707 lines
32 KiB
Python
707 lines
32 KiB
Python
# coding=utf-8
|
|
# Copyright 2021 The Fairseq Authors The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""PyTorch XGLM model."""
|
|
|
|
import math
|
|
from typing import Optional, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
|
|
from ...activations import ACT2FN
|
|
from ...cache_utils import Cache, EncoderDecoderCache
|
|
from ...generation import GenerationMixin
|
|
from ...modeling_attn_mask_utils import _prepare_4d_attention_mask, _prepare_4d_causal_attention_mask
|
|
from ...modeling_layers import GradientCheckpointingLayer
|
|
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...utils import auto_docstring, logging
|
|
from .configuration_xglm import XGLMConfig
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
# Copied from transformers.models.bart.modeling_bart.BartScaledWordEmbedding with Bart->XGLM
|
|
class XGLMScaledWordEmbedding(nn.Embedding):
|
|
"""
|
|
This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
|
|
"""
|
|
|
|
def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: Optional[float] = 1.0):
|
|
super().__init__(num_embeddings, embedding_dim, padding_idx)
|
|
self.embed_scale = embed_scale
|
|
|
|
def forward(self, input_ids: torch.Tensor):
|
|
return super().forward(input_ids) * self.embed_scale
|
|
|
|
|
|
class XGLMSinusoidalPositionalEmbedding(nn.Module):
|
|
"""This module produces sinusoidal positional embeddings of any length."""
|
|
|
|
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None):
|
|
super().__init__()
|
|
self.offset = 2
|
|
self.embedding_dim = embedding_dim
|
|
self.padding_idx = padding_idx
|
|
self.make_weights(num_positions + self.offset, embedding_dim, padding_idx)
|
|
|
|
def make_weights(self, num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
|
|
emb_weights = self.get_embedding(num_embeddings, embedding_dim, padding_idx)
|
|
if hasattr(self, "weights"):
|
|
# in forward put the weights on the correct dtype and device of the param
|
|
emb_weights = emb_weights.to(dtype=self.weights.dtype, device=self.weights.device)
|
|
|
|
self.register_buffer("weights", emb_weights, persistent=False)
|
|
|
|
@staticmethod
|
|
def get_embedding(num_embeddings: int, embedding_dim: int, padding_idx: Optional[int] = None):
|
|
"""
|
|
Build sinusoidal embeddings.
|
|
|
|
This matches the implementation in tensor2tensor, but differs slightly from the description in Section 3.5 of
|
|
"Attention Is All You Need".
|
|
"""
|
|
half_dim = embedding_dim // 2
|
|
emb = math.log(10000) / (half_dim - 1)
|
|
emb = torch.exp(torch.arange(half_dim, dtype=torch.int64).float() * -emb)
|
|
emb = torch.arange(num_embeddings, dtype=torch.int64).float().unsqueeze(1) * emb.unsqueeze(0)
|
|
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
|
|
if embedding_dim % 2 == 1:
|
|
# zero pad
|
|
emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
|
|
if padding_idx is not None:
|
|
emb[padding_idx, :] = 0
|
|
|
|
return emb.to(torch.get_default_dtype())
|
|
|
|
@torch.no_grad()
|
|
def forward(self, position_ids: Optional[torch.Tensor] = None, past_key_values_length: int = 0):
|
|
bsz, seq_len = position_ids.size()
|
|
position_ids += self.offset
|
|
|
|
# Expand embeddings if needed. `position_ids.max()` is NOT used to keep torch.fx compatibility.
|
|
max_pos = 2 + seq_len + past_key_values_length
|
|
if max_pos > self.weights.size(0):
|
|
self.make_weights(max_pos, self.embedding_dim, self.padding_idx)
|
|
|
|
return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach()
|
|
|
|
|
|
class XGLMAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(
|
|
self,
|
|
embed_dim: int,
|
|
num_heads: int,
|
|
dropout: Optional[float] = 0.0,
|
|
is_decoder: Optional[bool] = False,
|
|
bias: Optional[bool] = True,
|
|
layer_idx: Optional[bool] = None,
|
|
):
|
|
super().__init__()
|
|
self.embed_dim = embed_dim
|
|
self.num_heads = num_heads
|
|
self.dropout = dropout
|
|
self.head_dim = embed_dim // num_heads
|
|
|
|
if (self.head_dim * num_heads) != self.embed_dim:
|
|
raise ValueError(
|
|
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
|
|
f" and `num_heads`: {num_heads})."
|
|
)
|
|
self.scaling = self.head_dim**-0.5
|
|
self.is_decoder = is_decoder
|
|
self.layer_idx = layer_idx
|
|
|
|
self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
|
self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
|
self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
|
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
key_value_states: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
layer_head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
|
|
"""Input shape: Batch x Time x Channel"""
|
|
|
|
# if key_value_states are provided this layer is used as a cross-attention layer
|
|
# for the decoder
|
|
is_cross_attention = key_value_states is not None
|
|
|
|
bsz, tgt_len, _ = hidden_states.size()
|
|
src_len = key_value_states.shape[1] if is_cross_attention else tgt_len
|
|
|
|
# get query proj
|
|
query_states = self.q_proj(hidden_states) * self.scaling
|
|
|
|
if past_key_value is not None:
|
|
if isinstance(past_key_value, EncoderDecoderCache):
|
|
is_updated = past_key_value.is_updated.get(self.layer_idx)
|
|
if is_cross_attention:
|
|
# after the first generated id, we can subsequently re-use all key/value_states from cache
|
|
curr_past_key_value = past_key_value.cross_attention_cache
|
|
else:
|
|
curr_past_key_value = past_key_value.self_attention_cache
|
|
else:
|
|
curr_past_key_value = past_key_value
|
|
|
|
current_states = key_value_states if is_cross_attention else hidden_states
|
|
if is_cross_attention and past_key_value is not None and is_updated:
|
|
# reuse k,v, cross_attentions
|
|
key_states = curr_past_key_value.key_cache[self.layer_idx]
|
|
value_states = curr_past_key_value.value_cache[self.layer_idx]
|
|
else:
|
|
key_states = self.k_proj(current_states)
|
|
value_states = self.v_proj(current_states)
|
|
key_states = key_states.view(bsz, src_len, -1, self.head_dim).transpose(1, 2)
|
|
value_states = value_states.view(bsz, src_len, -1, self.head_dim).transpose(1, 2)
|
|
|
|
if past_key_value is not None:
|
|
# save all key/value_states to cache to be re-used for fast auto-regressive generation
|
|
cache_position = cache_position if not is_cross_attention else None
|
|
key_states, value_states = curr_past_key_value.update(
|
|
key_states, value_states, self.layer_idx, {"cache_position": cache_position}
|
|
)
|
|
# set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
|
|
if is_cross_attention:
|
|
past_key_value.is_updated[self.layer_idx] = True
|
|
|
|
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
|
query_states = query_states.view(bsz, tgt_len, self.num_heads, self.head_dim).transpose(1, 2)
|
|
query_states = query_states.reshape(*proj_shape)
|
|
key_states = key_states.reshape(*proj_shape)
|
|
value_states = value_states.reshape(*proj_shape)
|
|
|
|
src_len = key_states.size(1)
|
|
attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))
|
|
|
|
if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
|
|
raise ValueError(
|
|
f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is"
|
|
f" {attn_weights.size()}"
|
|
)
|
|
|
|
if attention_mask is not None:
|
|
if attention_mask.size() != (bsz, 1, tgt_len, src_len):
|
|
raise ValueError(
|
|
f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
|
|
)
|
|
attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
|
|
attn_weights = torch.max(
|
|
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
|
|
)
|
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
# upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
|
|
if attn_weights.dtype == torch.float16:
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
|
|
else:
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
|
|
if layer_head_mask is not None:
|
|
if layer_head_mask.size() != (self.num_heads,):
|
|
raise ValueError(
|
|
f"Head mask for a single layer should be of size {(self.num_heads,)}, but is"
|
|
f" {layer_head_mask.size()}"
|
|
)
|
|
attn_weights = layer_head_mask.view(1, -1, 1, 1) * attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
if output_attentions:
|
|
# this operation is a bit awkward, but it's required to
|
|
# make sure that attn_weights keeps its gradient.
|
|
# In order to do so, attn_weights have to be reshaped
|
|
# twice and have to be reused in the following
|
|
attn_weights_reshaped = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
|
attn_weights = attn_weights_reshaped.view(bsz * self.num_heads, tgt_len, src_len)
|
|
else:
|
|
attn_weights_reshaped = None
|
|
|
|
attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
|
|
|
|
attn_output = torch.bmm(attn_probs, value_states)
|
|
|
|
if attn_output.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
|
|
raise ValueError(
|
|
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
|
|
f" {attn_output.size()}"
|
|
)
|
|
|
|
attn_output = attn_output.view(bsz, self.num_heads, tgt_len, self.head_dim)
|
|
attn_output = attn_output.transpose(1, 2)
|
|
|
|
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
|
|
# partitioned aross GPUs when using tensor-parallelism.
|
|
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
|
|
|
|
attn_output = self.out_proj(attn_output)
|
|
|
|
return attn_output, attn_weights_reshaped
|
|
|
|
|
|
class XGLMDecoderLayer(GradientCheckpointingLayer):
|
|
def __init__(self, config: XGLMConfig, layer_idx=None):
|
|
super().__init__()
|
|
self.embed_dim = config.d_model
|
|
|
|
self.self_attn = XGLMAttention(
|
|
embed_dim=self.embed_dim,
|
|
num_heads=config.attention_heads,
|
|
dropout=config.attention_dropout,
|
|
is_decoder=True,
|
|
layer_idx=layer_idx,
|
|
)
|
|
self.dropout = config.dropout
|
|
self.activation_fn = ACT2FN[config.activation_function]
|
|
self.activation_dropout = config.activation_dropout
|
|
|
|
if config.add_cross_attention:
|
|
self.encoder_attn = XGLMAttention(
|
|
embed_dim=self.embed_dim,
|
|
num_heads=config.attention_heads,
|
|
dropout=config.attention_dropout,
|
|
is_decoder=True,
|
|
layer_idx=layer_idx,
|
|
)
|
|
self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
|
self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
self.fc1 = nn.Linear(self.embed_dim, config.ffn_dim)
|
|
self.fc2 = nn.Linear(config.ffn_dim, self.embed_dim)
|
|
self.final_layer_norm = nn.LayerNorm(self.embed_dim)
|
|
|
|
# Copied from transformers.models.musicgen.modeling_musicgen.MusicgenDecoderLayer.forward
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
layer_head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_layer_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_value: Optional[Cache] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
use_cache: Optional[bool] = True,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> torch.Tensor:
|
|
"""
|
|
Args:
|
|
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
attention_mask (`torch.FloatTensor`): attention mask of size
|
|
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
|
encoder_hidden_states (`torch.FloatTensor`):
|
|
cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
|
|
encoder_attention_mask (`torch.FloatTensor`): encoder 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`): mask for attention heads in a given layer of size
|
|
`(encoder_attention_heads,)`.
|
|
cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of
|
|
size `(decoder_attention_heads,)`.
|
|
past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
|
returned tensors for more detail.
|
|
"""
|
|
residual = hidden_states
|
|
hidden_states = self.self_attn_layer_norm(hidden_states)
|
|
|
|
# Self Attention
|
|
hidden_states, self_attn_weights = 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,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Cross-Attention Block
|
|
cross_attn_weights = None
|
|
if encoder_hidden_states is not None:
|
|
residual = hidden_states
|
|
hidden_states = self.encoder_attn_layer_norm(hidden_states)
|
|
|
|
hidden_states, cross_attn_weights = self.encoder_attn(
|
|
hidden_states=hidden_states,
|
|
key_value_states=encoder_hidden_states,
|
|
attention_mask=encoder_attention_mask,
|
|
layer_head_mask=cross_attn_layer_head_mask,
|
|
past_key_value=past_key_value,
|
|
output_attentions=output_attentions,
|
|
cache_position=cache_position,
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
# Fully Connected
|
|
residual = hidden_states
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
hidden_states = self.activation_fn(self.fc1(hidden_states))
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
|
|
hidden_states = self.fc2(hidden_states)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (self_attn_weights, cross_attn_weights)
|
|
|
|
return outputs
|
|
|
|
|
|
@auto_docstring
|
|
class XGLMPreTrainedModel(PreTrainedModel):
|
|
config: XGLMConfig
|
|
base_model_prefix = "model"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["XGLMDecoderLayer"]
|
|
|
|
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_()
|
|
|
|
|
|
@auto_docstring
|
|
class XGLMModel(XGLMPreTrainedModel):
|
|
def __init__(self, config: XGLMConfig, embed_tokens: Optional[nn.Embedding] = None):
|
|
r"""
|
|
embed_tokens (`nn.Embedding`, *optional*):
|
|
output embeddings
|
|
"""
|
|
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
|
|
embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
|
|
|
|
if embed_tokens is not None:
|
|
self.embed_tokens = embed_tokens
|
|
else:
|
|
self.embed_tokens = XGLMScaledWordEmbedding(
|
|
config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
|
|
)
|
|
|
|
self.embed_positions = XGLMSinusoidalPositionalEmbedding(
|
|
config.max_position_embeddings,
|
|
config.d_model,
|
|
config.pad_token_id,
|
|
)
|
|
self.layers = nn.ModuleList([XGLMDecoderLayer(config, layer_idx=i) for i in range(config.num_layers)])
|
|
self.layer_norm = nn.LayerNorm(config.d_model)
|
|
|
|
self.gradient_checkpointing = False
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
) -> Union[tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
|
|
r"""
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
|
|
the decoder.
|
|
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
|
|
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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)
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
"""
|
|
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 input_ids and inputs_embeds at the same time")
|
|
elif input_ids is not None:
|
|
self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
|
|
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 input_ids or inputs_embeds")
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_tokens(input_ids)
|
|
|
|
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
|
|
|
|
# initialize `past_key_values`
|
|
return_legacy_cache = False
|
|
if use_cache and not isinstance(past_key_values, Cache):
|
|
return_legacy_cache = True
|
|
logger.warning_once(
|
|
"Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. "
|
|
"You should pass an instance of `EncoderDecoderCache` instead, e.g. "
|
|
"`past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`."
|
|
)
|
|
past_key_values = EncoderDecoderCache.from_legacy_cache(past_key_values)
|
|
|
|
past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
|
|
attention_mask = _prepare_4d_causal_attention_mask(
|
|
attention_mask, input_shape, inputs_embeds, past_key_values_length
|
|
)
|
|
|
|
if position_ids is None:
|
|
position_ids = torch.arange(
|
|
past_key_values_length,
|
|
input_shape[-1] + past_key_values_length,
|
|
dtype=torch.long,
|
|
device=input_ids.device if input_ids is not None else inputs_embeds.device,
|
|
)
|
|
position_ids = position_ids.unsqueeze(0)
|
|
|
|
# expand encoder attention mask
|
|
if encoder_hidden_states is not None and encoder_attention_mask is not None:
|
|
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
|
encoder_attention_mask = _prepare_4d_attention_mask(
|
|
encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
|
|
)
|
|
|
|
hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length).to(
|
|
inputs_embeds.device
|
|
)
|
|
hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training)
|
|
|
|
# decoder layers
|
|
all_hidden_states = () if output_hidden_states else None
|
|
all_self_attns = () if output_attentions else None
|
|
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
|
|
|
|
# check if head_mask/cross_attn_head_mask has a correct number of layers specified if desired
|
|
for attn_mask, mask_name in zip([head_mask, cross_attn_head_mask], ["head_mask", "cross_attn_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,
|
|
encoder_hidden_states, # as a positional argument for gradient checkpointing
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
layer_head_mask=(head_mask[idx] if head_mask is not None else None),
|
|
cross_attn_layer_head_mask=(cross_attn_head_mask[idx] if cross_attn_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,
|
|
)
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_self_attns += (layer_outputs[1],)
|
|
|
|
if encoder_hidden_states is not None:
|
|
all_cross_attentions += (layer_outputs[2],)
|
|
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
|
|
# add hidden states from the last decoder layer
|
|
if output_hidden_states:
|
|
all_hidden_states += (hidden_states,)
|
|
|
|
if return_legacy_cache:
|
|
past_key_values = past_key_values.to_legacy_cache()
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, past_key_values, all_hidden_states, all_self_attns, all_cross_attentions]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutputWithPastAndCrossAttentions(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=past_key_values,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_self_attns,
|
|
cross_attentions=all_cross_attentions,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
The XGLM Model transformer with a language modeling head on top (linear layer with weights tied to the input
|
|
embeddings).
|
|
"""
|
|
)
|
|
class XGLMForCausalLM(XGLMPreTrainedModel, GenerationMixin):
|
|
base_model_prefix = "model"
|
|
_tied_weights_keys = ["lm_head.weight"]
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.model = XGLMModel(config)
|
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.Tensor] = None,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
position_ids: Optional[torch.Tensor] = None,
|
|
encoder_hidden_states: Optional[torch.Tensor] = None,
|
|
encoder_attention_mask: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
cross_attn_head_mask: Optional[torch.Tensor] = None,
|
|
past_key_values: Optional[list[torch.FloatTensor]] = None,
|
|
inputs_embeds: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
cache_position: Optional[torch.Tensor] = None,
|
|
**kwargs,
|
|
) -> Union[tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
|
r"""
|
|
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
|
|
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of
|
|
the decoder.
|
|
encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
|
|
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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)
|
|
cross_attn_head_mask (`torch.Tensor` of shape `(num_layers, attention_heads)`, *optional*):
|
|
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
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]`.
|
|
"""
|
|
|
|
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(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
encoder_attention_mask=encoder_attention_mask,
|
|
head_mask=head_mask,
|
|
cross_attn_head_mask=cross_attn_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,
|
|
cache_position=cache_position,
|
|
)
|
|
|
|
logits = self.lm_head(outputs[0])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss = self.loss_function(
|
|
logits,
|
|
labels,
|
|
vocab_size=self.config.vocab_size,
|
|
pad_token_id=self.config.pad_token_id,
|
|
**kwargs,
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[1:]
|
|
return (loss,) + output if loss is not None else output
|
|
|
|
return CausalLMOutputWithCrossAttentions(
|
|
loss=loss,
|
|
logits=logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
cross_attentions=outputs.cross_attentions,
|
|
)
|
|
|
|
|
|
__all__ = ["XGLMForCausalLM", "XGLMModel", "XGLMPreTrainedModel"]
|