
|
|
|
|
|
|
|
|
993 |
output_hidden_states = (
|
994 |
-
output_hidden_states
|
|
|
|
|
|
|
|
|
|
|
995 |
)
|
996 |
-
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
997 |
|
998 |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
999 |
outputs = self.model.decoder(
|
@@ -1008,11 +1282,7 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
1008 |
return_dict=return_dict,
|
1009 |
)
|
1010 |
|
1011 |
-
|
1012 |
-
if self.model.decoder.model_parallel:
|
1013 |
-
torch.cuda.set_device(self.model.decoder.first_device)
|
1014 |
-
|
1015 |
-
logits = self.lm_head(outputs[0].to(self.lm_head.weight.device)).contiguous()
|
1016 |
|
1017 |
loss = None
|
1018 |
if labels is not None:
|
@@ -1023,7 +1293,9 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
1023 |
shift_labels = labels[..., 1:].contiguous()
|
1024 |
# Flatten the tokens
|
1025 |
loss_fct = CrossEntropyLoss()
|
1026 |
-
loss = loss_fct(
|
|
|
|
|
1027 |
|
1028 |
if not return_dict:
|
1029 |
output = (logits,) + outputs[1:]
|
@@ -1037,36 +1309,59 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
1037 |
attentions=outputs.attentions,
|
1038 |
)
|
1039 |
|
1040 |
-
def prepare_inputs_for_generation(
|
1041 |
-
|
1042 |
-
|
1043 |
-
|
1044 |
-
|
1045 |
-
|
1046 |
-
|
1047 |
-
|
1048 |
-
|
1049 |
-
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1050 |
-
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1051 |
-
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1052 |
-
|
1053 |
-
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|
1054 |
|
1055 |
@staticmethod
|
1056 |
-
def _reorder_cache(
|
1057 |
reordered_past = ()
|
1058 |
-
for layer_past in
|
1059 |
-
reordered_past += (
|
|
|
|
|
|
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|
|
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|
1060 |
return reordered_past
|
1061 |
|
1062 |
|
1063 |
@add_start_docstrings(
|
1064 |
"""
|
1065 |
The OPT Model transformer with a sequence classification head on top (linear layer).
|
1066 |
-
|
1067 |
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1068 |
(e.g. GPT-2) do.
|
1069 |
-
|
1070 |
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1071 |
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1072 |
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
@@ -1076,8 +1371,6 @@ class OPTForCausalLM(OPTPreTrainedModel):
|
|
1076 |
OPT_START_DOCSTRING,
|
1077 |
)
|
1078 |
class OPTForSequenceClassification(OPTPreTrainedModel):
|
1079 |
-
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
1080 |
-
|
1081 |
def __init__(self, config: OPTConfig):
|
1082 |
super().__init__(config)
|
1083 |
self.num_labels = config.num_labels
|
@@ -1089,7 +1382,6 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1089 |
|
1090 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
1091 |
@add_code_sample_docstrings(
|
1092 |
-
processor_class=_TOKENIZER_FOR_DOC,
|
1093 |
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
|
1094 |
output_type=SequenceClassifierOutputWithPast,
|
1095 |
config_class=_CONFIG_FOR_DOC,
|
@@ -1115,7 +1407,9 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1115 |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1116 |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1117 |
"""
|
1118 |
-
return_dict =
|
|
|
|
|
1119 |
|
1120 |
transformer_outputs = self.model(
|
1121 |
input_ids,
|
@@ -1140,7 +1434,12 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1140 |
sequence_lengths = -1
|
1141 |
else:
|
1142 |
if input_ids is not None:
|
1143 |
-
|
|
|
|
|
|
|
|
|
|
|
1144 |
else:
|
1145 |
sequence_lengths = -1
|
1146 |
logger.warning(
|
@@ -1148,14 +1447,18 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1148 |
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
1149 |
)
|
1150 |
|
1151 |
-
pooled_logits = logits[
|
|
|
|
|
1152 |
|
1153 |
loss = None
|
1154 |
if labels is not None:
|
1155 |
if self.config.problem_type is None:
|
1156 |
if self.num_labels == 1:
|
1157 |
self.config.problem_type = "regression"
|
1158 |
-
elif self.num_labels > 1 and (
|
|
|
|
|
1159 |
self.config.problem_type = "single_label_classification"
|
1160 |
else:
|
1161 |
self.config.problem_type = "multi_label_classification"
|
@@ -1168,7 +1471,9 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1168 |
loss = loss_fct(pooled_logits, labels)
|
1169 |
elif self.config.problem_type == "single_label_classification":
|
1170 |
loss_fct = CrossEntropyLoss()
|
1171 |
-
loss = loss_fct(
|
|
|
|
|
1172 |
elif self.config.problem_type == "multi_label_classification":
|
1173 |
loss_fct = BCEWithLogitsLoss()
|
1174 |
loss = loss_fct(pooled_logits, labels)
|
@@ -1199,8 +1504,6 @@ class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
1199 |
OPT_START_DOCSTRING,
|
1200 |
)
|
1201 |
class OPTForQuestionAnswering(OPTPreTrainedModel):
|
1202 |
-
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
1203 |
-
|
1204 |
def __init__(self, config: OPTConfig):
|
1205 |
super().__init__(config)
|
1206 |
self.model = OPTModel(config)
|
@@ -1210,7 +1513,9 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
|
|
1210 |
self.post_init()
|
1211 |
|
1212 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
1213 |
-
@replace_return_docstrings(
|
|
|
|
|
1214 |
def forward(
|
1215 |
self,
|
1216 |
input_ids: Optional[torch.LongTensor] = None,
|
@@ -1234,37 +1539,33 @@ class OPTForQuestionAnswering(OPTPreTrainedModel):
|
|
1234 |
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1235 |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1236 |
are not taken into account for computing the loss.
|
1237 |
-
|
1238 |
Returns:
|
1239 |
-
|
1240 |
Example:
|
1241 |
-
|
1242 |
```python
|
1243 |
-
>>> from transformers import
|
1244 |
>>> import torch
|
1245 |
-
|
1246 |
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
|
1247 |
-
>>> tokenizer =
|
1248 |
-
|
1249 |
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
|
1250 |
>>> # so the head will be randomly initialized, hence the predictions will be random
|
1251 |
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
|
1252 |
-
|
1253 |
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
1254 |
-
|
1255 |
>>> inputs = tokenizer(question, text, return_tensors="pt")
|
1256 |
>>> with torch.no_grad():
|
1257 |
... outputs = model(**inputs)
|
1258 |
-
|
1259 |
>>> answer_start_index = outputs.start_logits.argmax()
|
1260 |
>>> answer_end_index = outputs.end_logits.argmax()
|
1261 |
-
|
1262 |
-
>>> predict_answer_tokens = inputs.input_ids[
|
|
|
|
|
1263 |
>>> predicted = tokenizer.decode(predict_answer_tokens)
|
1264 |
>>> predicted
|
1265 |
-
'
|
1266 |
```"""
|
1267 |
-
return_dict =
|
|
|
|
|
1268 |
|
1269 |
transformer_outputs = self.model(
|
1270 |
input_ids,
|
|
|
13 |
# See the License for the specific language governing permissions and
|
14 |
# limitations under the License.
|
15 |
""" PyTorch OPT model."""
|
|
|
16 |
from typing import List, Optional, Tuple, Union
|
17 |
|
18 |
import torch
|
19 |
+
import torch.nn.functional as F
|
20 |
import torch.utils.checkpoint
|
21 |
from torch import nn
|
22 |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
23 |
|
24 |
from transformers.activations import ACT2FN
|
25 |
+
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
|
26 |
from transformers.modeling_outputs import (
|
27 |
BaseModelOutputWithPast,
|
28 |
CausalLMOutputWithPast,
|
|
|
34 |
add_code_sample_docstrings,
|
35 |
add_start_docstrings,
|
36 |
add_start_docstrings_to_model_forward,
|
37 |
+
is_flash_attn_2_available,
|
38 |
+
is_flash_attn_greater_or_equal_2_10,
|
39 |
logging,
|
40 |
replace_return_docstrings,
|
41 |
)
|
42 |
from .configuration_opt import OPTConfig
|
43 |
+
|
44 |
+
|
45 |
+
if is_flash_attn_2_available():
|
46 |
+
from flash_attn import flash_attn_func, flash_attn_varlen_func
|
47 |
+
from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
|
48 |
|
49 |
|
50 |
logger = logging.get_logger(__name__)
|
51 |
|
52 |
_CHECKPOINT_FOR_DOC = "facebook/opt-350m"
|
53 |
_CONFIG_FOR_DOC = "OPTConfig"
|
|
|
54 |
|
55 |
# Base model docstring
|
56 |
_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
|
|
|
71 |
# See all OPT models at https://huggingface.co/models?filter=opt
|
72 |
]
|
73 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
74 |
|
75 |
+
# Copied from transformers.models.llama.modeling_llama._get_unpad_data
|
76 |
+
def _get_unpad_data(attention_mask):
|
77 |
+
seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
|
78 |
+
indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
|
79 |
+
max_seqlen_in_batch = seqlens_in_batch.max().item()
|
80 |
+
cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
|
81 |
+
return (
|
82 |
+
indices,
|
83 |
+
cu_seqlens,
|
84 |
+
max_seqlen_in_batch,
|
85 |
+
)
|
86 |
|
87 |
|
88 |
+
# class OPTLearnedPositionalEmbedding(nn.Embedding):
|
89 |
+
# """
|
90 |
+
# This module learns positional embeddings up to a fixed maximum size.
|
91 |
+
# """
|
92 |
+
|
93 |
+
# def __init__(self, num_embeddings: int, embedding_dim: int):
|
94 |
+
# # OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
|
95 |
+
# # and adjust num_embeddings appropriately. Other models don't have this hack
|
96 |
+
# self.offset = 2
|
97 |
+
# super().__init__(num_embeddings + self.offset, embedding_dim)
|
98 |
+
|
99 |
+
# def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
|
100 |
+
# """`input_ids_shape` is expected to be [bsz x seqlen]."""
|
101 |
+
# attention_mask = attention_mask.long()
|
102 |
|
103 |
+
# # create positions depending on attention_mask
|
104 |
+
# positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
|
105 |
|
106 |
+
# # cut positions if `past_key_values_length` is > 0
|
107 |
+
# positions = positions[:, past_key_values_length:]
|
108 |
|
109 |
+
# return super().forward(positions + self.offset)
|
110 |
|
111 |
|
112 |
+
class OPTLearnedPositionalEmbedding(nn.Module):
|
113 |
"""
|
114 |
This module learns positional embeddings up to a fixed maximum size.
|
115 |
"""
|
|
|
117 |
def __init__(self, num_embeddings: int, embedding_dim: int):
|
118 |
# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
|
119 |
# and adjust num_embeddings appropriately. Other models don't have this hack
|
120 |
+
super().__init__()
|
121 |
self.offset = 2
|
122 |
+
self.embeddings = nn.Embedding(num_embeddings + self.offset, embedding_dim)
|
123 |
|
124 |
+
def forward(
|
125 |
+
self, attention_mask: torch.LongTensor, past_key_values_length: int = 0
|
126 |
+
):
|
127 |
"""`input_ids_shape` is expected to be [bsz x seqlen]."""
|
128 |
attention_mask = attention_mask.long()
|
129 |
|
130 |
# create positions depending on attention_mask
|
131 |
+
positions = (
|
132 |
+
torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask
|
133 |
+
).long() - 1
|
134 |
|
135 |
# cut positions if `past_key_values_length` is > 0
|
136 |
positions = positions[:, past_key_values_length:]
|
137 |
|
138 |
+
return self.embeddings(positions + self.offset)
|
139 |
|
140 |
|
141 |
class OPTAttention(nn.Module):
|
|
|
143 |
|
144 |
def __init__(
|
145 |
self,
|
146 |
+
config: OPTConfig,
|
|
|
|
|
147 |
is_decoder: bool = False,
|
148 |
+
**kwargs,
|
149 |
):
|
150 |
super().__init__()
|
151 |
+
self.config = config
|
152 |
+
|
153 |
+
def _handle_deprecated_argument(config_arg_name, config, fn_arg_name, kwargs):
|
154 |
+
"""
|
155 |
+
If a the deprecated argument `fn_arg_name` is passed, raise a deprecation
|
156 |
+
warning and return that value, otherwise take the equivalent config.config_arg_name
|
157 |
+
"""
|
158 |
+
val = None
|
159 |
+
if fn_arg_name in kwargs:
|
160 |
+
logging.warning(
|
161 |
+
"Passing in {fn_arg_name} to {self.__class__.__name__} is deprecated and won't be supported from "
|
162 |
+
"v4.39. Please set it in the config instead"
|
163 |
+
)
|
164 |
+
val = kwargs.pop(fn_arg_name)
|
165 |
+
else:
|
166 |
+
val = getattr(config, config_arg_name)
|
167 |
+
return val
|
168 |
|
169 |
+
self.embed_dim = _handle_deprecated_argument(
|
170 |
+
"hidden_size", config, "embed_dim", kwargs
|
171 |
+
)
|
172 |
+
self.num_heads = _handle_deprecated_argument(
|
173 |
+
"num_attention_heads", config, "num_heads", kwargs
|
174 |
+
)
|
175 |
+
self.dropout = _handle_deprecated_argument(
|
176 |
+
"attention_dropout", config, "dropout", kwargs
|
177 |
+
)
|
178 |
+
self.enable_bias = _handle_deprecated_argument(
|
179 |
+
"enable_bias", config, "bias", kwargs
|
180 |
+
)
|
181 |
+
|
182 |
+
self.head_dim = self.embed_dim // self.num_heads
|
183 |
+
self.is_causal = True
|
184 |
+
|
185 |
+
if (self.head_dim * self.num_heads) != self.embed_dim:
|
186 |
raise ValueError(
|
187 |
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
|
188 |
+
f" and `num_heads`: {self.num_heads})."
|
189 |
)
|
190 |
self.scaling = self.head_dim**-0.5
|
191 |
self.is_decoder = is_decoder
|
192 |
|
193 |
+
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
|
194 |
+
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
|
195 |
+
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
|
196 |
+
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
|
|
|
|
|
|
|
197 |
|
198 |
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
199 |
+
return (
|
200 |
+
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
|
201 |
+
.transpose(1, 2)
|
202 |
+
.contiguous()
|
203 |
+
)
|
204 |
|
205 |
def forward(
|
206 |
self,
|
|
|
270 |
raise ValueError(
|
271 |
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
|
272 |
)
|
273 |
+
attn_weights = (
|
274 |
+
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
275 |
+
+ attention_mask
|
276 |
+
)
|
277 |
+
attn_weights = torch.max(
|
278 |
+
attn_weights,
|
279 |
+
torch.tensor(
|
280 |
+
torch.finfo(attn_weights.dtype).min, device=attn_weights.device
|
281 |
+
),
|
282 |
+
)
|
283 |
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
284 |
|
285 |
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
|
286 |
if attn_weights.dtype == torch.float16:
|
287 |
+
attn_weights = nn.functional.softmax(
|
288 |
+
attn_weights, dim=-1, dtype=torch.float32
|
289 |
+
).to(torch.float16)
|
290 |
else:
|
291 |
+
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
292 |
|
293 |
if layer_head_mask is not None:
|
294 |
if layer_head_mask.size() != (self.num_heads,):
|
|
|
296 |
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
|
297 |
f" {layer_head_mask.size()}"
|
298 |
)
|
299 |
+
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(
|
300 |
+
bsz, self.num_heads, tgt_len, src_len
|
301 |
+
)
|
302 |
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
303 |
|
304 |
if output_attentions:
|
|
|
306 |
# make sure that attn_weights keeps its gradient.
|
307 |
# In order to do so, attn_weights have to be reshaped
|
308 |
# twice and have to be reused in the following
|
309 |
+
attn_weights_reshaped = attn_weights.view(
|
310 |
+
bsz, self.num_heads, tgt_len, src_len
|
311 |
+
)
|
312 |
+
attn_weights = attn_weights_reshaped.view(
|
313 |
+
bsz * self.num_heads, tgt_len, src_len
|
314 |
+
)
|
315 |
else:
|
316 |
attn_weights_reshaped = None
|
317 |
|
318 |
+
attn_probs = nn.functional.dropout(
|
319 |
+
attn_weights, p=self.dropout, training=self.training
|
320 |
+
)
|
321 |
+
|
322 |
attn_output = torch.bmm(attn_probs, value_states)
|
323 |
|
324 |
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
|
|
|
339 |
return attn_output, attn_weights_reshaped, past_key_value
|
340 |
|
341 |
|
342 |
+
class OptFlashAttention2(OPTAttention):
|
343 |
+
"""
|
344 |
+
OPT flash attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
|
345 |
+
The only required change would be on the forward pass where it needs to correctly call the public API of flash
|
346 |
+
attention and deal with padding tokens in case the input contains any of them.
|
347 |
+
"""
|
348 |
+
|
349 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
|
350 |
+
def __init__(self, *args, **kwargs):
|
351 |
+
super().__init__(*args, **kwargs)
|
352 |
+
|
353 |
+
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
|
354 |
+
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
|
355 |
+
# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
|
356 |
+
self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
|
357 |
+
|
358 |
+
def forward(
|
359 |
+
self,
|
360 |
+
hidden_states: torch.Tensor,
|
361 |
+
key_value_states: Optional[torch.Tensor] = None,
|
362 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
363 |
+
attention_mask: Optional[torch.Tensor] = None,
|
364 |
+
layer_head_mask: Optional[torch.Tensor] = None,
|
365 |
+
output_attentions: bool = False,
|
366 |
+
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
367 |
+
"""Input shape: Batch x Time x Channel"""
|
368 |
+
|
369 |
+
# if key_value_states are provided this layer is used as a cross-attention layer
|
370 |
+
# for the decoder
|
371 |
+
is_cross_attention = key_value_states is not None
|
372 |
+
|
373 |
+
bsz, _, _ = hidden_states.size()
|
374 |
+
|
375 |
+
# get query proj
|
376 |
+
query_states = self.q_proj(hidden_states)
|
377 |
+
# get key, value proj
|
378 |
+
if is_cross_attention and past_key_value is not None:
|
379 |
+
# reuse k,v, cross_attentions
|
380 |
+
key_states = past_key_value[0]
|
381 |
+
value_states = past_key_value[1]
|
382 |
+
elif is_cross_attention:
|
383 |
+
# cross_attentions
|
384 |
+
key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
|
385 |
+
value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
|
386 |
+
elif past_key_value is not None:
|
387 |
+
# reuse k, v, self_attention
|
388 |
+
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
389 |
+
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
390 |
+
key_states = torch.cat([past_key_value[0], key_states], dim=2)
|
391 |
+
value_states = torch.cat([past_key_value[1], value_states], dim=2)
|
392 |
+
else:
|
393 |
+
# self_attention
|
394 |
+
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
395 |
+
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
396 |
+
|
397 |
+
if self.is_decoder:
|
398 |
+
# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
|
399 |
+
# Further calls to cross_attention layer can then reuse all cross-attention
|
400 |
+
# key/value_states (first "if" case)
|
401 |
+
# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
|
402 |
+
# all previous decoder key/value_states. Further calls to uni-directional self-attention
|
403 |
+
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
|
404 |
+
# if encoder bi-directional self-attention `past_key_value` is always `None`
|
405 |
+
past_key_value = (key_states, value_states)
|
406 |
+
|
407 |
+
query_length = query_states.shape[1]
|
408 |
+
tgt_len = key_states.shape[-2]
|
409 |
+
|
410 |
+
# Flash attention requires the input to have the shape
|
411 |
+
# batch_size x seq_length x head_dim x hidden_dim
|
412 |
+
query_states = query_states.view(
|
413 |
+
bsz, query_length, self.num_heads, self.head_dim
|
414 |
+
)
|
415 |
+
key_states = key_states.transpose(1, 2).view(
|
416 |
+
bsz, tgt_len, self.num_heads, self.head_dim
|
417 |
+
)
|
418 |
+
value_states = value_states.transpose(1, 2).view(
|
419 |
+
bsz, tgt_len, self.num_heads, self.head_dim
|
420 |
+
)
|
421 |
+
|
422 |
+
attn_dropout = self.dropout if self.training else 0.0
|
423 |
+
|
424 |
+
# In PEFT, usually we cast the layer norms in float32 for training stability reasons
|
425 |
+
# therefore the input hidden states gets silently casted in float32. Hence, we need
|
426 |
+
# cast them back in float16 just to be sure everything works as expected.
|
427 |
+
input_dtype = query_states.dtype
|
428 |
+
if input_dtype == torch.float32:
|
429 |
+
if torch.is_autocast_enabled():
|
430 |
+
target_dtype = torch.get_autocast_gpu_dtype()
|
431 |
+
# Handle the case where the model is quantized
|
432 |
+
elif hasattr(self.config, "_pre_quantization_dtype"):
|
433 |
+
target_dtype = self.config._pre_quantization_dtype
|
434 |
+
else:
|
435 |
+
target_dtype = self.q_proj.weight.dtype
|
436 |
+
|
437 |
+
logger.warning_once(
|
438 |
+
f"The input hidden states seems to be silently casted in float32, this might be related to"
|
439 |
+
f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
|
440 |
+
f" {target_dtype}."
|
441 |
+
)
|
442 |
+
|
443 |
+
query_states = query_states.to(target_dtype)
|
444 |
+
key_states = key_states.to(target_dtype)
|
445 |
+
value_states = value_states.to(target_dtype)
|
446 |
+
|
447 |
+
attn_output = self._flash_attention_forward(
|
448 |
+
query_states,
|
449 |
+
key_states,
|
450 |
+
value_states,
|
451 |
+
attention_mask,
|
452 |
+
query_length,
|
453 |
+
dropout=attn_dropout,
|
454 |
+
)
|
455 |
+
|
456 |
+
attn_weights_reshaped = attn_output.reshape(
|
457 |
+
bsz, query_length, self.num_heads * self.head_dim
|
458 |
+
)
|
459 |
+
attn_output = self.out_proj(attn_weights_reshaped)
|
460 |
+
|
461 |
+
if not output_attentions:
|
462 |
+
attn_weights_reshaped = None
|
463 |
+
|
464 |
+
return attn_output, attn_weights_reshaped, past_key_value
|
465 |
+
|
466 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
|
467 |
+
def _flash_attention_forward(
|
468 |
+
self,
|
469 |
+
query_states,
|
470 |
+
key_states,
|
471 |
+
value_states,
|
472 |
+
attention_mask,
|
473 |
+
query_length,
|
474 |
+
dropout=0.0,
|
475 |
+
softmax_scale=None,
|
476 |
+
):
|
477 |
+
"""
|
478 |
+
Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
|
479 |
+
first unpad the input, then computes the attention scores and pad the final attention scores.
|
480 |
+
Args:
|
481 |
+
query_states (`torch.Tensor`):
|
482 |
+
Input query states to be passed to Flash Attention API
|
483 |
+
key_states (`torch.Tensor`):
|
484 |
+
Input key states to be passed to Flash Attention API
|
485 |
+
value_states (`torch.Tensor`):
|
486 |
+
Input value states to be passed to Flash Attention API
|
487 |
+
attention_mask (`torch.Tensor`):
|
488 |
+
The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
|
489 |
+
position of padding tokens and 1 for the position of non-padding tokens.
|
490 |
+
dropout (`int`, *optional*):
|
491 |
+
Attention dropout
|
492 |
+
softmax_scale (`float`, *optional*):
|
493 |
+
The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
|
494 |
+
"""
|
495 |
+
if not self._flash_attn_uses_top_left_mask:
|
496 |
+
causal = self.is_causal
|
497 |
+
else:
|
498 |
+
# TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
|
499 |
+
causal = self.is_causal and query_length != 1
|
500 |
+
|
501 |
+
# Contains at least one padding token in the sequence
|
502 |
+
if attention_mask is not None:
|
503 |
+
batch_size = query_states.shape[0]
|
504 |
+
(
|
505 |
+
query_states,
|
506 |
+
key_states,
|
507 |
+
value_states,
|
508 |
+
indices_q,
|
509 |
+
cu_seq_lens,
|
510 |
+
max_seq_lens,
|
511 |
+
) = self._upad_input(
|
512 |
+
query_states, key_states, value_states, attention_mask, query_length
|
513 |
+
)
|
514 |
+
|
515 |
+
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
516 |
+
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
517 |
+
|
518 |
+
attn_output_unpad = flash_attn_varlen_func(
|
519 |
+
query_states,
|
520 |
+
key_states,
|
521 |
+
value_states,
|
522 |
+
cu_seqlens_q=cu_seqlens_q,
|
523 |
+
cu_seqlens_k=cu_seqlens_k,
|
524 |
+
max_seqlen_q=max_seqlen_in_batch_q,
|
525 |
+
max_seqlen_k=max_seqlen_in_batch_k,
|
526 |
+
dropout_p=dropout,
|
527 |
+
softmax_scale=softmax_scale,
|
528 |
+
causal=causal,
|
529 |
+
)
|
530 |
+
|
531 |
+
attn_output = pad_input(
|
532 |
+
attn_output_unpad, indices_q, batch_size, query_length
|
533 |
+
)
|
534 |
+
else:
|
535 |
+
attn_output = flash_attn_func(
|
536 |
+
query_states,
|
537 |
+
key_states,
|
538 |
+
value_states,
|
539 |
+
dropout,
|
540 |
+
softmax_scale=softmax_scale,
|
541 |
+
causal=causal,
|
542 |
+
)
|
543 |
+
|
544 |
+
return attn_output
|
545 |
+
|
546 |
+
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
547 |
+
def _upad_input(
|
548 |
+
self, query_layer, key_layer, value_layer, attention_mask, query_length
|
549 |
+
):
|
550 |
+
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
551 |
+
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
552 |
+
|
553 |
+
key_layer = index_first_axis(
|
554 |
+
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
555 |
+
indices_k,
|
556 |
+
)
|
557 |
+
value_layer = index_first_axis(
|
558 |
+
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
|
559 |
+
indices_k,
|
560 |
+
)
|
561 |
+
if query_length == kv_seq_len:
|
562 |
+
query_layer = index_first_axis(
|
563 |
+
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
|
564 |
+
indices_k,
|
565 |
+
)
|
566 |
+
cu_seqlens_q = cu_seqlens_k
|
567 |
+
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
568 |
+
indices_q = indices_k
|
569 |
+
elif query_length == 1:
|
570 |
+
max_seqlen_in_batch_q = 1
|
571 |
+
cu_seqlens_q = torch.arange(
|
572 |
+
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
573 |
+
) # There is a memcpy here, that is very bad.
|
574 |
+
indices_q = cu_seqlens_q[:-1]
|
575 |
+
query_layer = query_layer.squeeze(1)
|
576 |
+
else:
|
577 |
+
# The -q_len: slice assumes left padding.
|
578 |
+
attention_mask = attention_mask[:, -query_length:]
|
579 |
+
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
|
580 |
+
query_layer, attention_mask
|
581 |
+
)
|
582 |
+
|
583 |
+
return (
|
584 |
+
query_layer,
|
585 |
+
key_layer,
|
586 |
+
value_layer,
|
587 |
+
indices_q,
|
588 |
+
(cu_seqlens_q, cu_seqlens_k),
|
589 |
+
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
590 |
+
)
|
591 |
+
|
592 |
+
|
593 |
+
OPT_ATTENTION_CLASSES = {
|
594 |
+
"eager": OPTAttention,
|
595 |
+
"flash_attention_2": OptFlashAttention2,
|
596 |
+
}
|
597 |
+
|
598 |
+
|
599 |
class OPTDecoderLayer(nn.Module):
|
600 |
def __init__(self, config: OPTConfig):
|
601 |
super().__init__()
|
602 |
self.embed_dim = config.hidden_size
|
603 |
+
|
604 |
+
self.self_attn = OPT_ATTENTION_CLASSES[config._attn_implementation](
|
605 |
+
config=config, is_decoder=True
|
|
|
|
|
606 |
)
|
607 |
+
|
608 |
self.do_layer_norm_before = config.do_layer_norm_before
|
609 |
+
self.dropout = config.dropout
|
610 |
self.activation_fn = ACT2FN[config.activation_function]
|
611 |
|
612 |
+
self.self_attn_layer_norm = nn.LayerNorm(
|
613 |
+
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
|
614 |
+
)
|
615 |
+
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
|
616 |
+
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
|
617 |
+
self.final_layer_norm = nn.LayerNorm(
|
618 |
+
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
|
619 |
+
)
|
620 |
|
621 |
def forward(
|
622 |
self,
|
623 |
hidden_states: torch.Tensor,
|
624 |
attention_mask: Optional[torch.Tensor] = None,
|
625 |
layer_head_mask: Optional[torch.Tensor] = None,
|
626 |
+
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
627 |
output_attentions: Optional[bool] = False,
|
628 |
use_cache: Optional[bool] = False,
|
629 |
+
) -> Tuple[
|
630 |
+
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
|
631 |
+
]:
|
632 |
"""
|
633 |
Args:
|
634 |
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
|
659 |
layer_head_mask=layer_head_mask,
|
660 |
output_attentions=output_attentions,
|
661 |
)
|
662 |
+
hidden_states = nn.functional.dropout(
|
663 |
+
hidden_states, p=self.dropout, training=self.training
|
664 |
+
)
|
665 |
hidden_states = residual + hidden_states
|
666 |
|
667 |
# 350m applies layer norm AFTER attention
|
|
|
681 |
hidden_states = self.activation_fn(hidden_states)
|
682 |
|
683 |
hidden_states = self.fc2(hidden_states)
|
684 |
+
hidden_states = nn.functional.dropout(
|
685 |
+
hidden_states, p=self.dropout, training=self.training
|
686 |
+
)
|
687 |
|
688 |
hidden_states = (residual + hidden_states).view(hidden_states_shape)
|
689 |
|
|
|
706 |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
707 |
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
708 |
etc.)
|
|
|
709 |
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
710 |
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
711 |
and behavior.
|
|
|
712 |
Parameters:
|
713 |
config ([`OPTConfig`]):
|
714 |
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
|
726 |
base_model_prefix = "model"
|
727 |
supports_gradient_checkpointing = True
|
728 |
_no_split_modules = ["OPTDecoderLayer"]
|
729 |
+
_supports_flash_attn_2 = True
|
730 |
|
731 |
def _init_weights(self, module):
|
732 |
std = self.config.init_std
|
|
|
739 |
if module.padding_idx is not None:
|
740 |
module.weight.data[module.padding_idx].zero_()
|
741 |
|
|
|
|
|
|
|
|
|
742 |
|
743 |
OPT_INPUTS_DOCSTRING = r"""
|
744 |
Args:
|
745 |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
746 |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
747 |
it.
|
748 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
|
749 |
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
750 |
[What are input IDs?](../glossary#input-ids)
|
751 |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
752 |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
753 |
- 1 for tokens that are **not masked**,
|
754 |
- 0 for tokens that are **masked**.
|
|
|
755 |
[What are attention masks?](../glossary#attention-mask)
|
756 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
|
757 |
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
758 |
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
759 |
`past_key_values`).
|
|
|
760 |
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
761 |
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
762 |
information on the default strategy.
|
763 |
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
|
764 |
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
|
|
|
765 |
- 1 indicates the head is **not masked**,
|
766 |
- 0 indicates the head is **masked**.
|
|
|
767 |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
768 |
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
769 |
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
770 |
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
|
|
771 |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
772 |
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
773 |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
774 |
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
775 |
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
|
794 |
class OPTDecoder(OPTPreTrainedModel):
|
795 |
"""
|
796 |
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
|
|
|
797 |
Args:
|
798 |
config: OPTConfig
|
799 |
"""
|
|
|
806 |
self.max_target_positions = config.max_position_embeddings
|
807 |
self.vocab_size = config.vocab_size
|
808 |
|
809 |
+
self.embed_tokens = nn.Embedding(
|
810 |
+
config.vocab_size, config.word_embed_proj_dim, self.padding_idx
|
811 |
+
)
|
812 |
+
self._embed_positions = OPTLearnedPositionalEmbedding(
|
813 |
+
config.max_position_embeddings, config.hidden_size
|
814 |
+
)
|
815 |
+
self.embed_positions = self._embed_positions.embeddings
|
816 |
|
817 |
if config.word_embed_proj_dim != config.hidden_size:
|
818 |
+
self.project_out = nn.Linear(
|
819 |
+
config.hidden_size, config.word_embed_proj_dim, bias=False
|
820 |
+
)
|
821 |
else:
|
822 |
self.project_out = None
|
823 |
|
824 |
if config.word_embed_proj_dim != config.hidden_size:
|
825 |
+
self.project_in = nn.Linear(
|
826 |
+
config.word_embed_proj_dim, config.hidden_size, bias=False
|
827 |
+
)
|
828 |
else:
|
829 |
self.project_in = None
|
830 |
|
|
|
832 |
# with checkpoints that have been fine-tuned before transformers v4.20.1
|
833 |
# see https://github.com/facebookresearch/metaseq/pull/164
|
834 |
if config.do_layer_norm_before and not config._remove_final_layer_norm:
|
835 |
+
self.final_layer_norm = nn.LayerNorm(
|
836 |
+
config.hidden_size,
|
837 |
+
elementwise_affine=config.layer_norm_elementwise_affine,
|
838 |
+
)
|
839 |
else:
|
840 |
self.final_layer_norm = None
|
841 |
|
842 |
+
self.layers = nn.ModuleList(
|
843 |
+
[OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)]
|
844 |
+
)
|
845 |
+
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
846 |
|
847 |
self.gradient_checkpointing = False
|
848 |
# Initialize weights and apply final processing
|
|
|
854 |
def set_input_embeddings(self, value):
|
855 |
self.embed_tokens = value
|
856 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
857 |
def forward(
|
858 |
self,
|
859 |
input_ids: torch.LongTensor = None,
|
|
|
871 |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
872 |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
873 |
provide it.
|
874 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
|
875 |
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
876 |
[What are input IDs?](../glossary#input-ids)
|
877 |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
878 |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
879 |
- 1 for tokens that are **not masked**,
|
880 |
- 0 for tokens that are **masked**.
|
|
|
881 |
[What are attention masks?](../glossary#attention-mask)
|
882 |
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
883 |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
884 |
- 1 indicates the head is **not masked**,
|
885 |
- 0 indicates the head is **masked**.
|
|
|
886 |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
887 |
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
888 |
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
|
|
889 |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
890 |
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
891 |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
892 |
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
893 |
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
|
894 |
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
895 |
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
896 |
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
|
904 |
return_dict (`bool`, *optional*):
|
905 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
906 |
"""
|
907 |
+
output_attentions = (
|
908 |
+
output_attentions
|
909 |
+
if output_attentions is not None
|
910 |
+
else self.config.output_attentions
|
911 |
+
)
|
912 |
output_hidden_states = (
|
913 |
+
output_hidden_states
|
914 |
+
if output_hidden_states is not None
|
915 |
+
else self.config.output_hidden_states
|
916 |
)
|
917 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
918 |
|
919 |
+
return_dict = (
|
920 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
921 |
+
)
|
922 |
|
923 |
# retrieve input_ids and inputs_embeds
|
924 |
if input_ids is not None and inputs_embeds is not None:
|
925 |
+
raise ValueError(
|
926 |
+
"You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
|
927 |
+
)
|
928 |
elif input_ids is not None:
|
929 |
input_shape = input_ids.size()
|
930 |
input_ids = input_ids.view(-1, input_shape[-1])
|
931 |
elif inputs_embeds is not None:
|
932 |
input_shape = inputs_embeds.size()[:-1]
|
933 |
else:
|
934 |
+
raise ValueError(
|
935 |
+
"You have to specify either decoder_input_ids or decoder_inputs_embeds"
|
936 |
+
)
|
937 |
|
938 |
if inputs_embeds is None:
|
939 |
inputs_embeds = self.embed_tokens(input_ids)
|
940 |
|
941 |
+
batch_size, seq_length = input_shape
|
942 |
+
past_key_values_length = (
|
943 |
+
past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
944 |
+
)
|
945 |
+
# required mask seq length can be calculated via length of past
|
946 |
+
mask_seq_length = past_key_values_length + seq_length
|
947 |
+
|
948 |
# embed positions
|
949 |
+
if self._use_flash_attention_2:
|
950 |
+
# 2d mask is passed through the layers
|
951 |
+
causal_attention_mask = (
|
952 |
+
attention_mask
|
953 |
+
if (attention_mask is not None and 0 in attention_mask)
|
954 |
+
else None
|
955 |
+
)
|
956 |
+
attention_mask = (
|
957 |
+
torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
958 |
+
if attention_mask is None
|
959 |
+
else attention_mask
|
960 |
+
)
|
961 |
+
else:
|
962 |
+
# 4d mask is passed through the layers
|
963 |
+
if attention_mask is None:
|
964 |
+
attention_mask = torch.ones(
|
965 |
+
batch_size, mask_seq_length, device=inputs_embeds.device
|
966 |
+
)
|
967 |
+
elif attention_mask.shape[1] != mask_seq_length:
|
968 |
+
raise ValueError(
|
969 |
+
f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
|
970 |
+
f"{mask_seq_length} (sum of the lengths of current and past inputs)"
|
971 |
+
)
|
972 |
+
causal_attention_mask = _prepare_4d_causal_attention_mask(
|
973 |
+
attention_mask, input_shape, inputs_embeds, past_key_values_length
|
974 |
+
)
|
975 |
|
976 |
+
pos_embeds = self._embed_positions(attention_mask, past_key_values_length)
|
|
|
|
|
977 |
|
978 |
if self.project_in is not None:
|
979 |
inputs_embeds = self.project_in(inputs_embeds)
|
980 |
|
981 |
hidden_states = inputs_embeds + pos_embeds
|
982 |
|
983 |
+
if self.gradient_checkpointing and self.training:
|
984 |
+
if use_cache:
|
985 |
+
logger.warning_once(
|
986 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
987 |
+
)
|
988 |
+
use_cache = False
|
989 |
+
|
990 |
# decoder layers
|
991 |
all_hidden_states = () if output_hidden_states else None
|
992 |
all_self_attns = () if output_attentions else None
|
|
|
1006 |
if output_hidden_states:
|
1007 |
all_hidden_states += (hidden_states,)
|
1008 |
|
1009 |
+
if self.training:
|
1010 |
+
dropout_probability = torch.rand([])
|
1011 |
+
if dropout_probability < self.layerdrop:
|
1012 |
+
continue
|
1013 |
|
1014 |
+
past_key_value = (
|
1015 |
+
past_key_values[idx] if past_key_values is not None else None
|
1016 |
+
)
|
1017 |
|
1018 |
if self.gradient_checkpointing and self.training:
|
1019 |
+
layer_outputs = self._gradient_checkpointing_func(
|
1020 |
+
decoder_layer.__call__,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1021 |
hidden_states,
|
1022 |
+
causal_attention_mask,
|
1023 |
head_mask[idx] if head_mask is not None else None,
|
1024 |
None,
|
1025 |
+
output_attentions,
|
1026 |
+
use_cache,
|
1027 |
)
|
1028 |
else:
|
|
|
1029 |
layer_outputs = decoder_layer(
|
1030 |
hidden_states,
|
1031 |
+
attention_mask=causal_attention_mask,
|
1032 |
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
1033 |
past_key_value=past_key_value,
|
1034 |
output_attentions=output_attentions,
|
|
|
1043 |
if output_attentions:
|
1044 |
all_self_attns += (layer_outputs[1],)
|
1045 |
|
|
|
|
|
|
|
|
|
|
|
|
|
1046 |
if self.final_layer_norm is not None:
|
1047 |
hidden_states = self.final_layer_norm(hidden_states)
|
1048 |
|
|
|
1055 |
|
1056 |
next_cache = next_decoder_cache if use_cache else None
|
1057 |
if not return_dict:
|
1058 |
+
return tuple(
|
1059 |
+
v
|
1060 |
+
for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
|
1061 |
+
if v is not None
|
1062 |
+
)
|
1063 |
return BaseModelOutputWithPast(
|
1064 |
last_hidden_state=hidden_states,
|
1065 |
past_key_values=next_cache,
|
|
|
1076 |
def __init__(self, config: OPTConfig):
|
1077 |
super().__init__(config)
|
1078 |
self.decoder = OPTDecoder(config)
|
|
|
|
|
|
|
|
|
|
|
|
|
1079 |
# Initialize weights and apply final processing
|
1080 |
self.post_init()
|
1081 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1082 |
def get_input_embeddings(self):
|
1083 |
return self.decoder.embed_tokens
|
1084 |
|
|
|
1090 |
|
1091 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
1092 |
@add_code_sample_docstrings(
|
|
|
1093 |
checkpoint=_CHECKPOINT_FOR_DOC,
|
1094 |
output_type=BaseModelOutputWithPast,
|
1095 |
config_class=_CONFIG_FOR_DOC,
|
|
|
1107 |
output_hidden_states: Optional[bool] = None,
|
1108 |
return_dict: Optional[bool] = None,
|
1109 |
) -> Union[Tuple, BaseModelOutputWithPast]:
|
1110 |
+
output_attentions = (
|
1111 |
+
output_attentions
|
1112 |
+
if output_attentions is not None
|
1113 |
+
else self.config.output_attentions
|
1114 |
+
)
|
1115 |
output_hidden_states = (
|
1116 |
+
output_hidden_states
|
1117 |
+
if output_hidden_states is not None
|
1118 |
+
else self.config.output_hidden_states
|
1119 |
)
|
1120 |
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
1121 |
+
return_dict = (
|
1122 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
1123 |
+
)
|
1124 |
|
1125 |
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
|
1126 |
decoder_outputs = self.decoder(
|
|
|
1147 |
|
1148 |
|
1149 |
class OPTForCausalLM(OPTPreTrainedModel):
|
1150 |
+
_tied_weights_keys = ["lm_head.weight"]
|
1151 |
|
1152 |
def __init__(self, config):
|
1153 |
super().__init__(config)
|
1154 |
self.model = OPTModel(config)
|
1155 |
|
1156 |
# the lm_head weight is automatically tied to the embed tokens weight
|
1157 |
+
self.lm_head = nn.Linear(
|
1158 |
+
config.word_embed_proj_dim, config.vocab_size, bias=False
|
1159 |
+
)
|
|
|
|
|
1160 |
|
1161 |
# Initialize weights and apply final processing
|
1162 |
self.post_init()
|
1163 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1164 |
def get_input_embeddings(self):
|
1165 |
return self.model.decoder.embed_tokens
|
1166 |
|
|
|
1179 |
def get_decoder(self):
|
1180 |
return self.model.decoder
|
1181 |
|
1182 |
+
@replace_return_docstrings(
|
1183 |
+
output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
|
1184 |
+
)
|
1185 |
def forward(
|
1186 |
self,
|
1187 |
input_ids: torch.LongTensor = None,
|
|
|
1200 |
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
1201 |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
1202 |
provide it.
|
1203 |
+
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
|
1204 |
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
1205 |
[What are input IDs?](../glossary#input-ids)
|
1206 |
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
1207 |
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
1208 |
- 1 for tokens that are **not masked**,
|
1209 |
- 0 for tokens that are **masked**.
|
|
|
1210 |
[What are attention masks?](../glossary#attention-mask)
|
1211 |
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
1212 |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
1213 |
- 1 indicates the head is **not masked**,
|
1214 |
- 0 indicates the head is **masked**.
|
|
|
1215 |
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
1216 |
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
1217 |
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
1218 |
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
|
1219 |
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
|
|
|
1220 |
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
1221 |
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
1222 |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
1223 |
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
1224 |
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
|
1241 |
for more detail.
|
1242 |
return_dict (`bool`, *optional*):
|
1243 |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
|
1244 |
Returns:
|
|
|
1245 |
Example:
|
|
|
1246 |
```python
|
1247 |
+
>>> from transformers import AutoTokenizer, OPTForCausalLM
|
|
|
1248 |
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
|
1249 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
1250 |
+
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
|
1251 |
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
1252 |
>>> # Generate
|
1253 |
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
1254 |
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
1255 |
+
"Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
|
1256 |
```"""
|
1257 |
|
1258 |
+
output_attentions = (
|
1259 |
+
output_attentions
|
1260 |
+
if output_attentions is not None
|
1261 |
+
else self.config.output_attentions
|
1262 |
+
)
|
1263 |
output_hidden_states = (
|
1264 |
+
output_hidden_states
|
1265 |
+
if output_hidden_states is not None
|
1266 |
+
else self.config.output_hidden_states
|
1267 |
+
)
|
1268 |
+
return_dict = (
|
1269 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
1270 |
)
|
|
|
1271 |
|
1272 |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
1273 |
outputs = self.model.decoder(
|
|
|
1282 |
return_dict=return_dict,
|
1283 |
)
|
1284 |
|
1285 |
+
logits = self.lm_head(outputs[0]).contiguous()
|
|
|
|
|
|
|
|
|
1286 |
|
1287 |
loss = None
|
1288 |
if labels is not None:
|
|
|
1293 |
shift_labels = labels[..., 1:].contiguous()
|
1294 |
# Flatten the tokens
|
1295 |
loss_fct = CrossEntropyLoss()
|
1296 |
+
loss = loss_fct(
|
1297 |
+
shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1)
|
1298 |
+
)
|
1299 |
|
1300 |
if not return_dict:
|
1301 |
output = (logits,) + outputs[1:]
|
|
|
1309 |
attentions=outputs.attentions,
|
1310 |
)
|
1311 |
|
1312 |
+
def prepare_inputs_for_generation(
|
1313 |
+
self,
|
1314 |
+
input_ids,
|
1315 |
+
past_key_values=None,
|
1316 |
+
attention_mask=None,
|
1317 |
+
inputs_embeds=None,
|
1318 |
+
**kwargs,
|
1319 |
+
):
|
1320 |
+
if past_key_values is not None:
|
1321 |
+
past_length = past_key_values[0][0].shape[2]
|
1322 |
+
|
1323 |
+
# Some generation methods already pass only the last input ID
|
1324 |
+
if input_ids.shape[1] > past_length:
|
1325 |
+
remove_prefix_length = past_length
|
1326 |
+
else:
|
1327 |
+
# Default to old behavior: keep only final ID
|
1328 |
+
remove_prefix_length = input_ids.shape[1] - 1
|
1329 |
+
|
1330 |
+
input_ids = input_ids[:, remove_prefix_length:]
|
1331 |
+
|
1332 |
+
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
1333 |
+
if inputs_embeds is not None and past_key_values is None:
|
1334 |
+
model_inputs = {"inputs_embeds": inputs_embeds}
|
1335 |
+
else:
|
1336 |
+
model_inputs = {"input_ids": input_ids}
|
1337 |
+
|
1338 |
+
model_inputs.update(
|
1339 |
+
{
|
1340 |
+
"past_key_values": past_key_values,
|
1341 |
+
"use_cache": kwargs.get("use_cache"),
|
1342 |
+
"attention_mask": attention_mask,
|
1343 |
+
}
|
1344 |
+
)
|
1345 |
+
return model_inputs
|
1346 |
|
1347 |
@staticmethod
|
1348 |
+
def _reorder_cache(past_key_values, beam_idx):
|
1349 |
reordered_past = ()
|
1350 |
+
for layer_past in past_key_values:
|
1351 |
+
reordered_past += (
|
1352 |
+
tuple(
|
1353 |
+
past_state.index_select(0, beam_idx.to(past_state.device))
|
1354 |
+
for past_state in layer_past
|
1355 |
+
),
|
1356 |
+
)
|
1357 |
return reordered_past
|
1358 |
|
1359 |
|
1360 |
@add_start_docstrings(
|
1361 |
"""
|
1362 |
The OPT Model transformer with a sequence classification head on top (linear layer).
|
|
|
1363 |
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
1364 |
(e.g. GPT-2) do.
|
|
|
1365 |
Since it does classification on the last token, it requires to know the position of the last token. If a
|
1366 |
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
1367 |
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
|
|
1371 |
OPT_START_DOCSTRING,
|
1372 |
)
|
1373 |
class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
|
|
|
1374 |
def __init__(self, config: OPTConfig):
|
1375 |
super().__init__(config)
|
1376 |
self.num_labels = config.num_labels
|
|
|
1382 |
|
1383 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
1384 |
@add_code_sample_docstrings(
|
|
|
1385 |
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
|
1386 |
output_type=SequenceClassifierOutputWithPast,
|
1387 |
config_class=_CONFIG_FOR_DOC,
|
|
|
1407 |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
1408 |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
1409 |
"""
|
1410 |
+
return_dict = (
|
1411 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
1412 |
+
)
|
1413 |
|
1414 |
transformer_outputs = self.model(
|
1415 |
input_ids,
|
|
|
1434 |
sequence_lengths = -1
|
1435 |
else:
|
1436 |
if input_ids is not None:
|
1437 |
+
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
1438 |
+
sequence_lengths = (
|
1439 |
+
torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
1440 |
+
)
|
1441 |
+
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
1442 |
+
sequence_lengths = sequence_lengths.to(logits.device)
|
1443 |
else:
|
1444 |
sequence_lengths = -1
|
1445 |
logger.warning(
|
|
|
1447 |
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
1448 |
)
|
1449 |
|
1450 |
+
pooled_logits = logits[
|
1451 |
+
torch.arange(batch_size, device=logits.device), sequence_lengths
|
1452 |
+
]
|
1453 |
|
1454 |
loss = None
|
1455 |
if labels is not None:
|
1456 |
if self.config.problem_type is None:
|
1457 |
if self.num_labels == 1:
|
1458 |
self.config.problem_type = "regression"
|
1459 |
+
elif self.num_labels > 1 and (
|
1460 |
+
labels.dtype == torch.long or labels.dtype == torch.int
|
1461 |
+
):
|
1462 |
self.config.problem_type = "single_label_classification"
|
1463 |
else:
|
1464 |
self.config.problem_type = "multi_label_classification"
|
|
|
1471 |
loss = loss_fct(pooled_logits, labels)
|
1472 |
elif self.config.problem_type == "single_label_classification":
|
1473 |
loss_fct = CrossEntropyLoss()
|
1474 |
+
loss = loss_fct(
|
1475 |
+
pooled_logits.view(-1, self.num_labels), labels.view(-1)
|
1476 |
+
)
|
1477 |
elif self.config.problem_type == "multi_label_classification":
|
1478 |
loss_fct = BCEWithLogitsLoss()
|
1479 |
loss = loss_fct(pooled_logits, labels)
|
|
|
1504 |
OPT_START_DOCSTRING,
|
1505 |
)
|
1506 |
class OPTForQuestionAnswering(OPTPreTrainedModel):
|
|
|
|
|
1507 |
def __init__(self, config: OPTConfig):
|
1508 |
super().__init__(config)
|
1509 |
self.model = OPTModel(config)
|
|
|
1513 |
self.post_init()
|
1514 |
|
1515 |
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
1516 |
+
@replace_return_docstrings(
|
1517 |
+
output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC
|
1518 |
+
)
|
1519 |
def forward(
|
1520 |
self,
|
1521 |
input_ids: Optional[torch.LongTensor] = None,
|
|
|
1539 |
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1540 |
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1541 |
are not taken into account for computing the loss.
|
|
|
1542 |
Returns:
|
|
|
1543 |
Example:
|
|
|
1544 |
```python
|
1545 |
+
>>> from transformers import AutoTokenizer, OPTForQuestionAnswering
|
1546 |
>>> import torch
|
|
|
1547 |
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
|
1548 |
+
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
|
1549 |
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
|
1550 |
>>> # so the head will be randomly initialized, hence the predictions will be random
|
1551 |
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
|
|
|
1552 |
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
|
1553 |
>>> inputs = tokenizer(question, text, return_tensors="pt")
|
1554 |
>>> with torch.no_grad():
|
1555 |
... outputs = model(**inputs)
|
|
|
1556 |
>>> answer_start_index = outputs.start_logits.argmax()
|
1557 |
>>> answer_end_index = outputs.end_logits.argmax()
|
1558 |
+
>>> answer_offset = len(tokenizer(question)[0])
|
1559 |
+
>>> predict_answer_tokens = inputs.input_ids[
|
1560 |
+
... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
|
1561 |
+
... ]
|
1562 |
>>> predicted = tokenizer.decode(predict_answer_tokens)
|
1563 |
>>> predicted
|
1564 |
+
' a nice puppet'
|
1565 |
```"""
|
1566 |
+
return_dict = (
|
1567 |
+
return_dict if return_dict is not None else self.config.use_return_dict
|
1568 |
+
)
|
1569 |
|
1570 |
transformer_outputs = self.model(
|
1571 |
input_ids,
|