1457 lines
66 KiB
Python
1457 lines
66 KiB
Python
# coding=utf-8
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# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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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 ...activations import ACT2FN
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from ...modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from ...modeling_outputs import (
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BaseModelOutputWithPast,
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CausalLMOutputWithPast,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutputWithPast,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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logging,
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replace_return_docstrings,
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)
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from .configuration_opt import OPTConfig
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if is_flash_attn_2_available():
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from flash_attn import flash_attn_func, flash_attn_varlen_func
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from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
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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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# Base model docstring
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_EXPECTED_OUTPUT_SHAPE = [1, 8, 1024]
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# SequenceClassification docstring
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_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "ArthurZ/opt-350m-dummy-sc"
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_SEQ_CLASS_EXPECTED_LOSS = 1.71
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_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_0'"
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from ..deprecated._archive_maps import OPT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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# Copied from transformers.models.llama.modeling_llama._get_unpad_data
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def _get_unpad_data(attention_mask):
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seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
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indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
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max_seqlen_in_batch = seqlens_in_batch.max().item()
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cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
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return (
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indices,
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cu_seqlens,
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max_seqlen_in_batch,
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)
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class OPTLearnedPositionalEmbedding(nn.Embedding):
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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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super().__init__(num_embeddings + self.offset, embedding_dim)
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def forward(self, attention_mask: torch.LongTensor, past_key_values_length: int = 0):
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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 = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1
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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 super().forward(positions + self.offset)
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class OPTAttention(nn.Module):
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"""Multi-headed attention from 'Attention Is All You Need' paper"""
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def __init__(
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self,
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config: OPTConfig,
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is_decoder: bool = False,
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**kwargs,
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):
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super().__init__()
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self.config = config
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self.embed_dim = config.hidden_size
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self.num_heads = config.num_attention_heads
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self.dropout = config.attention_dropout
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self.enable_bias = config.enable_bias
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self.head_dim = self.embed_dim // self.num_heads
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self.is_causal = True
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if (self.head_dim * self.num_heads) != self.embed_dim:
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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`: {self.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(self.embed_dim, self.embed_dim, bias=self.enable_bias)
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self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
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self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
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self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=self.enable_bias)
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def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
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return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
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def forward(
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self,
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hidden_states: torch.Tensor,
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key_value_states: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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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: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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# if key_value_states are provided this layer is used as a cross-attention layer
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# for the decoder
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is_cross_attention = key_value_states is not None
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bsz, tgt_len, _ = hidden_states.size()
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# get query proj
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query_states = self.q_proj(hidden_states) * self.scaling
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# get key, value proj
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_states = past_key_value[0]
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value_states = past_key_value[1]
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elif is_cross_attention:
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# cross_attentions
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key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
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value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
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elif past_key_value is not None:
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# reuse k, v, self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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else:
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# self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_states, value_states)
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proj_shape = (bsz * self.num_heads, -1, self.head_dim)
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query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
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key_states = key_states.view(*proj_shape)
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value_states = value_states.view(*proj_shape)
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src_len = key_states.size(1)
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attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
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if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
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raise ValueError(
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f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
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f" {attn_weights.size()}"
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)
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if attention_mask is not None:
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if attention_mask.size() != (bsz, 1, tgt_len, src_len):
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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 = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
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attn_weights = torch.max(
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attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
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)
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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 = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
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else:
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attn_weights = nn.functional.softmax(attn_weights, dim=-1)
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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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raise ValueError(
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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(bsz, self.num_heads, tgt_len, src_len)
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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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# this operation is a bit awkward, but it's required to
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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(bsz, self.num_heads, tgt_len, src_len)
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attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
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else:
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attn_weights_reshaped = None
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attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
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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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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
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attn_output = attn_output.transpose(1, 2)
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# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
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# partitioned aross GPUs when using tensor-parallelism.
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attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
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attn_output = self.out_proj(attn_output)
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return attn_output, attn_weights_reshaped, past_key_value
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class OptFlashAttention2(OPTAttention):
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"""
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OPT flash attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
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The only required change would be on the forward pass where it needs to correctly call the public API of flash
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attention and deal with padding tokens in case the input contains any of them.
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"""
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# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# 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.
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# 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).
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
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def forward(
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self,
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hidden_states: torch.Tensor,
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key_value_states: Optional[torch.Tensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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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: bool = False,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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"""Input shape: Batch x Time x Channel"""
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# if key_value_states are provided this layer is used as a cross-attention layer
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# for the decoder
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is_cross_attention = key_value_states is not None
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bsz, _, _ = hidden_states.size()
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# get query proj
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query_states = self.q_proj(hidden_states)
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# get key, value proj
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if is_cross_attention and past_key_value is not None:
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# reuse k,v, cross_attentions
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key_states = past_key_value[0]
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value_states = past_key_value[1]
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elif is_cross_attention:
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# cross_attentions
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key_states = self._shape(self.k_proj(key_value_states), -1, bsz)
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value_states = self._shape(self.v_proj(key_value_states), -1, bsz)
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elif past_key_value is not None:
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# reuse k, v, self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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else:
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# self_attention
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key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
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value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
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if self.is_decoder:
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# if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
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# Further calls to cross_attention layer can then reuse all cross-attention
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# key/value_states (first "if" case)
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# if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
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# all previous decoder key/value_states. Further calls to uni-directional self-attention
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# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
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# if encoder bi-directional self-attention `past_key_value` is always `None`
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past_key_value = (key_states, value_states)
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query_length = query_states.shape[1]
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tgt_len = key_states.shape[-2]
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# Flash attention requires the input to have the shape
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# batch_size x seq_length x head_dim x hidden_dim
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query_states = query_states.view(bsz, query_length, self.num_heads, self.head_dim)
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key_states = key_states.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim)
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value_states = value_states.transpose(1, 2).view(bsz, tgt_len, self.num_heads, self.head_dim)
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attn_dropout = self.dropout if self.training else 0.0
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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attn_output = self._flash_attention_forward(
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query_states, key_states, value_states, attention_mask, query_length, dropout=attn_dropout
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)
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attn_weights_reshaped = attn_output.reshape(bsz, query_length, self.num_heads * self.head_dim)
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attn_output = self.out_proj(attn_weights_reshaped)
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if not output_attentions:
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attn_weights_reshaped = None
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return attn_output, attn_weights_reshaped, past_key_value
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# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
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def _flash_attention_forward(
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self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
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):
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"""
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Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
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first unpad the input, then computes the attention scores and pad the final attention scores.
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Args:
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query_states (`torch.Tensor`):
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Input query states to be passed to Flash Attention API
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key_states (`torch.Tensor`):
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Input key states to be passed to Flash Attention API
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value_states (`torch.Tensor`):
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Input value states to be passed to Flash Attention API
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attention_mask (`torch.Tensor`):
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The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
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position of padding tokens and 1 for the position of non-padding tokens.
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dropout (`float`):
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Attention dropout
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softmax_scale (`float`, *optional*):
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The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
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"""
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if not self._flash_attn_uses_top_left_mask:
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causal = self.is_causal
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else:
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# 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__.
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causal = self.is_causal and query_length != 1
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# Contains at least one padding token in the sequence
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if attention_mask is not None:
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batch_size = query_states.shape[0]
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query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
|
|
query_states, key_states, value_states, attention_mask, query_length
|
|
)
|
|
|
|
cu_seqlens_q, cu_seqlens_k = cu_seq_lens
|
|
max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
|
|
|
|
attn_output_unpad = flash_attn_varlen_func(
|
|
query_states,
|
|
key_states,
|
|
value_states,
|
|
cu_seqlens_q=cu_seqlens_q,
|
|
cu_seqlens_k=cu_seqlens_k,
|
|
max_seqlen_q=max_seqlen_in_batch_q,
|
|
max_seqlen_k=max_seqlen_in_batch_k,
|
|
dropout_p=dropout,
|
|
softmax_scale=softmax_scale,
|
|
causal=causal,
|
|
)
|
|
|
|
attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
|
|
else:
|
|
attn_output = flash_attn_func(
|
|
query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
|
|
)
|
|
|
|
return attn_output
|
|
|
|
# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
|
|
def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
|
|
indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
|
|
batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
|
|
|
|
key_layer = index_first_axis(
|
|
key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
|
)
|
|
value_layer = index_first_axis(
|
|
value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
|
|
)
|
|
if query_length == kv_seq_len:
|
|
query_layer = index_first_axis(
|
|
query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
|
|
)
|
|
cu_seqlens_q = cu_seqlens_k
|
|
max_seqlen_in_batch_q = max_seqlen_in_batch_k
|
|
indices_q = indices_k
|
|
elif query_length == 1:
|
|
max_seqlen_in_batch_q = 1
|
|
cu_seqlens_q = torch.arange(
|
|
batch_size + 1, dtype=torch.int32, device=query_layer.device
|
|
) # There is a memcpy here, that is very bad.
|
|
indices_q = cu_seqlens_q[:-1]
|
|
query_layer = query_layer.squeeze(1)
|
|
else:
|
|
# The -q_len: slice assumes left padding.
|
|
attention_mask = attention_mask[:, -query_length:]
|
|
query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
|
|
|
|
return (
|
|
query_layer,
|
|
key_layer,
|
|
value_layer,
|
|
indices_q,
|
|
(cu_seqlens_q, cu_seqlens_k),
|
|
(max_seqlen_in_batch_q, max_seqlen_in_batch_k),
|
|
)
|
|
|
|
|
|
OPT_ATTENTION_CLASSES = {
|
|
"eager": OPTAttention,
|
|
"flash_attention_2": OptFlashAttention2,
|
|
}
|
|
|
|
|
|
class OPTDecoderLayer(nn.Module):
|
|
def __init__(self, config: OPTConfig):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
|
|
self.self_attn = OPT_ATTENTION_CLASSES[config._attn_implementation](config=config, is_decoder=True)
|
|
|
|
self.do_layer_norm_before = config.do_layer_norm_before
|
|
self.dropout = config.dropout
|
|
self.activation_fn = ACT2FN[config.activation_function]
|
|
|
|
self.self_attn_layer_norm = nn.LayerNorm(
|
|
self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
|
|
)
|
|
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
|
|
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
|
|
self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
layer_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[Tuple[torch.Tensor]] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = False,
|
|
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
|
|
`(encoder_attention_heads,)`.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
|
"""
|
|
|
|
residual = hidden_states
|
|
|
|
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
|
|
if self.do_layer_norm_before:
|
|
hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
past_key_value=past_key_value,
|
|
attention_mask=attention_mask,
|
|
layer_head_mask=layer_head_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# 350m applies layer norm AFTER attention
|
|
if not self.do_layer_norm_before:
|
|
hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
|
# Fully Connected
|
|
hidden_states_shape = hidden_states.shape
|
|
hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
|
|
residual = hidden_states
|
|
|
|
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
|
|
if self.do_layer_norm_before:
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
|
|
hidden_states = self.fc1(hidden_states)
|
|
hidden_states = self.activation_fn(hidden_states)
|
|
|
|
hidden_states = self.fc2(hidden_states)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
|
|
hidden_states = (residual + hidden_states).view(hidden_states_shape)
|
|
|
|
# 350m applies layer norm AFTER attention
|
|
if not self.do_layer_norm_before:
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights,)
|
|
|
|
if use_cache:
|
|
outputs += (present_key_value,)
|
|
|
|
return outputs
|
|
|
|
|
|
OPT_START_DOCSTRING = r"""
|
|
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
|
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
|
etc.)
|
|
|
|
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
|
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
|
and behavior.
|
|
|
|
Parameters:
|
|
config ([`OPTConfig`]):
|
|
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
|
load the weights associated with the model, only the configuration. Check out the
|
|
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
|
|
OPT_START_DOCSTRING,
|
|
)
|
|
class OPTPreTrainedModel(PreTrainedModel):
|
|
config_class = OPTConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["OPTDecoderLayer"]
|
|
_supports_flash_attn_2 = True
|
|
|
|
def _init_weights(self, module):
|
|
std = self.config.init_std
|
|
if isinstance(module, nn.Linear):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.Embedding):
|
|
module.weight.data.normal_(mean=0.0, std=std)
|
|
if module.padding_idx is not None:
|
|
module.weight.data[module.padding_idx].zero_()
|
|
|
|
|
|
OPT_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
|
it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
|
`past_key_values`).
|
|
|
|
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
|
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
|
information on the default strategy.
|
|
head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
|
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
|
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
|
model's internal embedding lookup matrix.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
class OPTDecoder(OPTPreTrainedModel):
|
|
"""
|
|
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
|
|
|
|
Args:
|
|
config: OPTConfig
|
|
"""
|
|
|
|
def __init__(self, config: OPTConfig):
|
|
super().__init__(config)
|
|
self.dropout = config.dropout
|
|
self.layerdrop = config.layerdrop
|
|
self.padding_idx = config.pad_token_id
|
|
self.max_target_positions = config.max_position_embeddings
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = nn.Embedding(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
|
|
self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
|
|
|
|
if config.word_embed_proj_dim != config.hidden_size:
|
|
self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
|
|
else:
|
|
self.project_out = None
|
|
|
|
if config.word_embed_proj_dim != config.hidden_size:
|
|
self.project_in = nn.Linear(config.word_embed_proj_dim, config.hidden_size, bias=False)
|
|
else:
|
|
self.project_in = None
|
|
|
|
# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
|
|
# with checkpoints that have been fine-tuned before transformers v4.20.1
|
|
# see https://github.com/facebookresearch/metaseq/pull/164
|
|
if config.do_layer_norm_before and not config._remove_final_layer_norm:
|
|
self.final_layer_norm = nn.LayerNorm(
|
|
config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
|
|
)
|
|
else:
|
|
self.final_layer_norm = None
|
|
|
|
self.layers = nn.ModuleList([OPTDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
|
self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embed_tokens = value
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
|
provide it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
|
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# retrieve input_ids and inputs_embeds
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
input_ids = input_ids.view(-1, input_shape[-1])
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
batch_size, seq_length = input_shape
|
|
past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
|
|
# required mask seq length can be calculated via length of past
|
|
mask_seq_length = past_key_values_length + seq_length
|
|
|
|
# embed positions
|
|
if self._use_flash_attention_2:
|
|
# 2d mask is passed through the layers
|
|
causal_attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
|
|
attention_mask = (
|
|
torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
|
if attention_mask is None
|
|
else attention_mask
|
|
)
|
|
else:
|
|
# 4d mask is passed through the layers
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
|
|
elif attention_mask.shape[1] != mask_seq_length:
|
|
raise ValueError(
|
|
f"The provided attention mask has length {attention_mask.shape[1]}, but its length should be "
|
|
f"{mask_seq_length} (sum of the lengths of current and past inputs)"
|
|
)
|
|
causal_attention_mask = _prepare_4d_causal_attention_mask(
|
|
attention_mask, input_shape, inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
pos_embeds = self.embed_positions(attention_mask, past_key_values_length)
|
|
|
|
if self.project_in is not None:
|
|
inputs_embeds = self.project_in(inputs_embeds)
|
|
|
|
hidden_states = inputs_embeds + pos_embeds
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
if use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
|
)
|
|
use_cache = False
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
next_decoder_cache = () if use_cache else None
|
|
|
|
# check if head_mask has a correct number of layers specified if desired
|
|
for attn_mask, mask_name in zip([head_mask], ["head_mask"]):
|
|
if attn_mask is not None:
|
|
if attn_mask.size()[0] != (len(self.layers)):
|
|
raise ValueError(
|
|
f"The `{mask_name}` should be specified for {len(self.layers)} layers, but it is for"
|
|
f" {head_mask.size()[0]}."
|
|
)
|
|
|
|
for idx, decoder_layer in enumerate(self.layers):
|
|
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if self.training:
|
|
dropout_probability = torch.rand([])
|
|
if dropout_probability < self.layerdrop:
|
|
continue
|
|
|
|
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
|
|
|
if self.gradient_checkpointing and self.training:
|
|
layer_outputs = self._gradient_checkpointing_func(
|
|
decoder_layer.__call__,
|
|
hidden_states,
|
|
causal_attention_mask,
|
|
head_mask[idx] if head_mask is not None else None,
|
|
None,
|
|
output_attentions,
|
|
use_cache,
|
|
)
|
|
else:
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_attention_mask,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if use_cache:
|
|
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if self.final_layer_norm is not None:
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
|
|
if self.project_out is not None:
|
|
hidden_states = self.project_out(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
next_cache = next_decoder_cache if use_cache else None
|
|
if not return_dict:
|
|
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=next_cache,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare OPT Model outputting raw hidden-states without any specific head on top.",
|
|
OPT_START_DOCSTRING,
|
|
)
|
|
class OPTModel(OPTPreTrainedModel):
|
|
def __init__(self, config: OPTConfig):
|
|
super().__init__(config)
|
|
self.decoder = OPTDecoder(config)
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.decoder.embed_tokens = value
|
|
|
|
def get_decoder(self):
|
|
return self.decoder
|
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# decoder outputs consists of (dec_features, past_key_value, dec_hidden, dec_attn)
|
|
decoder_outputs = self.decoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if not return_dict:
|
|
return decoder_outputs
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=decoder_outputs.last_hidden_state,
|
|
past_key_values=decoder_outputs.past_key_values,
|
|
hidden_states=decoder_outputs.hidden_states,
|
|
attentions=decoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class OPTForCausalLM(OPTPreTrainedModel):
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = OPTModel(config)
|
|
|
|
# the lm_head weight is automatically tied to the embed tokens weight
|
|
self.lm_head = nn.Linear(config.word_embed_proj_dim, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.decoder.embed_tokens = value
|
|
|
|
def get_output_embeddings(self):
|
|
return self.lm_head
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.lm_head = new_embeddings
|
|
|
|
def set_decoder(self, decoder):
|
|
self.model.decoder = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model.decoder
|
|
|
|
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
Args:
|
|
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
|
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
|
|
provide it.
|
|
|
|
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
|
[`PreTrainedTokenizer.__call__`] for details.
|
|
|
|
[What are input IDs?](../glossary#input-ids)
|
|
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 for tokens that are **not masked**,
|
|
- 0 for tokens that are **masked**.
|
|
|
|
[What are attention masks?](../glossary#attention-mask)
|
|
head_mask (`torch.Tensor` of shape `(num_hidden_layers, num_attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
|
|
shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
|
|
shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional
|
|
tensors are only required when the model is used as a decoder in a Sequence to Sequence model.
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
|
|
cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
|
|
that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
|
|
all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
|
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
|
|
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
|
|
than the model's internal embedding lookup matrix.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
|
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
|
(see `past_key_values`).
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
|
|
for more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, OPTForCausalLM
|
|
|
|
>>> model = OPTForCausalLM.from_pretrained("facebook/opt-350m")
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> prompt = "Hey, are you conscious? Can you talk to me?"
|
|
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
|
|
|
>>> # Generate
|
|
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
|
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
|
"Hey, are you conscious? Can you talk to me?\nI'm not conscious. I'm just a little bit of a weirdo."
|
|
```"""
|
|
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
|
outputs = self.model.decoder(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
past_key_values=past_key_values,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
logits = self.lm_head(outputs[0]).contiguous()
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
# Shift so that tokens < n predict n
|
|
shift_logits = logits[..., :-1, :].contiguous()
|
|
shift_labels = labels[..., 1:].contiguous()
|
|
# Flatten the tokens
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
|
):
|
|
if past_key_values is not None:
|
|
past_length = past_key_values[0][0].shape[2]
|
|
|
|
# Some generation methods already pass only the last input ID
|
|
if input_ids.shape[1] > past_length:
|
|
remove_prefix_length = past_length
|
|
else:
|
|
# Default to old behavior: keep only final ID
|
|
remove_prefix_length = input_ids.shape[1] - 1
|
|
|
|
input_ids = input_ids[:, remove_prefix_length:]
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and past_key_values is None:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs.update(
|
|
{
|
|
"past_key_values": past_key_values,
|
|
"use_cache": kwargs.get("use_cache"),
|
|
"attention_mask": attention_mask,
|
|
}
|
|
)
|
|
return model_inputs
|
|
|
|
@staticmethod
|
|
def _reorder_cache(past_key_values, beam_idx):
|
|
reordered_past = ()
|
|
for layer_past in past_key_values:
|
|
reordered_past += (
|
|
tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
|
|
)
|
|
return reordered_past
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The OPT Model transformer with a sequence classification head on top (linear layer).
|
|
|
|
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
|
(e.g. GPT-2) do.
|
|
|
|
Since it does classification on the last token, it requires to know the position of the last token. If a
|
|
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
|
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
|
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
|
each row of the batch).
|
|
""",
|
|
OPT_START_DOCSTRING,
|
|
)
|
|
class OPTForSequenceClassification(OPTPreTrainedModel):
|
|
def __init__(self, config: OPTConfig):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.model = OPTModel(config)
|
|
self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
|
|
output_type=SequenceClassifierOutputWithPast,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
|
|
expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
|
|
)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.model(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.score(hidden_states)
|
|
|
|
if input_ids is not None:
|
|
batch_size, sequence_length = input_ids.shape[:2]
|
|
else:
|
|
batch_size, sequence_length = inputs_embeds.shape[:2]
|
|
|
|
if self.config.pad_token_id is None:
|
|
sequence_lengths = -1
|
|
else:
|
|
if input_ids is not None:
|
|
# if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
|
|
sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
|
|
sequence_lengths = sequence_lengths % input_ids.shape[-1]
|
|
sequence_lengths = sequence_lengths.to(logits.device)
|
|
else:
|
|
sequence_lengths = -1
|
|
logger.warning(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(pooled_logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(pooled_logits, labels)
|
|
if not return_dict:
|
|
output = (pooled_logits,) + transformer_outputs[1:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return SequenceClassifierOutputWithPast(
|
|
loss=loss,
|
|
logits=pooled_logits,
|
|
past_key_values=transformer_outputs.past_key_values,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.decoder.embed_tokens = value
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
The OPT Model transformer with a span classification head on top for extractive question-answering tasks like SQuAD
|
|
(a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
|
|
""",
|
|
OPT_START_DOCSTRING,
|
|
)
|
|
class OPTForQuestionAnswering(OPTPreTrainedModel):
|
|
def __init__(self, config: OPTConfig):
|
|
super().__init__(config)
|
|
self.model = OPTModel(config)
|
|
self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(OPT_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=QuestionAnsweringModelOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
start_positions: Optional[torch.LongTensor] = None,
|
|
end_positions: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, OPTForQuestionAnswering
|
|
>>> import torch
|
|
|
|
>>> torch.manual_seed(4) # doctest: +IGNORE_RESULT
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> # note: we are loading a OPTForQuestionAnswering from the hub here,
|
|
>>> # so the head will be randomly initialized, hence the predictions will be random
|
|
>>> model = OPTForQuestionAnswering.from_pretrained("facebook/opt-350m")
|
|
|
|
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
|
|
|
|
>>> inputs = tokenizer(question, text, return_tensors="pt")
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
|
|
>>> answer_start_index = outputs.start_logits.argmax()
|
|
>>> answer_end_index = outputs.end_logits.argmax()
|
|
|
|
>>> answer_offset = len(tokenizer(question)[0])
|
|
|
|
>>> predict_answer_tokens = inputs.input_ids[
|
|
... 0, answer_offset + answer_start_index : answer_offset + answer_end_index + 1
|
|
... ]
|
|
>>> predicted = tokenizer.decode(predict_answer_tokens)
|
|
>>> predicted
|
|
' a nice puppet'
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
transformer_outputs = self.model(
|
|
input_ids,
|
|
past_key_values=past_key_values,
|
|
attention_mask=attention_mask,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = transformer_outputs[0]
|
|
|
|
logits = self.qa_outputs(hidden_states)
|
|
start_logits, end_logits = logits.split(1, dim=-1)
|
|
start_logits = start_logits.squeeze(-1).contiguous()
|
|
end_logits = end_logits.squeeze(-1).contiguous()
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if len(start_positions.size()) > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if len(end_positions.size()) > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.size(1)
|
|
start_positions = start_positions.clamp(0, ignored_index)
|
|
end_positions = end_positions.clamp(0, ignored_index)
|
|
|
|
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + transformer_outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
def get_input_embeddings(self):
|
|
return self.model.decoder.embed_tokens
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.model.decoder.embed_tokens = value
|