mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-04-21 14:52:20 +00:00
# What does this PR do? <!-- Congratulations! You've made it this far! You're not quite done yet though. Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution. Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change. Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost. --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests), Pull Request section? - [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? Here are the [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). - [ ] Did you write any new necessary tests? ## Who can review? Anyone in the community is free to review the PR once the tests have passed. Feel free to tag members/contributors who may be interested in your PR. <!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @ @OlivierDehaene OR @Narsil -->
532 lines
21 KiB
Python
532 lines
21 KiB
Python
# coding=utf-8
|
|
# Copyright 2021 The OpenAI Team Authors and The HuggingFace 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 IdeficsVision model: a copy of CLIPVisionModel using a simpler config object"""
|
|
|
|
|
|
from dataclasses import dataclass
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
|
|
from transformers.activations import ACT2FN
|
|
from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
|
|
from transformers.utils import (
|
|
ModelOutput,
|
|
logging,
|
|
)
|
|
from text_generation_server.layers import (
|
|
TensorParallelColumnLinear,
|
|
TensorParallelRowLinear,
|
|
TensorParallelEmbedding,
|
|
)
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
@dataclass
|
|
class IdeficsVisionModelOutput(ModelOutput):
|
|
"""
|
|
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
|
|
|
|
Args:
|
|
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
|
|
The image embeddings obtained by applying the projection layer to the pooler_output.
|
|
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
Sequence of hidden-states at the output of the last layer of the model.
|
|
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
|
|
Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
|
|
one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
|
|
|
|
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
|
|
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
|
|
Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
|
|
sequence_length)`.
|
|
|
|
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
|
|
heads.
|
|
"""
|
|
|
|
image_embeds: Optional[torch.FloatTensor] = None
|
|
last_hidden_state: torch.FloatTensor = None
|
|
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
|
|
attentions: Optional[Tuple[torch.FloatTensor]] = None
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPVisionEmbeddings with CLIP->Idefics
|
|
class IdeficsVisionEmbeddings(nn.Module):
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.image_size = config.image_size
|
|
self.patch_size = config.patch_size
|
|
|
|
self.class_embedding = nn.Parameter(
|
|
weights.get_tensor(f"{prefix}.class_embedding")
|
|
)
|
|
|
|
self.patch_embedding = nn.Conv2d.load_no_bias(
|
|
prefix=f"{prefix}.patch_embedding",
|
|
weights=weights,
|
|
in_channels=config.num_channels,
|
|
out_channels=self.embed_dim,
|
|
kernel_size=self.patch_size,
|
|
stride=self.patch_size,
|
|
)
|
|
|
|
self.num_patches = (self.image_size // self.patch_size) ** 2
|
|
self.num_positions = self.num_patches + 1
|
|
self.position_embedding = TensorParallelEmbedding(
|
|
prefix="model.vision_model.embeddings.position_embedding", weights=weights
|
|
)
|
|
self.position_ids = (
|
|
torch.arange(self.num_positions).expand((1, -1)).to(device=weights.device)
|
|
)
|
|
|
|
def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
|
|
batch_size = pixel_values.shape[0]
|
|
target_dtype = self.patch_embedding.weight.dtype
|
|
patch_embeds = self.patch_embedding(
|
|
pixel_values.to(dtype=target_dtype)
|
|
) # shape = [*, width, grid, grid]
|
|
patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
|
|
|
|
class_embeds = self.class_embedding.expand(batch_size, 1, -1)
|
|
embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
|
|
embeddings = embeddings + self.position_embedding(self.position_ids)
|
|
return embeddings
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPAttention with CLIP->IdeficsVision
|
|
class IdeficsVisionAttention(nn.Module):
|
|
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
|
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.config = config
|
|
self.embed_dim = config.hidden_size
|
|
self.num_heads = config.num_attention_heads
|
|
self.head_dim = self.embed_dim // self.num_heads
|
|
if self.head_dim * self.num_heads != self.embed_dim:
|
|
raise ValueError(
|
|
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
|
|
f" {self.num_heads})."
|
|
)
|
|
self.scale = self.head_dim**-0.5
|
|
self.dropout = config.attention_dropout
|
|
|
|
process_group = weights.process_group
|
|
if self.num_heads % weights.process_group.size() != 0:
|
|
raise ValueError(
|
|
f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
|
|
f"and `num_shards`: {weights.process_group.size()}"
|
|
)
|
|
self.num_heads = self.num_heads // weights.process_group.size()
|
|
self.embed_dim = self.embed_dim // weights.process_group.size()
|
|
|
|
self.k_proj = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.k_proj", weights=weights, bias=True
|
|
)
|
|
self.v_proj = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.v_proj", weights=weights, bias=True
|
|
)
|
|
self.q_proj = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.q_proj", weights=weights, bias=True
|
|
)
|
|
self.out_proj = TensorParallelRowLinear.load(
|
|
config, prefix=f"{prefix}.out_proj", weights=weights, bias=True
|
|
)
|
|
|
|
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
|
return (
|
|
tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
|
|
.transpose(1, 2)
|
|
.contiguous()
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
causal_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
|
"""Input shape: Batch x Time x Channel"""
|
|
|
|
bsz, tgt_len, _ = hidden_states.size()
|
|
|
|
# get query proj
|
|
query_states = self.q_proj(hidden_states) * self.scale
|
|
key_states = self._shape(self.k_proj(hidden_states), -1, bsz)
|
|
value_states = self._shape(self.v_proj(hidden_states), -1, bsz)
|
|
|
|
proj_shape = (bsz * self.num_heads, -1, self.head_dim)
|
|
query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
|
|
key_states = key_states.view(*proj_shape)
|
|
value_states = value_states.view(*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()}"
|
|
)
|
|
|
|
# apply the causal_attention_mask first
|
|
if causal_attention_mask is not None:
|
|
if causal_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"
|
|
f" {causal_attention_mask.size()}"
|
|
)
|
|
attn_weights = (
|
|
attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
|
|
+ causal_attention_mask
|
|
)
|
|
attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
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 = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
|
|
|
|
attn_weights = nn.functional.softmax(attn_weights, dim=-1)
|
|
|
|
if output_attentions:
|
|
# this operation is a bit akward, but it's required to
|
|
# make sure that attn_weights keeps its gradient.
|
|
# In order to do so, attn_weights have to 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)
|
|
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
|
|
|
|
attn_output = self.out_proj(attn_output)
|
|
|
|
return attn_output, attn_weights_reshaped
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->IdeficsVision
|
|
class IdeficsVisionMLP(nn.Module):
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.config = config
|
|
self.activation_fn = ACT2FN[config.hidden_act]
|
|
self.fc1 = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.fc1", weights=weights, bias=True
|
|
)
|
|
self.fc2 = TensorParallelRowLinear.load(
|
|
config, prefix=f"{prefix}.fc2", weights=weights, bias=True
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.fc1(hidden_states)
|
|
hidden_states = self.activation_fn(hidden_states)
|
|
hidden_states = self.fc2(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->IdeficsVision
|
|
class IdeficsVisionEncoderLayer(nn.Module):
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.embed_dim = config.hidden_size
|
|
self.self_attn = IdeficsVisionAttention(
|
|
prefix=f"{prefix}.self_attn", config=config, weights=weights
|
|
)
|
|
self.layer_norm1 = nn.LayerNorm.load(
|
|
prefix=f"{prefix}.layer_norm1", weights=weights, eps=config.layer_norm_eps
|
|
)
|
|
self.mlp = IdeficsVisionMLP(
|
|
prefix=f"{prefix}.mlp", config=config, weights=weights
|
|
)
|
|
self.layer_norm2 = nn.LayerNorm.load(
|
|
prefix=f"{prefix}.layer_norm2", weights=weights, eps=config.layer_norm_eps
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
causal_attention_mask: torch.Tensor,
|
|
output_attentions: Optional[bool] = False,
|
|
) -> Tuple[torch.FloatTensor]:
|
|
"""
|
|
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.
|
|
`(config.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.
|
|
"""
|
|
residual = hidden_states
|
|
|
|
hidden_states = self.layer_norm1(hidden_states)
|
|
hidden_states, attn_weights = self.self_attn(
|
|
hidden_states=hidden_states,
|
|
attention_mask=attention_mask,
|
|
causal_attention_mask=causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = residual + hidden_states
|
|
|
|
residual = hidden_states
|
|
hidden_states = self.layer_norm2(hidden_states)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = residual + hidden_states
|
|
|
|
outputs = (hidden_states,)
|
|
|
|
if output_attentions:
|
|
outputs += (attn_weights,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->IdeficsVision
|
|
class IdeficsVisionEncoder(nn.Module):
|
|
"""
|
|
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
|
|
[`IdeficsVisionEncoderLayer`].
|
|
|
|
Args:
|
|
config: IdeficsVisionConfig
|
|
"""
|
|
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.config = config
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
IdeficsVisionEncoderLayer(
|
|
prefix=f"{prefix}.encoder.layers.{layer_id}",
|
|
config=config,
|
|
weights=weights,
|
|
)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
# self.gradient_checkpointing = False
|
|
|
|
def forward(
|
|
self,
|
|
inputs_embeds,
|
|
attention_mask: Optional[torch.Tensor] = None,
|
|
causal_attention_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutput]:
|
|
r"""
|
|
Args:
|
|
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
|
|
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.
|
|
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)
|
|
causal_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Causal mask for the text model. 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)
|
|
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
|
|
)
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
|
|
encoder_states = () if output_hidden_states else None
|
|
all_attentions = () if output_attentions else None
|
|
|
|
hidden_states = inputs_embeds
|
|
for idx, encoder_layer in enumerate(self.layers):
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
# if self.gradient_checkpointing and self.training:
|
|
|
|
# def create_custom_forward(module):
|
|
# def custom_forward(*inputs):
|
|
# return module(*inputs, output_attentions)
|
|
|
|
# return custom_forward
|
|
|
|
# layer_outputs = torch.utils.checkpoint.checkpoint(
|
|
# create_custom_forward(encoder_layer),
|
|
# hidden_states,
|
|
# attention_mask,
|
|
# causal_attention_mask,
|
|
# )
|
|
# else:
|
|
layer_outputs = encoder_layer(
|
|
hidden_states,
|
|
attention_mask,
|
|
causal_attention_mask,
|
|
output_attentions=output_attentions,
|
|
)
|
|
|
|
hidden_states = layer_outputs[0]
|
|
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (layer_outputs[1],)
|
|
|
|
if output_hidden_states:
|
|
encoder_states = encoder_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, encoder_states, all_attentions]
|
|
if v is not None
|
|
)
|
|
return BaseModelOutput(
|
|
last_hidden_state=hidden_states,
|
|
hidden_states=encoder_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
# Adapted from transformers.models.clip.modeling_clip.CLIPVisionTransformer
|
|
class IdeficsVisionTransformer(nn.Module):
|
|
def __init__(self, prefix, config, weights):
|
|
super().__init__()
|
|
self.config = config
|
|
embed_dim = config.hidden_size
|
|
|
|
self.embeddings = IdeficsVisionEmbeddings(
|
|
prefix=f"{prefix}.embeddings", config=config, weights=weights
|
|
)
|
|
self.pre_layrnorm = nn.LayerNorm.load(
|
|
prefix=f"{prefix}.pre_layrnorm", weights=weights, eps=config.layer_norm_eps
|
|
)
|
|
self.encoder = IdeficsVisionEncoder(
|
|
prefix=prefix, config=config, weights=weights
|
|
)
|
|
self.post_layernorm = nn.LayerNorm.load(
|
|
prefix=f"{prefix}.post_layernorm",
|
|
weights=weights,
|
|
eps=config.layer_norm_eps,
|
|
)
|
|
|
|
# copied from transformers.models.clip.modeling_clip.CLIPVisionTransformer.forward
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPooling]:
|
|
r"""
|
|
Returns:
|
|
|
|
"""
|
|
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
|
|
)
|
|
|
|
if pixel_values is None:
|
|
raise ValueError("You have to specify pixel_values")
|
|
|
|
hidden_states = self.embeddings(pixel_values)
|
|
hidden_states = self.pre_layrnorm(hidden_states)
|
|
|
|
encoder_outputs = self.encoder(
|
|
inputs_embeds=hidden_states,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = last_hidden_state[:, 0, :]
|
|
pooled_output = self.post_layernorm(pooled_output)
|
|
|
|
if not return_dict:
|
|
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|