1110 lines
48 KiB
Python
1110 lines
48 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 Callable, Optional, 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 ...activations import ACT2FN
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from ...cache_utils import Cache, DynamicCache
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from ...generation import GenerationMixin
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from ...modeling_attn_mask_utils import AttentionMaskConverter
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...modeling_layers import GradientCheckpointingLayer
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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 ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...processing_utils import Unpack
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from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_flex_attn_available, logging
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from .configuration_opt import OPTConfig
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if is_torch_flex_attn_available():
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from torch.nn.attention.flex_attention import BlockMask
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from ...integrations.flex_attention import make_flex_block_causal_mask
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logger = logging.get_logger(__name__)
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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(
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self,
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attention_mask: torch.LongTensor,
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past_key_values_length: int = 0,
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position_ids: Optional[torch.LongTensor] = None,
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):
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"""`input_ids_shape` is expected to be [bsz x seqlen]."""
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if position_ids is None:
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position_ids = torch.cumsum(attention_mask, dim=1)
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position_ids = (position_ids * attention_mask - 1).long()
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# cut positions if `past_key_values_length` is > 0
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position_ids = position_ids[:, past_key_values_length:]
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return super().forward(position_ids + self.offset)
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# Copied from transformers.models.siglip.modeling_siglip.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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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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layer_idx: Optional[int] = None,
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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.layer_idx = layer_idx
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if layer_idx is None:
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logger.warning_once(
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f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
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"lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
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"when creating this class."
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)
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self.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.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 forward(
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self,
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hidden_states: torch.Tensor,
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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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cache_position: Optional[torch.Tensor] = None,
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**kwargs,
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) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[Cache]]:
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"""Input shape: Batch x Time x Channel"""
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bsz, tgt_len, _ = hidden_states.size()
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# Scaling is susceptible to floating point arithmetics' inprecisions
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# which can lead to different results (this is dependent from model
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# to model, e.g. whisper is one such case). We therefore keep the
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# original order of scaling to follow the original implementation
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# and enforce no scaling (1.0) in the attention call below.
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query_states = self.q_proj(hidden_states) * self.scaling
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query_states = query_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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key_states = key_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, -1, self.num_heads, self.head_dim).transpose(1, 2)
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if past_key_value is not None:
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# save all key/value_states to cache to be re-used for fast auto-regressive generation
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key_states, value_states = past_key_value.update(
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key_states, value_states, self.layer_idx, {"cache_position": cache_position}
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)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and output_attentions:
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.dropout,
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scaling=1.0,
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**kwargs,
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)
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attn_output = attn_output.reshape(bsz, tgt_len, -1).contiguous()
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attn_output = self.out_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights
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class OPTDecoderLayer(GradientCheckpointingLayer):
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def __init__(self, config: OPTConfig, layer_idx: Optional[int] = None):
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super().__init__()
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self.embed_dim = config.hidden_size
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self.self_attn = OPTAttention(config=config, layer_idx=layer_idx)
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self.do_layer_norm_before = config.do_layer_norm_before
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self.dropout = config.dropout
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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.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
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)
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self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim, bias=config.enable_bias)
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self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim, bias=config.enable_bias)
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self.final_layer_norm = nn.LayerNorm(self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine)
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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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past_key_value: Optional[tuple[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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position_ids: Optional[torch.LongTensor] = None,
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cache_position: Optional[torch.Tensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
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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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attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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layer_head_mask (`torch.FloatTensor`, *optional*): mask for attention heads in a given layer of size
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`(encoder_attention_heads,)`.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
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Indices depicting the position of the input sequence tokens in the sequence..
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"""
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residual = hidden_states
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# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
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if self.do_layer_norm_before:
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hidden_states = self.self_attn_layer_norm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights = self.self_attn(
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hidden_states=hidden_states,
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past_key_value=past_key_value,
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position_ids=position_ids,
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attention_mask=attention_mask,
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layer_head_mask=layer_head_mask,
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output_attentions=output_attentions,
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cache_position=cache_position,
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**kwargs,
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)
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hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = residual + hidden_states
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# 350m applies layer norm AFTER attention
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if not self.do_layer_norm_before:
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hidden_states = self.self_attn_layer_norm(hidden_states)
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# Fully Connected
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hidden_states_shape = hidden_states.shape
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hidden_states = hidden_states.reshape(-1, hidden_states.size(-1))
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residual = hidden_states
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# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
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if self.do_layer_norm_before:
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hidden_states = self.final_layer_norm(hidden_states)
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hidden_states = self.fc1(hidden_states)
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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 = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
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hidden_states = (residual + hidden_states).view(hidden_states_shape)
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# 350m applies layer norm AFTER attention
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if not self.do_layer_norm_before:
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hidden_states = self.final_layer_norm(hidden_states)
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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return outputs
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@auto_docstring
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class OPTPreTrainedModel(PreTrainedModel):
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config: OPTConfig
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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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_supports_attention_backend = True
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_supports_flash_attn = True
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_supports_sdpa = True
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_supports_flex_attn = True
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_can_compile_fullgraph = True
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def _init_weights(self, module):
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std = self.config.init_std
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if isinstance(module, nn.Linear):
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module.weight.data.normal_(mean=0.0, std=std)
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if module.bias is not None:
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module.bias.data.zero_()
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elif isinstance(module, nn.Embedding):
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module.weight.data.normal_(mean=0.0, std=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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elif isinstance(module, nn.LayerNorm):
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module.weight.data.fill_(1.0)
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module.bias.data.zero_()
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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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def __init__(self, config: OPTConfig):
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super().__init__(config)
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self.dropout = config.dropout
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self.layerdrop = config.layerdrop
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self.padding_idx = config.pad_token_id
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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(config.vocab_size, config.word_embed_proj_dim, self.padding_idx)
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self.embed_positions = OPTLearnedPositionalEmbedding(config.max_position_embeddings, config.hidden_size)
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if config.word_embed_proj_dim != config.hidden_size:
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self.project_out = nn.Linear(config.hidden_size, config.word_embed_proj_dim, bias=False)
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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(config.word_embed_proj_dim, config.hidden_size, bias=False)
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else:
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self.project_in = None
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# Note that the only purpose of `config._remove_final_layer_norm` is to keep backward compatibility
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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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config.hidden_size, elementwise_affine=config.layer_norm_elementwise_affine
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)
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else:
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self.final_layer_norm = None
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self.layers = nn.ModuleList([OPTDecoderLayer(config, layer_idx=i) for i in range(config.num_hidden_layers)])
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self.gradient_checkpointing = False
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# Initialize weights and apply final processing
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self.post_init()
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# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._update_causal_mask
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def _update_causal_mask(
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self,
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attention_mask: Union[torch.Tensor, "BlockMask"],
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input_tensor: torch.Tensor,
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cache_position: torch.Tensor,
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past_key_values: Cache,
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output_attentions: bool = False,
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):
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if self.config._attn_implementation == "flash_attention_2":
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if attention_mask is not None and (attention_mask == 0.0).any():
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return attention_mask
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return None
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if self.config._attn_implementation == "flex_attention":
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if isinstance(attention_mask, torch.Tensor):
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attention_mask = make_flex_block_causal_mask(attention_mask)
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return attention_mask
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# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
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# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
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# to infer the attention mask.
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past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
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using_compilable_cache = past_key_values.is_compileable if past_key_values is not None else False
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# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
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if self.config._attn_implementation == "sdpa" and not using_compilable_cache and not output_attentions:
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if AttentionMaskConverter._ignore_causal_mask_sdpa(
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attention_mask,
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inputs_embeds=input_tensor,
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past_key_values_length=past_seen_tokens,
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is_training=self.training,
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):
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return None
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dtype = input_tensor.dtype
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sequence_length = input_tensor.shape[1]
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if using_compilable_cache:
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target_length = past_key_values.get_max_cache_shape()
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else:
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target_length = (
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attention_mask.shape[-1]
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if isinstance(attention_mask, torch.Tensor)
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else past_seen_tokens + sequence_length + 1
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)
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# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
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causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
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attention_mask,
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sequence_length=sequence_length,
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target_length=target_length,
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dtype=dtype,
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cache_position=cache_position,
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batch_size=input_tensor.shape[0],
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)
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if (
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self.config._attn_implementation == "sdpa"
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and attention_mask is not None
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and attention_mask.device.type in ["cuda", "xpu", "npu"]
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and not output_attentions
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):
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# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
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# using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
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# Details: https://github.com/pytorch/pytorch/issues/110213
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min_dtype = torch.finfo(dtype).min
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causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
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return causal_mask
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@staticmethod
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# Copied from transformers.models.gptj.modeling_gptj.GPTJModel._prepare_4d_causal_attention_mask_with_cache_position
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def _prepare_4d_causal_attention_mask_with_cache_position(
|
|
attention_mask: torch.Tensor,
|
|
sequence_length: int,
|
|
target_length: int,
|
|
dtype: torch.dtype,
|
|
cache_position: torch.Tensor,
|
|
batch_size: int,
|
|
**kwargs,
|
|
):
|
|
"""
|
|
Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
|
|
`(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
|
|
|
|
Args:
|
|
attention_mask (`torch.Tensor`):
|
|
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
|
|
`(batch_size, 1, query_length, key_value_length)`.
|
|
sequence_length (`int`):
|
|
The sequence length being processed.
|
|
target_length (`int`):
|
|
The target length: when generating with static cache, the mask should be as long as the static cache,
|
|
to account for the 0 padding, the part of the cache that is not filled yet.
|
|
dtype (`torch.dtype`):
|
|
The dtype to use for the 4D attention mask.
|
|
cache_position (`torch.Tensor`):
|
|
Indices depicting the position of the input sequence tokens in the sequence.
|
|
batch_size (`torch.Tensor`):
|
|
Batch size.
|
|
"""
|
|
if attention_mask is not None and attention_mask.dim() == 4:
|
|
# In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
|
|
causal_mask = attention_mask
|
|
else:
|
|
min_dtype = torch.finfo(dtype).min
|
|
causal_mask = torch.full(
|
|
(sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
|
|
)
|
|
if sequence_length != 1:
|
|
causal_mask = torch.triu(causal_mask, diagonal=1)
|
|
causal_mask *= torch.arange(target_length, device=cache_position.device) > cache_position.reshape(-1, 1)
|
|
causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
|
|
if attention_mask is not None:
|
|
causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit
|
|
mask_length = attention_mask.shape[-1]
|
|
padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
|
|
causal_mask.device
|
|
)
|
|
padding_mask = padding_mask == 0
|
|
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
|
|
padding_mask, min_dtype
|
|
)
|
|
|
|
return causal_mask
|
|
|
|
@can_return_tuple
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Cache] = 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,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> 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 (`Cache`, *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.
|
|
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
|
config.n_positions - 1]`. for padding use -1.
|
|
|
|
[What are position IDs?](../glossary#position-ids)
|
|
cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
|
|
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
|
|
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
|
|
the complete sequence length.
|
|
"""
|
|
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
|
|
|
|
if (input_ids is None) ^ (inputs_embeds is not None):
|
|
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
|
|
|
if self.gradient_checkpointing and self.training and use_cache:
|
|
logger.warning_once(
|
|
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
|
)
|
|
use_cache = False
|
|
|
|
if input_ids is not None:
|
|
input_ids = input_ids.view(-1, input_ids.shape[-1])
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
if use_cache and past_key_values is None:
|
|
past_key_values = DynamicCache()
|
|
|
|
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
if cache_position is None:
|
|
cache_position = torch.arange(
|
|
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
|
)
|
|
|
|
if attention_mask is None:
|
|
seq_length = past_seen_tokens + inputs_embeds.shape[1]
|
|
attention_mask = torch.ones(inputs_embeds.shape[0], seq_length, device=inputs_embeds.device)
|
|
|
|
causal_mask = self._update_causal_mask(
|
|
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
|
)
|
|
|
|
# embed positions
|
|
if position_ids is None:
|
|
# position_ids = cache_position.unsqueeze(0)
|
|
position_ids = torch.cumsum(attention_mask, dim=1)
|
|
position_ids = (position_ids * attention_mask - 1).long()
|
|
# cut positions if `past_seen_tokens` is > 0
|
|
position_ids = position_ids[:, past_seen_tokens:]
|
|
|
|
pos_embeds = self.embed_positions(attention_mask, past_seen_tokens, position_ids=position_ids)
|
|
|
|
if self.project_in is not None:
|
|
inputs_embeds = self.project_in(inputs_embeds)
|
|
|
|
hidden_states = inputs_embeds + pos_embeds.to(inputs_embeds.device)
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions 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://huggingface.co/papers/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
|
|
|
|
layer_outputs = decoder_layer(
|
|
hidden_states,
|
|
attention_mask=causal_mask,
|
|
position_ids=position_ids,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
past_key_value=past_key_values,
|
|
output_attentions=output_attentions,
|
|
use_cache=use_cache,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
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,)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
)
|
|
|
|
|
|
@auto_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
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = 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,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[FlashAttentionKwargs],
|
|
) -> 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,
|
|
position_ids=position_ids,
|
|
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=True,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
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, GenerationMixin):
|
|
_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 set_decoder(self, decoder):
|
|
self.model.decoder = decoder
|
|
|
|
def get_decoder(self):
|
|
return self.model.decoder
|
|
|
|
@can_return_tuple
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[Union[list[torch.FloatTensor], Cache]] = 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,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
**kwargs: Unpack[TransformersKwargs],
|
|
) -> Union[tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
|
config.vocab_size]` or -100 (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]`.
|
|
|
|
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,
|
|
position_ids=position_ids,
|
|
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=True,
|
|
cache_position=cache_position,
|
|
**kwargs,
|
|
)
|
|
|
|
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)
|
|
loss = self.loss_function(
|
|
logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
**kwargs,
|
|
)
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
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).
|
|
"""
|
|
)
|
|
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()
|
|
|
|
@auto_docstring
|
|
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[Union[list[torch.FloatTensor], Cache]] = 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,
|
|
position_ids: Optional[torch.LongTensor] = 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,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = 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 and batch_size != 1:
|
|
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
|
if self.config.pad_token_id is None:
|
|
last_non_pad_token = -1
|
|
elif input_ids is not None:
|
|
# To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
|
|
non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
|
|
token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
|
|
last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
|
|
else:
|
|
last_non_pad_token = -1
|
|
logger.warning_once(
|
|
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
|
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
|
)
|
|
|
|
pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
|
|
|
|
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
|
|
|
|
|
|
@auto_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()
|
|
|
|
@auto_docstring
|
|
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[Union[list[torch.FloatTensor], Cache]] = 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,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
) -> Union[tuple, QuestionAnsweringModelOutput]:
|
|
r"""
|
|
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,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
hidden_states = 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).to(logits.device)
|
|
end_positions = end_positions.clamp(0, ignored_index).to(logits.device)
|
|
|
|
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
|
|
|
|
|
|
__all__ = [
|
|
"OPTForCausalLM",
|
|
"OPTModel",
|
|
"OPTPreTrainedModel",
|
|
"OPTForSequenceClassification",
|
|
"OPTForQuestionAnswering",
|
|
]
|