# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
import math
import numpy as np
from ..._common import default_net
from ...functional import (ACT2FN, bert_attention, cast, concat, constant,
expand, expand_mask, matmul, select, shape, slice,
softmax, split, unsqueeze)
from ...layers import MLP, ColumnLinear, Embedding, LayerNorm, Linear, RowLinear
from ...mapping import Mapping
from ...module import Module, ModuleList
class BertEmbedding(Module):
def __init__(self,
vocab_size,
hidden_size,
max_position_embeddings,
type_vocab_size,
dtype=None):
super().__init__()
self.vocab_embedding = Embedding(vocab_size, hidden_size, dtype=dtype)
self.position_embedding = Embedding(max_position_embeddings,
hidden_size,
dtype=dtype)
self.token_embedding = Embedding(type_vocab_size,
hidden_size,
dtype=dtype)
self.max_position_embeddings = max_position_embeddings
self.embedding_ln = LayerNorm(normalized_shape=hidden_size, dtype=dtype)
def forward(self, input_ids, position_ids, token_type_ids):
x = self.vocab_embedding(input_ids)
x = x + self.position_embedding(position_ids)
x = x + self.token_embedding(token_type_ids)
x = self.embedding_ln(x)
return x
class BertAttention(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings,
dtype=None,
tp_group=None,
tp_size=1):
super().__init__()
self.attention_head_size = hidden_size // num_attention_heads
self.num_attention_heads = num_attention_heads // tp_size
self.hidden_size = hidden_size // tp_size
self.max_position_embeddings = max_position_embeddings
self.norm_factor = math.sqrt(self.attention_head_size)
self.qkv = ColumnLinear(hidden_size,
hidden_size * 3,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size,
gather_output=False)
self.dense = RowLinear(hidden_size,
hidden_size,
dtype=dtype,
tp_group=tp_group,
tp_size=tp_size)
def forward(self, hidden_states, attention_mask=None, input_lengths=None):
qkv = self.qkv(hidden_states)
# attention
if default_net().plugin_config.bert_attention_plugin:
assert input_lengths is not None
context = bert_attention(qkv, input_lengths,
self.num_attention_heads,
self.attention_head_size, 1.0)
else:
def transpose_for_scores(x):
new_x_shape = concat([
shape(x, 0),
shape(x, 1), self.num_attention_heads,
self.attention_head_size
])
return x.view(new_x_shape).permute([0, 2, 1, 3])
query, key, value = split(qkv, self.hidden_size, dim=2)
query = transpose_for_scores(query)
key = transpose_for_scores(key)
value = transpose_for_scores(value)
key = key.permute([0, 1, 3, 2])
attention_scores = matmul(query, key)
attention_scores = attention_scores / self.norm_factor
if attention_mask is not None:
attention_mask = cast(attention_mask, attention_scores.dtype)
attention_scores = attention_scores + attention_mask
attention_probs = softmax(attention_scores, dim=-1)
context = matmul(attention_probs, value,
use_fp32_acc=False).permute([0, 2, 1, 3])
context = context.view(
concat([shape(context, 0),
shape(context, 1), self.hidden_size]))
context = self.dense(context)
return context
class BertEncoderLayer(Module):
def __init__(self,
hidden_size,
num_attention_heads,
max_position_embeddings,
hidden_act='relu',
tp_group=None,
tp_size=1,
dtype=None):
super().__init__()
self.input_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
self.attention = BertAttention(hidden_size,
num_attention_heads,
max_position_embeddings,
tp_group=tp_group,
tp_size=tp_size,
dtype=dtype)
self.mlp = MLP(hidden_size=hidden_size,
ffn_hidden_size=hidden_size * 4,
hidden_act=hidden_act,
tp_group=tp_group,
tp_size=tp_size,
dtype=dtype)
self.post_layernorm = LayerNorm(normalized_shape=hidden_size,
dtype=dtype)
def forward(self, hidden_states, attention_mask=None, input_lengths=None):
residual = hidden_states
attention_output = self.attention(hidden_states,
attention_mask=attention_mask,
input_lengths=input_lengths)
hidden_states = residual + attention_output
hidden_states = self.input_layernorm(hidden_states)
residual = hidden_states
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
hidden_states = self.post_layernorm(hidden_states)
return hidden_states
[docs]
class BertModel(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
type_vocab_size,
pad_token_id=None,
is_roberta=False,
mapping=Mapping(),
dtype=None):
super().__init__()
self.max_position_embeddings = max_position_embeddings
self.padding_idx = pad_token_id
self.is_roberta = is_roberta
self.embedding = BertEmbedding(
vocab_size=vocab_size,
hidden_size=hidden_size,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
dtype=dtype)
self.layers = ModuleList([
BertEncoderLayer(hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=max_position_embeddings,
hidden_act=hidden_act,
tp_group=mapping.tp_group,
tp_size=mapping.tp_size,
dtype=dtype) for _ in range(num_layers)
])
[docs]
def forward(self,
input_ids=None,
input_lengths=None,
position_ids=None,
token_type_ids=None,
hidden_states=None):
seq_len_2d = concat([1, shape(input_ids, 1)])
# create position ids
position_ids_buffer = constant(
np.expand_dims(
np.arange(self.max_position_embeddings).astype(np.int32), 0))
tmp_position_ids = slice(position_ids_buffer,
starts=[0, 0],
sizes=seq_len_2d)
tmp_position_ids = expand(tmp_position_ids, shape(input_ids)) #BxL
tmp_input_lengths = unsqueeze(input_lengths, 1) #Bx1
tmp_input_lengths = expand(tmp_input_lengths, shape(input_ids)) #BxL
mask = tmp_position_ids < tmp_input_lengths # BxL
mask = mask.cast('int32')
if position_ids is None:
if self.is_roberta:
# see create_position_ids_from_input_ids() in https://github.com/huggingface/transformers/blob/main/src/transformers/models/roberta/modeling_roberta.py
position_ids = (tmp_position_ids + 1) * mask
position_ids = position_ids + self.padding_idx
else:
position_ids = slice(position_ids_buffer,
starts=[0, 0],
sizes=seq_len_2d)
position_ids = expand(position_ids, shape(input_ids))
# create extended_attention_mask as https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py
extended_attention_mask = expand_mask(mask, tgt_len=1) # BxL -> Bx1x1xL
# create token_type_ids
if token_type_ids is None:
token_type_ids_buffer = constant(
np.expand_dims(
np.zeros(self.max_position_embeddings).astype(np.int32), 0))
token_type_ids = slice(token_type_ids_buffer,
starts=[0, 0],
sizes=seq_len_2d)
token_type_ids = expand(token_type_ids, shape(input_ids))
hidden_states = self.embedding(input_ids, position_ids, token_type_ids)
for layer in self.layers:
hidden_states = layer(hidden_states=hidden_states,
input_lengths=input_lengths,
attention_mask=extended_attention_mask)
return hidden_states
[docs]
class BertForQuestionAnswering(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
type_vocab_size,
pad_token_id=None,
is_roberta=False,
num_labels=2,
mapping=Mapping(),
dtype=None):
super().__init__()
self.bert = BertModel(num_layers=num_layers,
num_heads=num_heads,
hidden_size=hidden_size,
vocab_size=vocab_size,
hidden_act=hidden_act,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
pad_token_id=pad_token_id,
is_roberta=is_roberta,
mapping=mapping,
dtype=dtype)
self.num_labels = num_labels
self.qa_outputs = Linear(hidden_size, num_labels, dtype=dtype)
[docs]
def forward(self,
input_ids=None,
input_lengths=None,
token_type_ids=None,
position_ids=None,
hidden_states=None):
hidden_states = self.bert.forward(input_ids=input_ids,
input_lengths=input_lengths,
token_type_ids=token_type_ids,
position_ids=position_ids,
hidden_states=hidden_states)
logits = self.qa_outputs(hidden_states)
return logits
class BertPooler(Module):
def __init__(self, hidden_size, dtype):
super().__init__()
self.dense = Linear(hidden_size, hidden_size, dtype=dtype)
self.activation = ACT2FN['tanh']
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = select(hidden_states, 1, 0)
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class RobertaClassificationHead(Module):
"""Head for sentence-level classification tasks."""
def __init__(self, hidden_size, dtype, num_labels):
super().__init__()
self.dense = Linear(hidden_size, hidden_size, dtype=dtype)
self.out_proj = Linear(hidden_size, num_labels)
def forward(self, features, **kwargs):
x = select(features, 1, 0)
x = self.dense(x)
x = ACT2FN['tanh'](x)
x = self.out_proj(x)
return x
[docs]
class BertForSequenceClassification(Module):
def __init__(self,
num_layers,
num_heads,
hidden_size,
vocab_size,
hidden_act,
max_position_embeddings,
type_vocab_size,
pad_token_id=None,
is_roberta=False,
num_labels=2,
mapping=Mapping(),
dtype=None):
super().__init__()
self.is_roberta = is_roberta
self.bert = BertModel(num_layers=num_layers,
num_heads=num_heads,
hidden_size=hidden_size,
vocab_size=vocab_size,
hidden_act=hidden_act,
max_position_embeddings=max_position_embeddings,
type_vocab_size=type_vocab_size,
pad_token_id=pad_token_id,
is_roberta=is_roberta,
mapping=mapping,
dtype=dtype)
self.num_labels = num_labels
if not is_roberta:
self.pooler = BertPooler(hidden_size=hidden_size, dtype=dtype)
self.classifier = Linear(hidden_size, num_labels, dtype=dtype)
else:
self.classifier = RobertaClassificationHead(hidden_size=hidden_size,
num_labels=num_labels,
dtype=dtype)
[docs]
def forward(self,
input_ids=None,
input_lengths=None,
token_type_ids=None,
position_ids=None,
hidden_states=None):
hidden_states = self.bert.forward(input_ids=input_ids,
input_lengths=input_lengths,
token_type_ids=token_type_ids,
position_ids=position_ids,
hidden_states=hidden_states)
if not self.is_roberta:
pooled_output = self.pooler(hidden_states)
logits = self.classifier(pooled_output)
else:
logits = self.classifier(hidden_states)
return logits