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Fix Ernie's config and model for "from_pretrained" #520

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May 21, 2023
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Fix Ernie's config and model for "from_pretrained" #520

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1 change: 1 addition & 0 deletions mindnlp/configs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,3 +27,4 @@
MINDNLP_CONFIG_URL_BASE = "https://download.mindspore.cn/toolkits/mindnlp/models/{}/{}/config.json"
MINDNLP_MODEL_URL_BASE = "https://download.mindspore.cn/toolkits/mindnlp/models/{}/{}/mindspore.ckpt"
MINDNLP_TOKENIZER_CONFIG_URL_BASE = "https://download.mindspore.cn/toolkits/mindnlp/models/{}/{}/tokenizer.json"
MINDNLP_VOCAB_URL_BASE = "https://download.mindspore.cn/toolkits/mindnlp/models/{}/{}/vocab.txt"
102 changes: 69 additions & 33 deletions mindnlp/models/ernie/ernie.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,16 +17,24 @@
Ernie Models
"""

import re
from typing import Optional, Tuple

import mindspore
from mindspore import nn, ops, Tensor
from mindspore.common.initializer import TruncatedNormal
from mindnlp.abc import PreTrainedModel
from .ernie_config import ErnieConfig, ERNIE_PRETRAINED_INIT_CONFIGURATION, ERNIE_PRETRAINED_RESOURCE_FILES_MAP
from mindnlp.configs import MINDNLP_MODEL_URL_BASE
from .ernie_config import ErnieConfig, ERNIE_SUPPORT_LIST


__all__ = ['ErnieEmbeddings', 'ErnieModel', 'ErniePooler', "UIE"]
PRETRAINED_MODEL_ARCHIVE_MAP = {
model: MINDNLP_MODEL_URL_BASE.format(re.search(r"^[^-]*", model).group(), model)
for model in ERNIE_SUPPORT_LIST
}

__all__ = ["ErnieEmbeddings", "ErnieModel", "ErniePooler", "UIE"]


class ErnieEmbeddings(nn.Cell):
"""
Expand All @@ -37,21 +45,30 @@ def __init__(self, config: ErnieConfig, embedding_table):
super().__init__()

self.word_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id, embedding_table=embedding_table
config.vocab_size,
config.hidden_size,
padding_idx=config.pad_token_id,
embedding_table=embedding_table,
)
self.position_embeddings = nn.Embedding(
config.max_position_embeddings, config.hidden_size, embedding_table=embedding_table
config.max_position_embeddings,
config.hidden_size,
embedding_table=embedding_table,
)
self.type_vocab_size = config.type_vocab_size
if self.type_vocab_size > 0:
self.token_type_embeddings = nn.Embedding(
config.type_vocab_size, config.hidden_size, embedding_table=embedding_table
config.type_vocab_size,
config.hidden_size,
embedding_table=embedding_table,
)
self.use_task_id = config.use_task_id
self.task_id = config.task_id
if self.use_task_id:
self.task_type_embeddings = nn.Embedding(
config.task_type_vocab_size, config.hidden_size, embedding_table=embedding_table
config.task_type_vocab_size,
config.hidden_size,
embedding_table=embedding_table,
)
self.layer_norm = nn.LayerNorm([config.hidden_size])
self.dropout = nn.Dropout(config.hidden_dropout_prob, p=0.5)
Expand All @@ -65,7 +82,6 @@ def construct(
inputs_embeds: Optional[Tensor] = None,
past_key_values_length: int = 0,
):

if input_ids is not None:
inputs_embeds = self.word_embeddings(input_ids)

Expand All @@ -92,8 +108,7 @@ def construct(

if self.use_task_id:
if task_type_ids is None:
task_type_ids = ops.ones(
input_shape, mindspore.int64) * self.task_id
task_type_ids = ops.ones(input_shape, mindspore.int64) * self.task_id
task_type_embeddings = self.task_type_embeddings(task_type_ids)
embeddings = embeddings + task_type_embeddings
embeddings = self.layer_norm(embeddings)
Expand All @@ -106,22 +121,22 @@ class ErniePretrainedModel(PreTrainedModel):
Ernie Pretrained Model.
"""

pretrained_init_configuration = ERNIE_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = ERNIE_PRETRAINED_RESOURCE_FILES_MAP
config_class = ErnieConfig
pretrained_model_archive_map = PRETRAINED_MODEL_ARCHIVE_MAP

# TODO
def get_input_embeddings(self):
pass

#TODO
# TODO
def get_position_embeddings(self):
pass

#TODO
# TODO
def resize_position_embeddings(self):
pass

#TODO
# TODO
def set_input_embeddings(self):
pass

Expand Down Expand Up @@ -153,10 +168,12 @@ class ErniePooler(nn.Cell):
"""
Ernie Pooler.
"""

def __init__(self, config: ErnieConfig, weight_init):
super().__init__()
self.dense = nn.Dense(config.hidden_size,
config.hidden_size, weight_init=weight_init)
self.dense = nn.Dense(
config.hidden_size, config.hidden_size, weight_init=weight_init
)
self.activation = nn.Tanh()

def construct(self, hidden_states):
Expand All @@ -180,18 +197,17 @@ def __init__(self, config: ErnieConfig):
self.nheads = config.num_attention_heads
embedding_table = TruncatedNormal(sigma=self.initializer_range)
self.embeddings = ErnieEmbeddings(
config=config, embedding_table=embedding_table)
config=config, embedding_table=embedding_table
)
encoder_layer = nn.TransformerEncoderLayer(
config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
dropout=config.hidden_dropout_prob,
activation=config.hidden_act,
batch_first=True
)
self.encoder = nn.TransformerEncoder(
encoder_layer, config.num_hidden_layers
batch_first=True,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
self.pooler = ErniePooler(config, weight_init=embedding_table)
self.apply(self.init_weights)

Expand All @@ -217,39 +233,57 @@ def construct(
):
batch_size, seq_length = input_ids.shape

return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
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.")
"You cannot specify both input_ids and inputs_embeds at the same time."
)

# init the default bool value
output_attentions = output_attentions if output_attentions is not None else False
output_hidden_states = output_hidden_states if output_hidden_states is not None else False
output_attentions = (
output_attentions if output_attentions is not None else False
)
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else False
)
return_dict = return_dict if return_dict is not None else False
# use_cache = use_cache if use_cache is not None else False
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]

if attention_mask is None:
attention_mask = ((input_ids == self.pad_token_id).astype(
self.pooler.dense.weight.dtype) * -1e4).unsqueeze(1).unsqueeze(2)
attention_mask = (
(
(input_ids == self.pad_token_id).astype(
self.pooler.dense.weight.dtype
)
* -1e4
)
.unsqueeze(1)
.unsqueeze(2)
)

if past_key_values is not None:
batch_size = past_key_values[0][0].shape[0]
past_mask = ops.zeros(
[batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
attention_mask = ops.concat(
[past_mask, attention_mask], axis=-1)
[batch_size, 1, 1, past_key_values_length],
dtype=attention_mask.dtype,
)
attention_mask = ops.concat([past_mask, attention_mask], axis=-1)

attention_mask = ops.tile(
attention_mask, (1, self.nheads, seq_length, 1)).reshape(-1, seq_length, seq_length)
attention_mask, (1, self.nheads, seq_length, 1)
).reshape(-1, seq_length, seq_length)
# For 2D attention_mask from tokenizer
elif attention_mask.ndim == 2:
attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
attention_mask = (1.0 - attention_mask) * -1e4
attention_mask = ops.tile(
attention_mask, (1, self.nheads, seq_length, 1)).reshape(-1, seq_length, seq_length)
attention_mask, (1, self.nheads, seq_length, 1)
).reshape(-1, seq_length, seq_length)

attention_mask.stop_gradient = True

Expand Down Expand Up @@ -298,7 +332,9 @@ def construct(
inputs_embeds: Optional[Tensor] = None,
return_dict: Optional[Tensor] = None,
):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
sequence_output, _ = self.ernie(
input_ids=input_ids,
token_type_ids=token_type_ids,
Expand Down
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